Why I Majored in Cognitive Science

Alexandra werner ’22.

Coming into Pomona, I really had no idea what I wanted to major in. Initially, it was biology, then art history, before finally deciding that cognitive science was where many of my interests intersected. The so-called ‘click’ moment for me was during Intro to Cog Sci with Professor Laura Johnson the spring semester of my first year. I was absolutely captivated by the range of subjects that cognitive science encapsulated, from philosophy to computer science to linguistics. I even decided to go a step further and minor in linguistics due to a rather niche reason. I am originally from London, UK and after coming to Southern California, I became acutely aware of my accent among everyone else, which prompted me to take some linguistics classes, and then some more, until I realized that I had fulfilled all the credits for a minor in the process (paired also with the fact that I found the material extremely interesting). 

I tend to be quite reluctant in narrowing down to a specific focus, but cognitive science was honestly the best option for me to explore a wide range of classes which still followed an interdisciplinary study of human cognition and language. Three classes which have been very impactful were: Linguistic Anthropology with Professor Cecile Evers, Cognitive Film Studies at Pitzer with Professor Timothy Justus, and Bilingual Cognition with Professor Megan Zirnstein. These topics are rather different from each other but the opportunity to take major credits from different departments across the 5Cs is exactly what drew me to the LGCS Department as a whole.

During summer 2020, I analyzed how emotion, bilingualism, and decision making interact in speech-sign bilinguals through Pomona’s Remote Alternative Independent Summer Experience (RAISE) program with Professor Zirnstein. The majority of research on bilingual cognition is conducted on unimodal bilinguals who know two spoken languages. The inclusion of speech-sign bilinguals offers important insights into how signed and spoken languages interact across modalities at the lexical and conceptual levels. To strengthen and inform my thesis, I interviewed some eminent researchers specializing in the field of bilingual cognition. This was an invaluable and exciting experience for me, especially over the summer of the pandemic.

I am taking advantage of the new major curriculum, where you have the option of designing your own concentration around three courses which cohere to a theme relevant to cognitive science. My proposed concentration focuses on user-centered design and how people interact with products, through the lens of language and cognition. The flexibility that the cognitive science major offers students is hugely beneficial and allows you to not only learn about the important cognitive functions of the mind but also how to apply the broader framework of human behavior to a multitude of disciplines.

Emily McClaughry ’22

I love the interdisciplinary nature of the major. Like most people at Pomona, I chose a liberal arts college because I wanted to explore as many different areas of study as possible. Cognitive science allows me to do that as my major! I love how diverse my schedule is—I study linguistics, anthropology, sociology, neuroscience and psychology all in one week!             

The LGCS Department genuinely feels like a family. I've worked with many of the professors and students multiple times, and I recognize friendly faces at every event, workshop, and LGCS class I take. And new people are welcomed in with open arms. Prospective students interested in the many opportunities afforded by this department will also be supported throughout their time at Pomona.

I have been working on helping Professor Michael Diercks with his upcoming class, Morphosyntactic Diversity, by looking through grammars of underrepresented languages for students to study. Currently, I'm creating a presentation on linguistic discrimination for a presentation workshop run by Professor Galia Bar-Sever. I hope to write my thesis on something similar, in the sociolinguistics field in general.

The cog sci major is so customizable! I'm creating a concentration in linguistics, I know someone creating a concentration in anthropology, and the possibilities are endless. I'd love to see more students become part of the LGCS family!

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Cognitive Science

Senior essays.

This page lists all of the senior projects from previous cognitive science majors, organized by year. If a project title is blue, you may click on it to download a PDF of it. For current majors, you can find a guide to research and the senior thesis at this link .

CLASS OF 2022 

Class of 2020 , class of 2019 , class of 2018 , class of 2017, class of 2016, class of 2015, class of 2014, class of 2013, class of 2012, class of 2011, class of 2010, class of 2009.

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Plastic Skull & Brain

Cognitive Science

“Learn to question what you think…” Jesse Berlin, Cognitive Science Alumnus

Our program seeks an understanding of perception, language, reasoning and consciousness by drawing on work in computer science, linguistics, human biology, philosophy and psychology.

Outside all the boxes

Our students are encouraged to excel in the field’s sub-disciplines, but are also rewarded for thinking outside of these disciplinary boxes to synthesize their learning and enrich their understanding. As alumnus Jesse Berlin observed, “Cognitive Science is about the hard problems of tomorrow and today. It's where you learn to question not only what you think and how you think, but also what thinking is. It's utterly fascinating."

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Diverse Curriculum

In addition to dedicated Cognitive Science courses, our curriculum comprises a blend of mind-related courses in Computer Science, Human Biology, Linguistics, Philosophy, and Psychology.

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Flexible Programs

Cog Sci is offered as either an arts or science major, each with a choice of streams tailored to your intellectual and career interests. Arts streams include Perception and Attention, Language and Cognition or Thinking and Reasoning; Science streams include Computational Cognition or Cognition and the Brain.

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An Expanding Field

The Cognitive Science program is fast-growing -- enrolment is up 76 per cent since 2009! We strive to enhance scholarship and travel opportunities for students and to foster outreach programming such as our biennial undergraduate conference, “Interdisciplinary Symposium on the Mind.”

UC Welcomes New Cognitive Science Faculty Member Can Mekik

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Cognitive Science Professor Yang Xu Finds ‘Unified Foundation’ of Word Meaning in Child Language Development and Language Evolution

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Student Initiatives

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Student Association

All of our students are entitled to membership with CASA (Cognitive Science and Artificial Intelligence Students’ Association). CASA aims to bring together anyone in the U of T community interested in the study of the mind.

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Symposium (UTism)

The University of Toronto Interdisciplinary Symposium on the Mind (UTism) is a biennial conference offered by CASA to explore an array of topics in cognitive science and related disciplines.

Have a question about Cognitive Science at UC?

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Cognitive Science

Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures. Its organizational origins are in the mid-1970s when the Cognitive Science Society was formed and the journal Cognitive Science began. Since then, more than one hundred universities in North America, Europe, Asia, and Australia have established cognitive science programs, and many others have instituted courses in cognitive science.

3. Representation and Computation

4.1 formal logic, 4.3 concepts, 4.4 analogies, 4.6 connectionism, 4.7 theoretical neuroscience, 4.8 bayesian, 4.9 deep learning, 4.10 predictive processing and active inference, 5.1 philosophical applications, 5.2 critique of cognitive science, 5.3 philosophy of cognitive science, other internet resources, related entries.

Attempts to understand the mind and its operation go back at least to the Ancient Greeks, when philosophers such as Plato and Aristotle tried to explain the nature of human knowledge. The study of mind remained the province of philosophy until the nineteenth century, when experimental psychology developed. Wilhelm Wundt and his students initiated laboratory methods for studying mental operations more systematically. Within a few decades, however, experimental psychology became dominated by behaviorism , a view that virtually denied the existence of mind. According to behaviorists such as J. B. Watson, psychology should restrict itself to examining the relation between observable stimuli and observable behavioral responses. Talk of consciousness and mental representations was banished from respectable scientific discussion. Especially in North America, behaviorism dominated the psychological scene through the 1950s.

Around 1956, the intellectual landscape began to change dramatically. George Miller summarized numerous studies which showed that the capacity of human thinking is limited, with short-term memory, for example, limited to around seven items. He proposed that memory limitations can be overcome by recoding information into chunks, mental representations that require mental procedures for encoding and decoding the information. At this time, primitive computers had been around for only a few years, but pioneers such as John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon were founding the field of artificial intelligence . In addition, Noam Chomsky rejected behaviorist assumptions about language as a learned habit and proposed instead to explain language comprehension in terms of mental grammars consisting of rules. The six thinkers mentioned in this paragraph can be viewed as the founders of cognitive science.

Cognitive science has unifying theoretical ideas, but we have to appreciate the diversity of outlooks and methods that researchers in different fields bring to the study of mind and intelligence. Although cognitive psychologists today often engage in theorizing and computational modeling, their primary method is experimentation with human participants. People, often undergraduates satisfying course requirements, are brought into the laboratory so that different kinds of thinking can be studied under controlled conditions. For example, psychologists have experimentally examined the kinds of mistakes people make in deductive reasoning, the ways that people form and apply concepts, the speed of people thinking with mental images, and the performance of people solving problems using analogies. Our conclusions about how the mind works must be based on more than “common sense” and introspection, since these can give a misleading picture of mental operations, many of which are not consciously accessible. Increasingly, psychologists draw their experimental participants from Amazon’s Mechanical Turk and from culturally diverse sources. Psychological experiments that carefully approach mental operations from diverse directions are therefore crucial for cognitive science to be scientific. Experimentation is also a methodology employed by experimental philosophy.

Although theory without experiment is empty, experiment without theory is blind. To address the crucial questions about the nature of mind, the psychological experiments need to be interpretable within a theoretical framework that postulates mental representations and procedures. One of the best ways of developing theoretical frameworks is by forming and testing computational models intended to be analogous to mental operations. To complement psychological experiments on deductive reasoning, concept formation, mental imagery, and analogical problem solving, researchers have developed computational models that simulate aspects of human performance. Designing, building, and experimenting with computational models is the central method of artificial intelligence (AI), the branch of computer science concerned with intelligent systems. Ideally in cognitive science, computational models and psychological experimentation go hand in hand, but much important work in AI has examined the power of different approaches to knowledge representation in relative isolation from experimental psychology.

While some linguists do psychological experiments or develop computational models, most currently use different methods. For linguists in the Chomskian tradition, the main theoretical task is to identify grammatical principles that provide the basic structure of human languages. Identification takes place by noticing subtle differences between grammatical and ungrammatical utterances. In English, for example, the sentences “She hit the ball” and “What do you like?” are grammatical, but “She the hit ball” and “What does you like?” are not. A grammar of English will explain why the former are acceptable but not the latter. An alternative approach, cognitive linguistics, puts less emphasis on syntax and more on semantics and concepts.

Like cognitive psychologists, neuroscientists often perform controlled experiments, but their observations are very different, since neuroscientists are concerned directly with the nature of the brain. With nonhuman subjects, researchers can insert electrodes and record the firing of individual neurons. With humans for whom this technique would be too invasive, it is now common to use magnetic and positron scanning devices to observe what is happening in different parts of the brain while people are doing various mental tasks. For example, brain scans have identified the regions of the brain involved in mental imagery and word interpretation. Additional evidence about brain functioning is gathered by observing the performance of people whose brains have been damaged in identifiable ways. A stroke, for example, in a part of the brain dedicated to language can produce deficits such as the inability to utter sentences. Like cognitive psychology, neuroscience is often theoretical as well as experimental, and theory development is frequently aided by developing computational models of the behavior of groups of neurons.

Cognitive anthropology expands the examination of human thinking to consider how thought works in different cultural settings. The study of mind should obviously not be restricted to how English speakers think but should consider possible differences in modes of thinking across cultures. Cognitive science is becoming increasingly aware of the need to view the operations of mind in particular physical and social environments. For cultural anthropologists, the main method is ethnography, which requires living and interacting with members of a culture to a sufficient extent that their social and cognitive systems become apparent. Cognitive anthropologists have investigated, for example, the similarities and differences across cultures in words for colors.

Traditionally, philosophers do not perform systematic empirical observations or construct computational models, although there has been a rise in work in experimental philosophy. But philosophy remains important to cognitive science because it deals with fundamental issues that underlie the experimental and computational approach to mind. Abstract questions such as the nature of representation and computation need not be addressed in the everyday practice of psychology or artificial intelligence, but they inevitably arise when researchers think deeply about what they are doing. Philosophy also deals with general questions such as the relation of mind and body and with methodological questions such as the nature of explanations found in cognitive science. In addition, philosophy concerns itself with normative questions about how people should think as well as with descriptive ones about how they do. Besides the theoretical goal of understanding human thinking, cognitive science can have the practical goal of improving it, which requires normative reflection on what we want thinking to be. Philosophy of mind does not have a distinct method, but should share with the best theoretical work in other fields a concern with empirical results.

In its weakest form, cognitive science is just the sum of the fields mentioned: psychology, artificial intelligence, linguistics, neuroscience, anthropology, and philosophy. Interdisciplinary work becomes much more interesting when there is theoretical and experimental convergence on conclusions about the nature of mind. For example, psychology and artificial intelligence can be combined through computational models of how people behave in experiments. The best way to grasp the complexity of human thinking is to use multiple methods, especially psychological and neurological experiments and computational models. Theoretically, the most fertile approach has been to understand the mind in terms of representation and computation.

The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. While there is much disagreement about the nature of the representations and computations that constitute thinking, the central hypothesis is general enough to encompass the current range of thinking in cognitive science, including connectionist theories which model thinking using artificial neural networks.

Most work in cognitive science assumes that the mind has mental representations analogous to computer data structures, and computational procedures similar to computational algorithms. Cognitive theorists have proposed that the mind contains such mental representations as logical propositions, rules, concepts, images, and analogies, and that it uses mental procedures such as deduction, search, matching, rotating, and retrieval. The dominant mind-computer analogy in cognitive science has taken on a novel twist from the use of another analog, the brain.

Connectionists have proposed novel ideas about representation and computation that use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others. There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work. The computers that most of us work with today are serial processors, performing one instruction at a time, but the brain and some recently developed computers are parallel processors, capable of doing many operations at once.

A major trend in current cognitive science is the integration of neuroscience with many areas of psychology, including cognitive, social, developmental, and clinical. This integration is partly experimental, resulting from an explosion of new instruments for studying the brain, such as functional magnetic resonance imaging, transcranial magnetic stimulation, and optogenetics. The integration is also theoretical, because of advances in understanding how large populations of neurons can perform tasks usually explained with cognitive theories of rules and concepts.

4. Theoretical Approaches

Here is a schematic summary of current theories about the nature of the representations and computations that explain how the mind works.

Formal logic provides some powerful tools for looking at the nature of representation and computation. Propositional and predicate calculus serve to express many complex kinds of knowledge, and many inferences can be understood in terms of logical deduction with inferences rules such as modus ponens. The explanation schema for the logical approach is:

Explanation target: Why do people make the inferences they do? Explanatory pattern: People have mental representations similar to sentences in predicate logic. People have deductive and inductive procedures that operate on those sentences. The deductive and inductive procedures, applied to the sentences, produce the inferences.

It is not certain, however, that logic provides the core ideas about representation and computation needed for cognitive science, since more efficient and psychologically natural methods of computation may be needed to explain human thinking. (See the entry on logic and artificial intelligence .)

Much of human knowledge is naturally described in terms of rules of the form IF … THEN …, and many kinds of thinking such as planning can be modeled by rule-based systems. The explanation schema used is:

Explanation target: Why do people have a particular kind of intelligent behavior? Explanatory pattern: People have mental rules. People have procedures for using these rules to search a space of possible solutions, and procedures for generating new rules. Procedures for using and forming rules produce the behavior.

Computational models based on rules have provided detailed simulations of a wide range of psychological experiments, from cryptarithmetic problem solving to skill acquisition to language use. Rule-based systems have also been of practical importance in suggesting how to improve learning and how to develop intelligent machine systems.

Concepts, which partly correspond to the words in spoken and written language, are an important kind of mental representation. There are computational and psychological reasons for abandoning the classical view that concepts have strict definitions. Instead, concepts can be viewed as sets of typical features. Concept application is then a matter of getting an approximate match between concepts and the world. Schemas and scripts are more complex than concepts that correspond to words, but they are similar in that they consist of bundles of features that can be matched and applied to new situations. The explanatory schema used in concept-based systems is:

Explanatory target: Why do people have a particular kind of intelligent behavior? Explanation pattern: People have a set of concepts, organized via kind and part hierarchies and other associations. People have a set of procedures for concept application, including spreading activation, matching, and inheritance. The procedures applied to the concepts produce the behavior. Concepts can be translated into rules, but they bundle information differently than sets of rules, making possible different computational procedures.

Analogies play an important role in human thinking, in areas as diverse as problem solving, decision making, explanation, and linguistic communication. Computational models simulate how people retrieve and map source analogs in order to apply them to target situations. The explanation schema for analogies is:

Explanation target: Why do people have a particular kind of intelligent behavior? Explanatory pattern: People have verbal and visual representations of situations that can be used as cases or analogs. People have processes of retrieval, mapping, and adaptation that operate on those analogs. The analogical processes, applied to the representations of analogs, produce the behavior.

The constraints of similarity, structure, and purpose overcome the difficult problem of how previous experiences can be found and used to help with new problems. Not all thinking is analogical, and using inappropriate analogies can hinder thinking, but analogies can be effective in applications such as education and design.

Visual and other kinds of images play an important role in human thinking. Pictorial representations capture visual and spatial information in a much more usable form than lengthy verbal descriptions. Computational procedures well suited to visual representations include inspecting, finding, zooming, rotating, and transforming. Such operations can be very useful for generating plans and explanations in domains to which pictorial representations apply. The explanatory schema for visual representation is:

Explanation target: Why do people have a particular kind of intelligent behavior? Explanatory pattern: People have visual images of situations. People have processes such as scanning and rotation that operate on those images. The processes for constructing and manipulating images produce the intelligent behavior.

Imagery can aid learning, and some metaphorical aspects of language may have their roots in imagery. Psychological experiments suggest that visual procedures such as scanning and rotating employ imagery, and neurophysiological results confirm a close physical link between reasoning with mental imagery and perception. Imagery is not just visual, but can also operate with other sensory experiences such as hearing, touch, smell, taste, pain, balance, nausea, fullness, and emotion.

Connectionist networks consisting of simple nodes and links are very useful for understanding psychological processes that involve parallel constraint satisfaction. Such processes include aspects of vision, decision making, explanation selection, and meaning making in language comprehension. Connectionist models can simulate learning by methods that include Hebbian learning and backpropagation. The explanatory schema for the connectionist approach is:

Explanation target: Why do people have a particular kind of intelligent behavior? Explanatory pattern: People have representations that involve simple processing units linked to each other by excitatory and inhibitory connections. People have processes that spread activation between the units via their connections, as well as processes for modifying the connections. Applying spreading activation and learning to the units produces the behavior.

Simulations of various psychological experiments have shown the psychological relevance of the connectionist models, which are, however, only very rough approximations to actual neural networks. (For more information, see the entry on connectionism .)

Theoretical neuroscience is the attempt to develop mathematical and computational theories and models of the structures and processes of the brains of humans and other animals. It differs from connectionism in trying to be more biologically accurate by modeling the behavior of large numbers of realistic neurons organized into functionally significant brain areas. Computational models of the brain have become biologically richer, both with respect to employing more realistic neurons such as ones that spike and have chemical pathways, and with respect to simulating the interactions among different areas of the brain such as the hippocampus and the cortex. These models are not strictly an alternative to computational accounts in terms of logic, rules, concepts, analogies, images, and connections, but should mesh with them and show how mental functioning can be performed at the neural level. The explanatory schema for theoretical neuroscience is:

Explanation target: How does the brain carry out functions such as cognitive tasks? Explanatory pattern: The brain has neurons organized by synaptic connections into populations and brain areas. The neural populations have spiking patterns that are transformed via sensory inputs and the spiking patterns of other neural populations. Interactions of neural populations carry out functions including cognitive tasks.

From the perspective of theoretical neuroscience, mental representations are patterns of neural activity, and inference is transformation of such patterns. (See the entries on neuroscience and the neuroscience of consciousness .)

Bayesian models are prominent in cognitive science, with applications to such psychological phenomena as learning, vision, motor control, language, and social cognition. They have also had effective applications in robotics. The Bayesian approach assumes that cognition is approximately optimal in accord with probability theory, especially Bayes’ theorem, which says that the probability of a hypothesis given evidence is equal to the result of multiplying the prior probability of the hypothesis by the conditional probability of the evidence given the hypothesis, all divided by the probability of the evidence. The explanatory schema for Bayesian cognition is:

Explanation target: How does the mind carry out functions such as inference? Explanatory pattern: The mind has representations for statistical correlations and conditional probabilities. The mind has the capacity for probabilistic computations such as applications of Bayes’ theorem. Applying probabilistic computations to statistical representations accomplishes mental tasks such as inference.

Although Bayesian methods have had impressive applications to a wide range of phenomena, their psychological plausibility is debatable because of assumptions about optimality and computations based on probability theory.

Artificial intelligence has been a central part of cognitive since the 1950s, and the most dramatic recent advances in AI have come from the approach of deep learning, which has produced major breakthroughs in fields that include game playing, object recognition, and translation. Deep learning builds on ideas from connectionism and theoretical neuroscience, but uses neural networks with more layers and improved algorithms, benefitting from faster computers and large data bases of examples. Another important innovation is combining learning from examples with reinforcement learning, resulting by 2016 in the world’s leading Go player, AlphaGo. Ideas from deep learning are spreading back into neuroscience and also beginning to influence research in cognitive psychology. The explanatory schema for deep learning is:

Explanation target: How does the brain carry out functions such as cognitive tasks? Explanatory pattern: The brain has large numbers of neurons organized into 6–20 layers. The brain has powerful mechanisms for learning from examples and for learning actions that are reinforced by their successes. Applying learning mechanisms to layered neural networks makes them capable of human and sometimes even super-human performance.

Although deep learning has produced dramatic improvements in some AI systems, it is not clear how it can be applied to aspects of human thought that include causal reasoning, imagery, emotion, and analogy. For further discussion, see Section 11 (on deep learning) of the entry on connectionism .

Predictive processing is an approach to theoretical neuroscience that views the brain as constantly generating and updating models of the environment in order to predict the results of perceptions and actions. Active inference is a version of predictive processing that hypothesizes that the brain uses Bayesian calculations to minimize “free energy” consisting of discrepancies between expectations and actual observations. Organisms survive when brains reduce prediction errors by changing their models of the environment or by changing the environment through action.

The explanatory schema for active inference is:

Explanation target: How does the brain function to support perception and action? Explanatory pattern: The brain is a prediction engine that uses probabilistic models to anticipate perceptions and the results of actions. To reduce prediction error, the brain uses Bayesian updating to change its models and uses actions to change its environment, e.g. by moving. Effective inference, perception, and action result from these reductions in prediction errors.

Active inference is open to numerous challenges. Is brain functioning really Bayesian updating rather than connectionist constraint satisfaction or deep reinforcement learning? Can predictive processing subsume other brain functions that include pattern recognition, explanation, emotional evaluation, memory, and communication? Does active inference explain high-level cognitive operations such as causal reasoning, language, and creativity?

5. Philosophical Relevance

Some philosophy, in particular naturalistic philosophy of mind, is part of cognitive science. But the interdisciplinary field of cognitive science is relevant to philosophy in several ways. First, the psychological, computational, and other results of cognitive science investigations have important potential applications to traditional philosophical problems in epistemology, metaphysics, and ethics. Second, cognitive science can serve as an object of philosophical critique, particularly concerning the central assumption that thinking is representational and computational. Third and more constructively, cognitive science can be taken as an object of investigation in the philosophy of science, generating reflections on the methodology and presuppositions of the enterprise.

Much philosophical research today is naturalistic, treating philosophical investigations as continuous with empirical work in fields such as psychology. From a naturalistic perspective, philosophy of mind is closely allied with theoretical and experimental work in cognitive science. Metaphysical conclusions about the nature of mind are to be reached, not by a priori speculation, but by informed reflection on scientific developments in fields such as psychology, neuroscience, and computer science. Similarly, epistemology is not a stand-alone conceptual exercise, but depends on and benefits from scientific findings concerning mental structures and learning procedures. Ethics can benefit by using greater understanding of the psychology of moral thinking to bear on ethical questions such as the nature of deliberations concerning right and wrong. Here are some philosophical problems to which ongoing developments in cognitive science are highly relevant. Links are provided to other relevant articles in this Encyclopedia.

  • Innateness . To what extent is knowledge innate or acquired by experience? Is human behavior shaped primarily by nature or nurture?
  • Language of thought . Does the human brain operate with a language-like code or with a more general connectionist architecture? What is the relation between symbolic cognitive models using rules and concepts and sub-symbolic models using neural networks?
  • Mental imagery . Do human minds think with visual and other kinds of imagery, or only with language-like representations?
  • Folk psychology . Does a person’s everyday understanding of other people consist of having a theory of mind, or of merely being able to simulate them?
  • Meaning . How do mental representations acquire meaning or mental content ? To what extent does the meaning of a representation depend on its relation to other representations, its relation to the world, and its relation to a community of thinkers?
  • Mind-brain identity . Are mental states brain states? Or can they be multiply realized by other material states? What is the relation between psychology and neuroscience? Is materialism true?
  • Free will . Is human action free or merely caused by brain events?
  • Moral psychology . How do minds/brains make ethical judgments?
  • The meaning of life . How can minds construed naturalistically as brains find value and meaning?
  • Emotions . What are emotions, and what role do they play in thinking?
  • Consciousness . Can conscious experience be scientifically explained, for example by the neuroscience of consciousness ?
  • Mental disorder . What are mental disorders, and how are psychological and neural processes relevant to their explanation and treatment?
  • Perception and reality . How do minds/brains form and evaluate representations of the external world?
  • Perception and cognition . How does perception differ from other kinds of cognition with respect to representational format and justification?
  • Realism . Is cognitive science consistent with views that minds grasp the real world? Could minds be computer simulations? Is virtual reality a kind of reality?
  • Information . How does cognitive science illuminate the operations of information and misinformation in minds and societies?
  • Social science . How do explanations of the operations of minds interact with explanations of the operations of groups and societies?

Additional philosophical problems arise from examining the presuppositions of current approaches to cognitive science.

The claim that human minds work by representation and computation is an empirical conjecture and might be wrong. Although the computational-representational approach to cognitive science has been successful in explaining many aspects of human problem solving, learning, and language use, some philosophical critics have claimed that this approach is fundamentally mistaken. Critics of cognitive science have offered such challenges as:

  • The emotion challenge: Cognitive science neglects the important role of emotions in human thinking.
  • The consciousness challenge: Cognitive science ignores the importance of consciousness in human thinking.
  • The world challenge: Cognitive science disregards the significant role of physical environments in human thinking, which is embedded in and extended into the world.
  • The body challenge: Cognitive science neglects the contribution of embodiment to human thought and action.
  • The dynamical systems challenge: The mind is a dynamical system, not a computational system.
  • The social challenge: Human thought is inherently social in ways that cognitive science ignores.
  • The mathematics challenge: Mathematical results show that human thinking cannot be computational in the standard sense, so the brain must operate differently, perhaps as a quantum computer.
  • The interdisciplinarity challenge: Cognitive science has failed to go beyond multidisciplinary interactions by developing a core theory that unifies work in its many disciplines.

The first five challenges are increasingly addressed by advances that explain emotions, consciousness, action, and embodiment in terms of neural mechanisms. The social challenge is being met by the development of computational models of interacting agents. The mathematics challenge is based on misunderstanding of Gödel’s theorem and on exaggeration of the relevance of quantum theory to neural processes. Response to the interdisciplinary challenge must recognize that cognitive science still has many contending theoretical approaches, without the unification that theories of evolution and genetics provide for biology. Nevertheless, interactions among psychology, neuroscience, linguistics, philosophy, anthropology, and computer modeling have contributed to theoretical and empirical progress concerning many aspects of cognition. For example, computational philosophy uses programmed models to address questions in epistemology, ethics, and other areas of philosophy.

Cognitive science raises many interesting methodological questions that are worthy of investigation by philosophers of science. What is the nature of representation? What role do computational models play in the development of cognitive theories? What is the relation among apparently competing accounts of mind involving symbolic processing, neural networks, and dynamical systems? What is the relation among the various fields of cognitive science such as psychology, linguistics, and neuroscience? Are psychological phenomena subject to reductionist explanations via neuroscience? Are levels of explanation best characterized in terms of ontological levels (molecular, neural, psychological, social) or methodological ones (computational, algorithmic, physical)?

The increasing prominence of neural explanations in cognitive, social, developmental, and clinical psychology raises important philosophical questions about explanation and reduction . Anti-reductionism, according to which psychological explanations are completely independent of neurological ones, is becoming increasingly implausible, but it remains controversial to what extent psychology can be reduced to neuroscience and molecular biology. Crucial to answering questions about the nature of reduction are answers to questions about the nature of explanation. Explanations in psychology, neuroscience, and biology in general are plausibly viewed as descriptions of mechanisms , which are combinations of connected parts that interact to produce regular changes. In psychological explanations, the parts are mental representations that interact by computational procedures to produce new representations. In neuroscientific explanations, the parts are neural populations that interact by electrochemical processes to produce new neural activity that leads to actions. If progress in theoretical neuroscience continues, it should become possible to tie psychological to neurological explanations by showing how mental representations such as concepts are constituted by activities in neural populations, and how computational procedures such as spreading activation among concepts are carried out by neural processes.

The increasing integration of cognitive psychology with neuroscience provides evidence for the mind-brain identity theory according to which mental processes are neural, representational, and computational. Other philosophers dispute such identification on the grounds that minds are embodied in biological systems and extended into the world. However, moderate claims about embodiment are consistent with the identity theory because brain representations operate in several modalities (e.g. visual and motor) that enable minds to deal with the world. Another materialist alternative to mind-brain identity comes from recognizing that explanations of mind employ molecular and social mechanisms as well as neural and representational ones.

  • Anderson, J. R., 2007. How Can the Mind Occur in the Physical Universe? , Oxford: Oxford University Press.
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  • Bechtel, W., 2008. Mental Mechanisms: Philosophical Perspectives on Cognitive Neurosciences , New York: Routledge.
  • Bechtel, W., & Graham, G. (eds.), 1998. A Companion to Cognitive Science , Malden, MA: Blackwell.
  • Bechtel, W., Mandik, P., Mundale, J., & Stufflebeam, R. S. (eds.), 2001. Philosophy and the Neurosciences: A Reader , Malden, MA: Blackwell.
  • Bermúdez, J. L., 2022. Cognitive Science: An Introduction to the Science of the Mind , 4th edition, Cambridge: Cambridge University Press.
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What we don’t yet know about cognitive science in the classroom

  • Perspective Article
  • Published on: September 20, 2022

cognitive science college essay

  • Cognitive science

Dr Thomas Perry, Assistant Professor, University of Warwick

To get the benefits from cognitive science The study of the human mind, such as the processes of though... More we need to talk about what we don’t know.

Our current knowledge

I am really excited about the possibilities of cognitive science. In the EEF review of Cognitive Science in the Classroom (Perry et al., 2021), after locating and reviewing hundreds of classroom studies applying cognitive science, we were convinced that cognitive science can offer significant insights into learning, and that applications of cognitive science have real potential to improve classroom practice. We also concluded, however, that cognitive science was being recommended – and even mandated – before we have a real understanding of how the basic science relating to cognition and memory translates into everyday classroom teaching and learning, across phases and subject areas. 

I see far too little discussion of what we don’t know or have wrong about cognitive science and, crucially, its application in the classroom. This is important for realising its benefits. So, in this short article, I hope to highlight the significant gap in our current knowledge and suggest that a little more caution is due, even if taking this position makes me feel a bit like the person who turns up at the party asking for the music to be turned down.

Blind spots

Let us think for a moment about the gaps in the evidence base. This is helpful for knowing who and what to trust; this also helps locate the ‘blind spots’ of research that may need attention when using research. Many teachers are already aware that quite a lot of the evidence from cognitive science comes from the psychology laboratory or from research on undergraduates. There is, however, also a lot of evidence from the classroom to be found. What many people haven’t realised yet – perhaps because it isn’t much discussed – is that only a tiny fraction of the latter tests cognitive science in realistic classroom conditions.

As well as many classroom studies being small (less than 100 pupils), few pay attention to important educational variables. For example, most strategies have only been tested in a limited number of subjects, with the evidence concentrated in mathematics and science (and in KS2 to KS4) and many studies are short (a few weeks or less), making it harder to judge long-term retention. The most serious problem is that the role of teachers and teacher professional development has not been widely studied. With only a small number of exceptions, cognitive science studies have sought to scientifically control or minimise the influence of teachers, other students, and curriculum content. This is done through use of standardised booklets and computer programmes; scripted lessons; restricting classroom activity to independent work; using curriculum content that students haven’t encountered previously; stark differences between experimental conditions (rather than exploring blends or thresholds of strategies); and by having lessons delivered by researchers, or by teachers who are experts and/or (enthusiastic) volunteers. 

Many are untroubled by such gaps in the evidence. Cognition is cognition – the basic architecture of memory and the brain are the same whether you are five or 55 years old, learning about polynomials or poetry. This is a good argument when it comes to the applicability of fundamental principles, but a poor one when it comes to recommending specific classroom strategies. One reason for caution is that when cognitive science has been tested at scale, in realistic classroom conditions, it has been found to have very little effect. Four of the strongest studies in the EEF review tested programmes which incorporated multiple cognitive science strategies into teacher professional development and/or curriculum redesign (Cromley et al., 2016; Davenport et al., 2020; Schunn et al., 2018; Yang et al., 2020). These all were realistic, delivered at scale with thousands of pupils (n=2,595 to 9,611) and were high quality studies employing experimental designs (even if all were in maths and/or science for KS3 pupils). The results, however, were disappointing. The most positive study was Cromley et al. (2016), finding moderate, positive results in two out of the six curriculum units tested, but statistically insignificant results (and one negative) in the other four. Davenport et al. (2020) and Yang et al. (2020) found small, positive, but not statistically significant results. Schunn et al. (2018) found a range of results from small positive to small negative impacts, mostly not statistically significant.

What is the difference between the impressive effect sizes seen in the basic science and these tiny and mixed effects from large-scale studies in realistic conditions? In our view, these results do not call the basic cognitive science into question. Instead, they raise educational questions relating to the curriculum, professional development, how the strategies linked to other aspects of teaching (e.g., feedback, assessment, planning), and differences in pupil motivation, needs and prior learning. In other words, we are left wondering about all the variables that teachers would think about and an applied educational science would study, but are ‘controlled’ out of a science focused on the fundamentals of cognition and memory. 

Educational questions

Let’s look a little closer of the kind of educational questions that we don’t currently have clear answers to, taking retrieval practice as an example area. For retrieval practice the evidence we reviewed and the advice from teachers suggests several areas to think about, including:

  • whether and how to build feedback and error correction into retrieval activities
  • the level of difficulty and retrieval success to aim for
  • the format of the retrieval tests (see Yang et al., 2021 for helpful evidence about this)
  • whether retrieval practice works for learning with high complexity, subtlety or ‘element interactivity’ (i.e. beyond factual recall)
  • whether it is desirable to mimic the wording, conditions and format of the original learning, or to deliberately change them to seek transfer The processes of applying learning to new situations More
  • how to build retrieval practice into planning and timetabling
  • how to select retrieval items from the (crowded) curriculum
  • how to tailor for pupil ability and prior learning
  • timing and spacing of practice to ensure consolidation
  • how to integrate retrieval practice into classroom dialogue and other activities
  • and how to motivate and encourage independent retrieval practice (e.g., for homework, revision or directed improvement and reflection time).

None of these represent insurmountable problems for retrieval practice. What these questions do highlight is the amount of variation possible in how retrieval practice is implemented, and how much teacher expertise is required to make it work outside of the optimised and highly controlled conditions of (the current) research. A similar exercise – with a longer, subject- and context-specific list – is needed for every cognitive science-informed classroom strategy. Until more applied research is available, is up to teachers to both pose and answer such questions.

Working with uncertainty

Teachers are right to be enthusiastic about cognitive science. I would be astonished if, in a few decades’ time, our understanding of the fundamentals of learning and memory have greatly changed. However, the profession has to connect this to everything else it knows about great teaching and figure out how to get cognitive science into practice. At present, we have more knowledge ‘that’, and less knowledge ‘how’, and the psychological (and neuroscientific) knowledge is still early on in its journey to becoming educational knowledge.

Realising the great potential of cognitive science requires a recognition of the blind spots of cognitive science, and steering clear of centralised and prescriptive approaches to ensure that teachers (not researchers or policymakers) are in the driving seat. It’s not just about filling gaps; it is also a matter of being judicious with our current understanding. So, let’s continue to make sure we are talking about what we don’t know about cognitive science in the classroom, as well as what we do know.

  • Cromley JG, Weisberg SM, Dai T et al. (2016) Improving middle school science learning using diagrammatic reasoning. Science Education 100(6): 1184–1213.
  • Davenport JL, Kao YS, Matlen BJ et al (2020) Cognition research in practice: engineering and evaluating a middle school math curriculum. The Journal of Experimental Education 88(4): 516–535.
  • Perry T, Lea R, Jørgensen CR et al. (2021) Cognitive Science in the Classroom. London: Education Endowment Foundation (EEF).
  • Schunn CD, Newcombe NS, Alfieri L et al. (2018) Using principles of cognitive science to improve science learning in middle school: What works when and for whom? Applied Cognitive Psychology 32(2): 225–240.
  • Yang C, Luo L, Vadillo MA et al. (2021) Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin 147(4): 399–435.
  • Yang R, Porter AC, Massey CM et al. (2020) Curriculum‐based teacher professional development in middle school science: A comparison of training focused on cognitive science principles versus content knowledge. Journal of Research in Science Teaching 57(4) 536–566.

From this issue

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Issue 16: Cognitive science and beyond

September 2022

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Building a strong foundation: A new head of department’s perspective on professional development for a diverse team of PSHE Educators

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Fostering Metacognition to Support Student Learning and Performance

  • Julie Dangremond Stanton
  • Amanda J. Sebesta
  • John Dunlosky

*Address correspondence to: Julie Dangremond Stanton ( E-mail Address: [email protected] ).

Department of Cellular Biology, University of Georgia, Athens, GA 30602

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Department of Biology, Saint Louis University, St. Louis, MO 63103

Department of Psychological Sciences, Kent State University, Kent, OH 44240

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition ), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students’ metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.

INTRODUCTION

Supporting the development of metacognition is a powerful way to promote student success in college. Students with strong metacognitive skills are positioned to learn more and perform better than peers who are still developing their metacognition (e.g., Wang et al. , 1990 ). Students with well-developed metacognition can identify concepts they do not understand and select appropriate strategies for learning those concepts. They know how to implement strategies they have selected and carry out their overall study plans. They can evaluate their strategies and adjust their plans based on outcomes. Metacognition allows students to be more expert-like in their thinking and more effective and efficient in their learning. While collaborating in small groups, students can also stimulate metacognition in one another, leading to improved outcomes. Ever since metacognition was first described ( Flavell, 1979 ), enthusiasm for its potential impact on student learning has remained high. In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on “Promoting Student Metacognition” ( Tanner, 2012 ).

Despite this enthusiasm, instructors face several challenges when attempting to harness metacognition to improve their students’ learning and performance. First, metacognition is a term that has been used so broadly that its meaning may not be clear ( Veenman et al. , 2006 ). We define metacognition as awareness and control of thinking for learning ( Cross and Paris, 1988 ). Metacognition includes metacognitive knowledge , which is your awareness of your own thinking and approaches for learning. Metacognition also includes metacognitive regulation , which is how you control your thinking for learning ( Figure 1 ). Second, metacognition includes multiple processes and skills that are named and emphasized differently in the literature from various disciplines. Yet upon examination, the metacognitive processes and skills from different fields are closely related, and they often overlap (see Supplemental Figure 1). Third, metacognition consists of a person’s thoughts, which may be challenging for that person to describe. The tacit nature of metacognitive processes makes it difficult for instructors to observe metacognition in their students, and it also makes metacognition difficult for researchers to measure. As a result, classroom intervention studies of metacognition—those that are necessary for making the most confident recommendations for promoting student metacognition—have lagged behind foundational and laboratory research on metacognitive processes and skills.

FIGURE 1. Metacognition framework commonly used in biology education research (modified from Schraw and Moshman, 1995 ). This theoretical framework divides metacognition into two components: metacognitive knowledge and metacognitive regulation. Metacognitive knowledge includes what you know about your own thinking and what you know about strategies for learning. Declarative knowledge involves knowing about yourself as a learner, the demands of the task, and what learning strategies exist. Procedural knowledge involves knowing how to use learning strategies. Conditional knowledge involves knowing when and why to use particular learning strategies. Metacognitive regulation involves the actions you take in order to learn. Planning involves deciding what strategies to use for a future learning task and when you will use them. Monitoring involves assessing your understanding of concepts and the effectiveness of your strategies while learning. Evaluating involves appraising your prior plan and adjusting it for future learning.

How do undergraduate students develop metacognitive skills?

To what extent do active learning and generative work 1 promote metacognition?

To what extent do increases in metacognition correspond to increases in achievement in science courses?

FIGURE 2. (A) Landing page for the Student Metacognition guide. The landing page provides a map with sections an instructor can click on to learn more about how to support students’ metacognition. (B) Example paper summary showing instructor recommendations. At the end of each summary in our guide, we used italicized text to point out what instructors should know based on the paper’s results.

The organization of this essay reflects the organization of our evidence-based teaching guide. In the guide, we first define terms and provide important background from papers that highlight the underpinnings and benefits of metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/benefits-definitions-underpinnings ). We then explore metacognition research by summarizing both classic and recent papers in the field and providing links for readers who want to examine the original studies. We consider three main areas related to metacognition: 1) student strategies for learning, 2) monitoring and control of learning, and 3) social metacognition during group work.

SUPPORTING STUDENTS TO USE EFFECTIVE LEARNING STRATEGIES

What strategies do students use for learning.

First our teaching guide examines metacognition in the context of independent study ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/supporting-student
-learning-strategies ). When students transition to college, they have increased responsibility for directing their learning, which includes making important decisions about how and when to study. Students rely on their metacognition to make those decisions, and they also use metacognitive processes and skills while studying on their own. Empirical work has confirmed what instructors observe about their own students’ studying—many students rely on passive strategies for learning. Students focus on reviewing material as it is written or presented, as opposed to connecting concepts and synthesizing information to make meaning. Some students use approaches that engage their metacognition, but they often do so without a full understanding of the benefits of these approaches ( Karpicke et al. , 2009 ). Students also tend to study based on exam dates and deadlines, rather than planning out when to study ( Hartwig and Dunlosky, 2012 ). As a result, they tend to cram, which is also known in the literature as massing their study. Students continue to cram because this approach is often effective for boosting short-term performance, although it does not promote long-term retention of information.

Which Strategies Should Students Use for Learning?

Here, we make recommendations about what students should do to learn, as opposed to what they typically do. In our teaching guide, we highlight three of the most effective strategies for learning: 1) self-testing, 2) spacing, and 3) interleaving ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/supporting-student-learning-strategies/
#whatstudentsshould ). These strategies are not yet part of many students’ metacognitive knowledge, but they should know about them and be encouraged to use them while metacognitively regulating their learning. Students self-test when they use flash cards and answer practice questions in an attempt to recall information. Self-testing provides students with opportunities to monitor their understanding of material and identify gaps in their understanding. Self-testing also allows students to activate relevant knowledge and encode prompted information so it can be more easily accessed from their memory in the future ( Dunlosky et al. , 2013 ).

Students space their studying when they spread their learning of the same material over multiple sessions. This approach requires students to intentionally plan their learning instead of focusing only on what is “due” next. Spacing can be combined with retrieval practice , which involves recalling information from memory. For example, self-testing is a form of retrieval practice. Retrieval practice with spacing encourages students to actively recall the same content across several study sessions, which is essential for consolidating information from prior study periods ( Dunlosky et al. , 2013 ). Importantly, when students spread their learning over multiple sessions, they are less susceptible to superficial familiarity with concepts, which can mislead them into thinking they have learned concepts based on recognition alone ( Kornell and Bjork, 2008 ).

Students interleave when they alternate studying of information from one category with studying of information from another category. For example, when students learn categories of amino acid side groups, they should alternate studying nonpolar amino acids with polar amino acids. This allows students to discriminate across categories, which is often critical for correctly solving problems ( Rohrer et al. , 2020 ). Interleaving between categories also supports student learning because it usually results in spacing of study.

How are students enacting specific learning strategies, and do different students enact them in different ways?

To what extent do self-testing, spacing, and interleaving support achievement in the context of undergraduate science courses?

What can instructors do to increase students’ use of effective learning strategies?

What Factors Affect the Strategies Students Should Use to Learn?

Next, we examined the factors that affect what students should do to learn. Although we recommend three well-established strategies for learning, other appropriate strategies can vary based on the learning context. For example, the nature of the material, the type of assessment, the learning objectives, and the instructional methods can render some strategies more effective than others ( Scouller, 1998 ; Sebesta and Bray Speth, 2017 ). Strategies for learning can be characterized as deep if they involve extending and connecting ideas or applying knowledge and skills in new ways ( Baeten et al. , 2010 ). Strategies can be characterized as surface if they involve recalling and reproducing content. While surface strategies are often viewed negatively, there are times when these approaches can be effective for learning ( Hattie and Donoghue, 2016 ). For example, when students have not yet gained background knowledge in an area, they can use surface strategies to acquire the necessary background knowledge. They can then incorporate deep strategies to extend, connect, and apply this knowledge. Importantly, surface and deep strategies can be used simultaneously for effective learning. The use of surface and deep strategies ultimately depends on what students are expected to know and be able to do, and these expectations are set by instructors. Openly discussing these expectations with students can enable them to more readily select effective strategies for learning.

What Challenges Do Students Face in Using Their Metacognition to Enact Effective Strategies?

How can students address challenges they will face when using effective—but effortful—strategies for learning?

What approaches can instructors take to help students overcome these challenges?

ENCOURAGING STUDENTS TO MONITOR AND CONTROL THEIR LEARNING FOR EXAMS

Metacognition can be investigated in the context of any learning task, but in the sciences, metacognitive processes and skills are most often investigated in the context of high-stakes exams. Because exams are a form of assessment common to nearly every science course, in the next part of our teaching guide, we summarized some of the vast research focused on monitoring and control before, during, and after an exam ( https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition/encouraging-students-monitor-control-learning ). In the following section, we demonstrate the kinds of monitoring and control decisions learners make by using an example of introductory biology students studying for an exam on cell division. The students’ instructor has explained that the exam will focus on the stages of mitosis and cytokinesis, and the exam will include both multiple-choice and short-answer questions.

How Should Students Use Metacognition while Preparing for and Taking an Exam?

As students prepare for an exam, they can use metacognition to inform their learning. Students can consider how they will be tested, set goals for their learning, and make a plan to meet their goals. It is expected that students who set specific goals while planning for an exam will be more effective in their studying than students who do not make specific goals. For example, a student who sets a specific goal to identify areas of confusion each week by answering end-of-chapter questions each weekend is expected to do better than a student who sets a more general goal of staying up-to-date on the material. Although some studies include goal setting and planning as one of many metacognitive strategies introduced to students, the influence of task-specific goal setting on academic achievement has not been well studied on its own in the context of science courses.

As students study, it is critical that they monitor both their use of learning strategies and their understanding of concepts. Yet many students struggle to accurately monitor their own understanding ( de Carvalho Filho, 2009 ). In the example we are considering, students may believe they have already learned mitosis because they recognize the terms “prophase,” “metaphase,” “anaphase,” and “telophase” from high school biology. When students read about mitosis in the textbook, processes involving the mitotic spindle may seem familiar because of their exposure to these concepts in class. As a result, students may inaccurately predict that they will perform well on exam questions focused on the mitotic spindle, and their overconfidence may cause them to stop studying the mitotic spindle and related processes ( Thiede et al. , 2003 ). Students often rate their confidence in their learning based on their ability to recognize, rather than recall, concepts.

Instead of focusing on familiarity, students should rate their confidence based on how well they can retrieve relevant information to correctly answer questions. Opportunities for practicing retrieval, such as self-testing, can improve monitoring accuracy. Instructors can help students monitor their understanding more accurately by encouraging students to complete practice exams and giving students feedback on their answers, perhaps in the form of a key or a class discussion ( Rawson and Dunlosky, 2007 ). Returning to the example, if students find they can easily recall the information needed to correctly answer questions about cytokinesis, they may wisely decide to spend their study time on other concepts. In contrast, if students struggle to remember information needed to answer questions about the mitotic spindle, and they answer these questions incorrectly, then they can use this feedback to direct their efforts toward mastering the structure and function of the mitotic spindle.

While taking a high-stakes exam, students can again monitor their performance on a single question, a set of questions, or an entire exam. Their monitoring informs whether they change an answer, with students tending to change answers they judge as incorrect. Accordingly, the accuracy of their monitoring will influence whether their changes result in increased performance ( Koriat and Goldsmith, 1996 ). In some studies, changing answers on an exam has been shown to increase student performance, in contrast to the common belief that a student’s first answer is usually right ( Stylianou-Georgiou and Papanastasiou, 2017 ). Changing answers on an exam can be beneficial if students return to questions they had low confidence in answering and make a judgment on their answers based on the ability to retrieve the information from memory, rather than a sense of familiarity with the concepts. Two important open questions are:

What techniques can students use to improve the accuracy of their monitoring, while preparing for an exam and while taking an exam?

How often do students monitor their understanding when studying on their own?

How Should Students Use Metacognition after Taking an Exam?

How do students develop metacognitive regulation skills such as evaluation?

To what extent does the ability to evaluate affect student learning and performance?

When students evaluate the outcome of their studying and believe their preparation was lacking, to what degree do they adopt more effective strategies for the next exam?

PROMOTING SOCIAL METACOGNITION DURING GROUP WORK

Next, our teaching guide covers a relatively new area of inquiry in the field of metacognition called social metacognition , which is also known as socially shared metacognition ( https://lse.ascb.org/evidence-based-teaching-guides/student
-metacognition/promoting-social-metacognition
-group-work ). Science students are expected to learn not only on their own, but also in the context of small groups. Understanding social metacognition is important because it can support effective student learning during collaborations both inside and outside the classroom. While individual metacognition involves awareness and control of one’s own thinking, social metacognition involves awareness and control of others’ thinking. For example, social metacognition happens when students share ideas with peers, invite peers to evaluate their ideas, and evaluate ideas shared by peers ( Goos et al. , 2002 ). Students also use social metacognition when they assess, modify, and enact one another’s strategies for solving problems ( Van De Bogart et al. , 2017 ). While enacting problem-solving strategies, students can evaluate their peers’ hypotheses, predictions, explanations, and interpretations. Importantly, metacognition and social metacognition are expected to positively affect one another ( Chiu and Kuo, 2009 ).

How do social metacognition and individual metacognition affect one another?

How can science instructors help students to effectively use social metacognition during group work?

CONCLUSIONS

We encourage instructors to support students’ success by helping them develop their metacognition. Our teaching guide ends with an Instructor Checklist of actions instructors can take to include opportunities for metacognitive practice in their courses ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Student-Metacognition-Instructor-Checklist.pdf ). We also provide a list of the most promising approaches instructors can take, called Four Strategies to Implement in Any Course ( https://lse.ascb.org/wp-content/uploads/sites/10/2020/12/Four
-Strategies-to-Foster-Student-Metacognition.pdf ). We not only encourage instructors to consider using these strategies, but given that more evidence for their efficacy is needed from classroom investigations, we also encourage instructors to evaluate and report how well these strategies are improving their students’ achievement. By exploring and supporting students’ metacognitive development, we can help them learn more and perform better in our courses, which will enable them to develop into lifelong learners.

1 Generative work “involves students working individually or collaboratively to generate ideas and products that go beyond what has been presented to them” ( Andrews et al. , 2019 , p2). Generative work is often stimulated by active-learning approaches.

ACKNOWLEDGMENTS

We are grateful to Cynthia Brame, Kristy Wilson, and Adele Wolfson for their insightful feedback on this paper and the guide. This material is based upon work supported in part by the National Science Foundation under grant number 1942318 (to J.D.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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  • Published: 10 June 2019

What happened to cognitive science?

  • Rafael Núñez   ORCID: orcid.org/0000-0001-5063-2952 1 ,
  • Michael Allen 1 ,
  • Richard Gao 1 ,
  • Carson Miller Rigoli 1 ,
  • Josephine Relaford-Doyle 1 &
  • Arturs Semenuks 1  

Nature Human Behaviour volume  3 ,  pages 782–791 ( 2019 ) Cite this article

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More than a half-century ago, the ‘cognitive revolution’, with the influential tenet ‘cognition is computation’, launched the investigation of the mind through a multidisciplinary endeavour called cognitive science. Despite significant diversity of views regarding its definition and intended scope, this new science, explicitly named in the singular, was meant to have a cohesive subject matter, complementary methods and integrated theories. Multiple signs, however, suggest that over time the prospect of an integrated cohesive science has not materialized. Here we investigate the status of the field in a data-informed manner, focusing on four indicators, two bibliometric and two socio-institutional. These indicators consistently show that the devised multi-disciplinary program failed to transition to a mature inter-disciplinary coherent field. Bibliometrically, the field has been largely subsumed by (cognitive) psychology, and educationally, it exhibits a striking lack of curricular consensus, raising questions about the future of the cognitive science enterprise.

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cognitive science college essay

Data availability

The data sets generated in this study can be found on GitHub ( https://github.com/rdgao/WH2CogSci ) and FigShare ( https://figshare.com/articles/scrapingcognition/7973372 ). They are openly available and free for use, with proper attribution.

Code availability

The code used for analysis and draft figure generation can be found on GitHub ( https://github.com/rdgao/WH2CogSci ) and FigShare ( https://figshare.com/articles/scrapingcognition/7973372 ). It is openly available and free for use, with proper attribution.

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Acknowledgements

We are grateful to K. Cooperrider, E. Beringer, J. Núñez and P. Gagneux for valuable comments on earlier drafts of this article, and to P. Van den Besselaar and M. Cole for constructive input on methods and theory, respectively. We thank C. Gere and R. Westman for insights into the history and practice of science as well as D. Christiano, K. Lacroix, and J. Dominguez for helping with data collection.

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Department of Cognitive Science, University of California, San Diego, La Jolla, California, USA

Rafael Núñez, Michael Allen, Richard Gao, Carson Miller Rigoli, Josephine Relaford-Doyle & Arturs Semenuks

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In the author list, M.A., R.G., C.M.R., J.R.-D. and A.S. are listed in alphabetical order. R.N. conceived and designed the overall structure of the study, organized the intellectual content of the article, was involved with data analysis and data visualization design, and wrote most of the paper with systematic input from all co-authors. C.M.R. did the analysis of the faculty Ph.D. backgrounds, wrote the draft reporting on these data and produced most of the figures, following designs conceived by the entire team. J.R.-D. and M.A. conducted the curriculum analysis. J.R.-D. wrote the draft reporting on these data and managed the work of a research assistant. M.A. wrote the supplementary information of this analysis. R.G. and A.S. conducted the authors’ affiliation analysis, performed web-page scraping and managed the computational work of two research assistants. R.G. wrote the draft reporting on these data and prepared the corresponding text for the supplementary information . R.G. performed the factor analysis and hierarchical clustering on the journal–journal citation data, produced the resulting dendrograms and wrote the drafts of the results and supplementary information . A.S. provided input regarding scientometric methods and compiled all the supplementary information .

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Correspondence to Rafael Núñez .

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Núñez, R., Allen, M., Gao, R. et al. What happened to cognitive science?. Nat Hum Behav 3 , 782–791 (2019). https://doi.org/10.1038/s41562-019-0626-2

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Issue Date : August 2019

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https://cogsci.barnard.edu/

Adviser for Columbia College and School of General Studies students:

Professor Mariusz S. Kozak:  [email protected]

Department website: https://cogsci.barnard.edu/

326 Milbank Hall 212-854-4689

Barnard Director: Professor John Morrison, [email protected] Columbia Director: Professor Mariusz S. Kozak, [email protected]

Department Assistant: Maia Bernstein, [email protected]

Cognitive Science is the cross-disciplinary study of how the mind works, with a focus on perception, reasoning, memory, attention, language, decision-making, motor control, and problem solving.  Cognitive scientists often compare minds to computers. In particular, they describe mental processes as computational operations on internal representations.  For instance, perception is seen as a representation of the external world that results from sensory stimulation; learning is analyzed as the addition of new representations through interactions with the environment; reasoning is treated as the addition of new representations through operations on existing representations.

Cognitive Science is an interdisciplinary field: it draws on tools and ideas from psychology, neuroscience, linguistics, economics, computer science, and philosophy, with affiliated faculty in each of these disciplines. Psychologists study the computational operations that we use to solve specific tasks; neuroscientists study the implementation of those operations in the brain; linguists study the representations involved in communication; economists study the representations involved in decisions involving uncertainty and reward; computer scientists consider how the processes involved in human cognition fit into a more general theory of computations and a larger space of tasks; and philosophers ask fundamental questions about the nature of representation and computation.

Learning Objectives

Cognitive Science majors will gain fluency in computational methods; a capacity for rigorous and careful thought; a broad understanding of the affiliated disciplines; and a deep understanding of cognition.  

Barnard Director: Professor John Morrison (Philosophy, Barnard) Columbia Director: Professor Mariusz S. Kozak (Music, Columbia)

Steering Committee: Dima Amso (Psychology, Columbia) Mariusz S. Kozak (Music, Columbia) John McWhorter (Linguistics, Columbia) John Morrison (Philosophy, Barnard) Christopher A.B. Peacocke (Philosophy, Columbia) Ann Senghas (Psychology, Barnard) Lisa Son (Psychology, Barnard) Michael Woodford (Economics, Columbia) Rebecca Wright (Computer Science, Barnard)

Affiliated Faculty: Mariam Aly (Psychology, Columbia) Christopher Baldassano (Psychology, Columbia) Peter Balsam (Neuroscience & Behavior; Psychology, Barnard) Akeel Bilgrami (Philosophy, Columbia) BJ Casey (Neuroscience & Behavior, Barnard) Jessica Collins (Philosophy, Columbia) Lila Davachi (Psychology, Columbia) Mark Dean (Economics, Columbia) Aaron A. Fox (Music, Columbia) David A. Freedberg (Art History & Archaeology, Columbia) Melissa Fusco (Philosophy, Columbia) Michelle Greene (Psychology, Barnard) Larisa Heiphetz (Psychology, Columbia) Niko Kriegeskorte (Psychology, Columbia) Karen Lewis (Philosophy, Barnard) Caroline Marvin (Psychology, Columbia) Koleen McCrink (Psychology, Barnard) Janet Metcalfe (Psychology, Columbia) Kevin Ochsner (Psychology, Columbia) Christos Papadimitriou (Computer Science, Columbia) Robert Remez (Psychology, Barnard) Daphna Shohamy (Psychology, Columbia) Rae Silver (Psychology, Columbia) Alfredo Spagna (Psychology, Columbia) Herbert Terrace (Psychology, Columbia) Nim Tottenham (Psychology, Columbia) Carl Vondrick (Computer Science, Columbia) Alex White (Neuroscience and Behavior, Barnard) Keren Yarhi-Milo (Political Science, Columbia) 

Cognitive science is the cross-disciplinary study of how the mind works, with a focus on perception, reasoning, memory, attention, language, decision-making, motor control, and problem solving. It draws on tools and ideas from psychology, neuroscience, linguistics, economics, computer science, and philosophy. The major requirements are designed to provide breadth in the affiliated disciplines and depth in the student’s chosen area of specialization. 

A major in Cognitive Science consists of seven required courses and four electives in a chosen area of specialization culminating in the senior capstone. The minimum number of courses is 13 and the minimum number of points is 39.

Required courses (7 classes)

  • COGS UN1001 Introduction to Cognitive Science Introduction to Cognitive Science
  • One course in each of four areas: psychology, neuroscience, philosophy, and linguistics.
  • Two courses in a fifth area: mathematical and computational methods. These two courses must be selected in consultation with the program director to make sure they aren’t redundant. 
  • Please see below for the lists of approved courses in each area.

Area of Specialization and Electives (four classes) Students must choose an area of specialization when they declare the major and choose four electives to build expertise in that area.

  • Possible areas of specialization include: aesthetics, cognitive development, cognitive linguistics, cognitive neuroscience, cognitive psychology, computer vision, consciousness, decision science, human-computer interaction, intelligence, learning, memory, natural language processing, neuroeconomics, perception, and social cognition.
  • The choice of specialization is flexible; there is not a predefined list. This is an opportunity for students to be creative; a student who has ideas about a new specialization that they would like to pursue may do so with the approval of the program director.
  • Although there is no predefined list, each student’s area of specialization and choice of electives must be approved by the program director, and there must be at least one faculty member affiliated with the program who has expertise in the student’s chosen area. 
  • The program director will consult with a faculty member who has expertise in the student’s area of specialization to ensure that the student’s electives will provide sufficient preparation for the senior project.
  • Please see below for a list of courses that students might want to consider as possible electives (depending upon their specialization), but please note that this list is not definitive. Any Barnard or Columbia (or approved transfer) course that builds expertise in the student's area of specialization may be counted as an elective with the approval of the program director. 

Senior Capstone

Students may fulfill the Senior Capstone requirement in two ways: with a year-long senior project, or by taking two additional advanced courses. 

  • Students who do senior projects must register for both COGS UN3903 Senior Project (3 points) and COGS UN3901 Senior Project Seminar (1 point) in the fall and COGS UN3904 Senior Project (3 points) and COGS UN3902 Senior Project Seminar (1 point) in the spring (8 points total).  
  • The Senior Project Seminar is an opportunity for students to present their projects to each other.
  • While a year-long project is recommended, students may also satisfy the senior capstone requirement by taking two advanced courses, at least one of which must include a significant paper or project. The courses must be chosen in consultation with the program director and must be related to the student’s area of specialization. Both courses should be at the 3000-level or above.

Courses approved to count in each area:

Please note that PSYC UN2430 Cognitive Neuroscience may be used to fulfill either the Neuroscience requirement or the Psychology requirement, but not both. 

Neuroscience

Please note that only the "Perception" section of PHIL UN3912 counts.  

Mathematical and Computational Methods Logic and Decision Theory:

Statistics:

Computer Science:

Possible electives:

Please note that the list of possible electives below is just to give students ideas. Whether or not a student can count a particular course as an elective depends upon the student’s area of specialization. Any course that builds expertise in the student’s area of specialization may be counted as an elective with the program director’s approval, regardless of whether it is listed below; a course that does not build expertise in the student’s area of specialization may not be counted as an elective even if it is listed below. 

Required Courses

Required for all Cognitive Science majors:

COGS UN1001 Introduction to Cognitive Science. 3 points .

Required for Cognitive Science majors doing senior projects:

COGS UN3901 Senior Project Seminar. 1.00 point .

Discussion of senior research projects during the fall and spring terms that culminate in written and oral senior theses. Each project must be supervised by a cognitive scientist working at Barnard or Columbia

COGS UN3902 Senior Project Seminar. 1.00 point .

COGS UN3903 Senior Project. 3.00 points .

Senior Project in Cognitive Science

COGS UN3904 Senior Project. 3.00 points .

Psychology:

PSYC BC2107 PSYCHOLOGY OF LEARNING - LEC. 3.00 points .

Prerequisites: BC1001 Introduction of Psychology or permission of the instructor. Enrollment limited to 72 students. Prerequisites: PSYC BC1001 Introduction to Psychology or COGS UN1001 Introduction to Cognitive Science or permission of the instructor. Lecture course covering the basic methods, results, and theory in the study of how experience affects behavior. The roles of early exposure, habitation, sensitization, conditioning, imitation, and memory in the acquisition and performance of behavior are studied. The following Columbia University course is considered overlapping and a student cannot receive credit for both the BC course and the equivalent CU course: PSYC UN1440 Experimental: Learning and Motivation

PSYC BC2110 PERCEPTION-LECTURE. 3.00 points .

Prerequisites: PSYC BC1001 Introduction to Psychology or COGS UN1001 Introduction to Cognitive Science or permission of the instructor. Lecture course covering an introduction to problems, methods, and research in perception. Discussion of psychological studies of seeing, hearing, touching, tasting, and smelling. Note that this lecture can be taken without its affiliated lab, PSYC BC2109 , however, if a student completes this lecture, she cannot enroll in the lab in a later semester. The following Columbia University course is considered overlapping and a student cannot receive credit for both the BC course and the equivalent CU course: PSYC UN1480 Perception and Attention; and PSYC UN2230 Perception and Sensory Processes

PSYC BC2115 COGNITIVE PSYCHOLOGY - LEC. 3.00 points .

Prerequisites: BC1001 or permission of the instructor. Prerequisites: PSYC BC1001 Introduction to Psychology or COGS UN1001 Introduction to Cognitive Science or permission of the instructor. Lecture covering selected topics illustrating the methods, findings, and theories of contemporary cognitive psychology. Topics include attention, memory, categorization, perception, and decision making. Special topics include neuropsychology and cognitive neuroscience. Note that this lecture can be taken without its affiliated lab, PSYC BC2114 , however, if a student completes this lecture, she cannot enroll in the lab in a later semester. The following Columbia University courses are considered overlapping and a student cannot receive credit for both the BC course and the equivalent CU course: PSYC UN2220 Cognition: Memory and Stress; and PSYC UN2210 Cognition: Basic Processes

PSYC UN2210 COGNITION: BASIC PROCESSES. 3.00 points .

PSYC UN2220 COGNITION: MEMORY AND STRESS. 3.00 points .

CC/GS: Partial Fulfillment of Science Requirement Attendance at the first class is mandatory.

Prerequisites: PSYC UN1001 or PSYC UN1010 or the instructor's permission. Prerequisites: PSYC UN1001 or PSYC UN1010 or the instructors permission. Memory, attention, and stress in human cognition

PSYC UN2270 Perception and Cognition in Social Life. 3.00 points .

This course focuses on perception and cognition in social life. We start by addressing the core social motivations we experience in everyday life (e.g., our desire to feel like we belong to a group). Next, we examine how these motivations shape our basic sensory experiences—for example why we can’t help but anthropomorphize inanimate objects or enjoy holding hands with our partner. We then examine the mental strategies we use to meet our social needs, such as how we figure out other people’s thoughts and feelings, as well as our own. Finally, we wrap up by examining how these motivations, perceptions, and cognitions play out not just within one mind – but also between minds in everyday social interaction. This course will not only teach you the fundamental science behind the social mind. It will also let you see your own social life through a whole new lens

PSYC UN2430 COGNITIVE NEUROSCIENCE. 3.00 points .

CC/GS: Partial Fulfillment of Science Requirement

Prerequisites: PSYC UN1001 or equivalent introductory course in Psychology Prerequisites: PSYC UN1001 or equivalent introductory course in Psychology This course provides an in-depth survey of data and models of a wide variety of human cognitive functions. Drawing on behavioral, neuropsychological, and neuroimaging research, the course explores the neural mechanisms underlying complex cognitive processes, such as perception, memory, and decision making. Importantly, the course examines the logic and assumptions that permit us to interpret brain activity in psychological terms

PSYC BC3394 METACOGNITION. 4.00 points .

Prerequisites: BC1001, and one psychology laboratory course; final enrollment determined on the first day of class Metacognition is one of the latest psychological buzzwords, but what exactly is metacognition? Metacognition enables us to be successful learners, problem solvers, and decision makers, and as often been used synonymously with words such as language, awareness, and consciousness. In this seminar, we will examine various components of metacognition, including its role in learning and memory, and its existence in various non-human populations. In addition, we will explore the fragility of metacognition, including illusions of confidence and harmful control strategies that people use. Readings will include classic and important recent papers in the field, looking at metacognition as a higher-level cognitive process, and as knowledge individuals use to guide behavior

NSBV BC1001 INTRODUCTION TO NEUROSCIENCE. 3.00 points .

This course is required for all the other courses offered in Neuroscience and Behavior. The course introduces students to the anatomy and physiology of the nervous system. The topics include the biological structure of the nervous system and its different cell types, the basis of the action potential, principles of neurotransmission, neuronal basis of behavior, sleep/wake cycles, and basic aspects of clinical neuroscience

NSBV BC2008 ADAPTIVE OR ARRESTED DEVELOPMENT OF THE ADOLESCENT BRAIN. 3.00 points .

The teen brain has received a lot of media coverage with advances in brain imaging techniques that provide a voyeuristic opportunity for us to look under the hood of the behaving adolescent brain. This course will cover empirical and theoretical accounts of adolescent-specific changes in brain and behavior that relate to the development of self control. These accounts of adolescent brain and behavior will then be discussed in the context of relevant legal, social and health policy issues. Lectures and discussion will address: Under what circumstances self control appears to be diminished in adolescents. How do dynamic changes in neural circuitry help to explain changes in self control across development? When does the capacity for self control fully mature? Are these changes observed in other species? How might these changes be evolutionarily adaptive and when are they maladaptive? How might understanding adolescent brain and behavioral development inform interventions and treatments for maladaptive behavior or inform policy for changing the environment to protect youth?

PSYC UN2435 Social Neuroscience. 3.00 points .

This course will provide a broad overview of the field of social neuroscience. We will consider how social processes are implemented at the neural level, but also how neural mechanisms help give rise to social phenomena and cultural experiences. Many believe that the large expansion of the human brain evolved due to the complex demands of dealing with social others—competing or cooperating with them, deceiving or empathizing with them, understanding or misjudging them. What kind of “social brain” has this evolutionary past left us with? In this course, we will review core principles, theories, and methods guiding social neuroscience, as well as research examining the brain basis of processes such as theory of mind, emotion, stereotyping, social group identity, empathy, judging faces and bodies, morality, decision-making, the impact of culture and development, among others. Overall, this course will introduce students to the field of social neuroscience and its multi-level approach to understanding the brain in its social context

PSYC UN2450 BEHAVIORAL NEUROSCIENCE. 3.00 points .

Prerequisites: PSYC UN1001 or PSYC UN1010 or the instructor's permission. Prerequisites: PSYC UN1001 or PSYC UN1010 or the instructors permission. Examines the principles governing neuronal activity, the role of neurotransmitter systems in memory and motivational processes, the presumed brain dysfunctions that give rise to schizophrenia and depression, and philosophical issues regarding the relationship between brain activity and subjective experience

PSYC UN2481 Developmental Cognitive Neuroscience. 3.00 points .

The course will be an introduction to the science of structural and functional brain development beginning in the prenatal period. We will cover major domains in both cognitive and social development. This is a flipped course, where students will watch lectures online (three 55 minute lectures each week) and participate in classroom discussions and exercises (1 hour 50 minutes twice a week) with the Professor and each other when in person

NSBV BC3381 Visual Neuroscience: From the Eyeball to the Mind's Eye. 4.00 points .

By absorbing electromagnetic radiation through their eyes, people are able to catch frisbees, recognize faces, and judge the beauty of art. For most of us, seeing feels effortless. That feeling is misleading. Seeing requires not only precise optics to focus images on the retina, but also the concerted action of millions of nerve cells in the brain. This intricate circuitry infers the likely causes of incoming patterns of light and transforms that information into feelings, thoughts, and actions. In this course we will study how light evokes electrical activity in a hierarchy of specialized neural networks that accomplish many unique aspects of seeing. Students will have the opportunity to focus their study on particular aspects, such as color, motion, object recognition, learning, attention, awareness, and how sight can be lost and recovered. Throughout the course we will discuss principles of neural information coding (e.g., receptive field tuning, adaptation, normalization, etc.) that are relevant to other areas of neuroscience, as well as medicine, engineering, art and design

Philosophy:

PHIL UN2655 COGNITIVE SCIENCE AND PHILOSOPHY. 3.00 points .

This course will survey a number of topics at the intersection of cognitive science and philosophy. Potential topics include free will, consciousness, embodied cognition, artificial intelligence, neural networks, and the language of thought

PHIL UN3252 Philosophy of Language and Mind. 3 points .

This course will provide an introduction to meaning, reference, understanding, and content in language, thought, and perception.  A central concern will be the question of the relation of meaning to truth-conditions, and what is involved in language and thought successfully latching on to reality.  If you have not already taken an elementary course in first order logic, you will need to catch up in that area to understand some crucial parts of the course.  All the same, the primary concerns of the course will be philosophical, rather than technical.

PHIL UN3655 TOPICS IN COGNITIVE SCIENCE AND PHILOSOPHY. 3.00 points .

This course will focus on one topic at the intersection of cognitive science and philosophy. Potential topics include free will, consciousness, modularity, mental representation, probabilistic inference, the language of thought, and the computational theory of mind

PHIL UN3912 SEMINAR. 3.00 points .

Required of senior majors, but also open to junior majors, and junior and senior concentrators who have taken at least four philosophy courses. This exploration will typically involve writing a substantial research paper. Capped at 20 students with preference to philosophy majors

(Please note that only the "Perception" section of the PHIL UN3912 Seminar counts towards your Cognitive Science major; that section is not offered every year.)

LING UN3101 INTRODUCTION TO LINGUISTICS. 3.00 points .

An introduction to the study of language from a scientific perspective. The course is divided into three units: language as a system (sounds, morphology, syntax, and semantics), language in context (in space, time, and community), and language of the individual (psycholinguistics, errors, aphasia, neurology of language, and acquisition). Workload: lecture, weekly homework, and final examination

Mathematical and Computational Methods

Logic and Decision Theory

ECON GU4850 COGNITIVE MECH & ECON BEHAVIOR. 4.00 points .

Prerequisites: ECON UN3211 and ECON UN3213 and STAT UN1201 Prerequisites: ECON UN3211 and ECON UN3213 and STAT UN1201 Standard economic theory seeks to explain human behavior (especially in economic settings, such as markets) in terms of rational choice, which means that the choices that are made can be predicted on the basis of what would best serve some coherent objective, under an objectively correct understanding of the predictable consequences of alternative actions. Observed behavior often seems difficult to reconcile with a strong form of this theory, even if incentives clearly have some influence on behavior; and the course will discuss empirical evidence (both from laboratory experiments and observations in the field) for some well-established anomalies. But beyond simply cataloguing anomalies for the standard theory, the course will consider the extent to which departures from a strong version of rational choice theory can be understood as reflecting cognitive processes that are also evident in other domains such as sensory perception; examples from visual perception will receive particular attention. And in addition to describing what is known about how the underlying mechanisms work (something that is understood in more detail in sensory contexts than in the case of value-based decision making), the course will consider the extent to which such mechanisms --- while suboptimal from a normative standpoint that treats perfect knowledge of one's situation as costless and automatic --- might actually represent efficient uses of the limited information and bounded information-processing resources available to actual people (or other organisms). Thus the course will consider both ways in which the realism of economic analysis may be improved by taking into account cognitive processes, and ways in which understanding of cognitive processes might be advanced by considering the economic problem of efficient use of limited (cognitive) resources

PHIL UN1401 INTRODUCTION TO LOGIC. 3.00 points .

Explicit criteria for recognizing valid and fallacious arguments, together with various methods for schematizing discourse for the purpose of logical analysis. Illustrative material taken from science and everyday life

PHIL UN3411 SYMBOLIC LOGIC. 4.00 points .

Corequisites: PHILV3413 Required Discussion Section (0 points). Advanced introduction to classical sentential and predicate logic. No previous acquaintance with logic is required; nonetheless a willingness to master technicalities and to work at a certain level of abstraction is desirable

PHIL GU4561 PROBABILITY & DECISION THEORY. 3.00 points .

Examines interpretations and applications of the calculus of probability including applications as a measure of degree of belief, degree of confirmation, relative frequency, a theoretical property of systems, and other notions of objective probability or chance. Attention to epistimological questions such as Hume's problem of induction, Goodman's problem of projectibility, and the paradox of confirmation

PSYC UN2235 THINKING AND DECISION MAKING. 3.00 points .

Prerequisites: an introductory course in psychology. Prerequisites: an introductory course in psychology. Models of judgment and decision making in both certain and uncertain or risky situations, illustrating the interplay of top-down (theory-driven) and bottom-up (data-driven) processes in creating knowledge. Focuses on how individuals do and should make decisions, with some extensions to group decision making and social dilemmas

ECON BC1007 MATH METHODS FOR ECONOMICS. 4.00 points .

Covers basic mathematical methods required for intermediate theory courses and upper level electives in economics, with a strong emphasis on applications. Topics include simultaneous equations, functions, partial differentiation, optimization of functions of more than one variable, constrained optimization, and financial mathematics. This course satisfies the Calculus requirement for the Barnard Economics major. NOTE: students who have previously taken Intermediate Micro Theory ( ECON BC3035 or the equivalent) are *not* allowed to take Math Methods for Economics

ECON BC2411 STATISTICS FOR ECONOMICS. 4.00 points .

Elementary computational methods in statistics. Basic techniques in regression analysis of econometric models. One-hour weekly recitation sessions to complement lectures

PSYC BC1101 STATISTICS LECTURE AND RECITATION. 4.00 points .

Prerequisites: BC1001 and instructor permission. Enrollment limited to 20 students per recitation section. Prerequisite (or co-requisite): PSYC BC1001 . Lecture course and associated recitation section introducing students to statistics and its applications to psychological research. The course covers basic theory, conceptual underpinnings, and common statistics. The following Columbia University courses are considered overlapping and a student cannot receive credit for both the BC course and the equivalent CU course: STAT UN1001 Introduction to Statistical Reasoning; STAT UN1101 Introduction to Statistics; STAT UN1201 Introduction to Statistics

PSYC UN1610 STATISTCS-BEHAVIORL SCIENTISTS. 4.00 points .

Lecture and lab. Priority given to psychology majors. Fee $70.

Prerequisites: PSYC UN1001 or PSYC UN1010 Recommended preparation: one course in behavioral science and knowledge of high school algebra. Corequisites: PSYC UN1611 Prerequisites: PSYC UN1001 or PSYC UN1010 Recommended preparation: one course in behavioral science and knowledge of high school algebra. Corequisites: PSYC UN1611 Introduction to statistics that concentrates on problems from the behavioral sciences

STAT UN1001 INTRO TO STATISTICAL REASONING. 3.00 points .

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance

STAT UN1101 INTRODUCTION TO STATISTICS. 3.00 points .

Prerequisites: intermediate high school algebra. Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement

STAT UN1201 CALC-BASED INTRO TO STATISTICS. 3.00 points .

Prerequisites: one semester of calculus. Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412

  Computer Science  

COMS BC1016 Introduction to Computational Thinking and Data Science. 3.00 points .

This course and its co-requisite lab course will introduce students to the methods and tools used in data science to obtain insights from data. Students will learn how to analyze data arising from real-world phenomena while mastering critical concepts and skills in computer programming and statistical inference. The course will involve hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. The course is ideal for students looking to increase their digital literacy and expand their use and understanding of computation and data analysis across disciplines. No prior programming or college-level math background is required

COMS W1001 Introduction to Information Science. 3 points .

Basic introduction to concepts and skills in Information Sciences: human-computer interfaces, representing information digitally, organizing and searching information on the internet, principles of algorithmic problem solving, introduction to database concepts, and introduction to programming in Python.

COMS W1002 COMPUTING IN CONTEXT. 4.00 points .

Introduction to elementary computing concepts and Python programming with domain-specific applications. Shared CS concepts and Python programming lectures with track-specific sections. Track themes will vary but may include computing for the social sciences, computing for economics and finance, digital humanities, and more. Intended for nonmajors. Students may only receive credit for one of ENGI E1006 or COMS W1002

COMS W1004 Introduction to Computer Science and Programming in Java. 3 points .

A general introduction to computer science for science and engineering students interested in majoring in computer science or engineering. Covers fundamental concepts of computer science, algorithmic problem-solving capabilities, and introductory Java programming skills. Assumes no prior programming background. Columbia University students may receive credit for only one of the following two courses: 1004  or  1005 .

COMS W1007 Honors Introduction to Computer Science. 3 points .

Prerequisites: AP Computer Science with a grade of 4 or 5 or similar experience.

An honors-level introduction to computer science, intended primarily for students considering a major in computer science. Computer science as a science of abstraction. Creating models for reasoning about and solving problems. The basic elements of computers and computer programs. Implementing abstractions using data structures and algorithms. Taught in Java. 

COMS W3134 Data Structures in Java. 3 points .

Prerequisites: ( COMS W1004 ) or knowledge of Java.

Data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Rudiments of the analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , COMS W3136 , COMS W3137 .

COMS W3136 ESSENTIAL DATA STRUCTURES. 4.00 points .

Prerequisites: ( COMS W1004 ) or ( COMS W1005 ) or ( COMS W1007 ) or ( ENGI E1006 ) Prerequisites: ( COMS W1004 ) or ( COMS W1005 ) or ( COMS W1007 ) or ( ENGI E1006 ) A second programming course intended for nonmajors with at least one semester of introductory programming experience. Basic elements of programming in C and C , arraybased data structures, heaps, linked lists, C programming in UNIX environment, object-oriented programming in C , trees, graphs, generic programming, hash tables. Due to significant overlap, students may only receive credit for either COMS W3134 , W3136 , or W3137

COMS W3137 HONORS DATA STRUCTURES & ALGOL. 4.00 points .

Prerequisites: ( COMS W1004 ) or ( COMS W1007 ) Corequisites: COMS W3203 Prerequisites: ( COMS W1004 ) or ( COMS W1007 ) Corequisites: COMS W3203 An honors introduction to data types and structures: arrays, stacks, singly and doubly linked lists, queues, trees, sets, and graphs. Programming techniques for processing such structures: sorting and searching, hashing, garbage collection. Storage management. Design and analysis of algorithms. Taught in Java. Note: Due to significant overlap, students may receive credit for only one of the following three courses: COMS W3134 , W3136 , or W3137

ENGI E1006 INTRO TO COMP FOR ENG/APP SCI. 3.00 points .

An interdisciplinary course in computing intended for first year SEAS students. Introduces computational thinking, algorithmic problem solving and Python programming with applications in science and engineering. Assumes no prior programming background

STEM BC2223 PROGRAMMING BEHAV SCIENCES. 4.00 points .

Possible Electives

Please note that the list of possible electives below is just to give students ideas. Whether or not a student can count a particular course as an elective depends upon the student’s area of specialization. Any course that builds expertise in the student’s area of specialization may be counted as an elective with the program director’s approval, regardless of whether it is listed below; a course that does not build expertise in the student’s area of specialization may not be counted as an elective even if it is listed below.

ANTH UN1009 INTRO TO LANGUAGE & CULTURE. 3.00 points .

This is an introduction to the study of the production, interpretation, and reproduction of social meanings as expressed through language. In exploring language in relation to culture and society, it focuses on how communication informs and transforms the sociocultural environment

COGS GU4050 Natural and Artificial Neural Networks. 3.00 points .

Artificial neural networks can do amazing things. They can play chess, recognize faces, predict human behavior, learn language, and create art. Natural neural networks -- that is to say, brains -- can do many of the same things, often a little more clumsily. But, unlike artificial networks, they can switch seamlessly between two tasks, learn to perform them without supervision, and do not need to be told to -- actually, they can choose to refuse. Brains provided the initial inspiration for the artificial networks, which is why we call them 'artificial neural networks.' But how deep are the similarities between the two? Do they share more than a few abilities, a similar structure, and a common nomenclature?

COGS GU4051 Natural and Artificial Neural Networks Lab. 1.00 point .

Understanding the powers and limitations of artificial neural networks requires exposure to both concepts and practice. This lab section focuses on the latter, supplementing the conceptual framework from the lecture, Natural and Artificial Neural Networks. The lab focuses on giving students without a background in computer science hands-on experience with basic programming in Python, tools for data science, and a variety of machine learning algorithms

COGS GU4800 Resource-Constrained Decision Making. 4.00 points .

There is a fundamental puzzle about human intelligence: How are we incredibly smart and stupid at the same time? Humans deal successfully with the world in a way that no machine can (for now), yet we routinely behave in ways that seem grossly inconsistent with normative canons of rational inference and rational choice. This course will seek to resolve the paradox by exploring the idea that while we make many mistakes, these mistakes are not haphazard; instead, they reflect a brain that is highly efficient at inference and decision making within the information, time, and energy constraints imposed by the finite resources available to it. In other words, our brains may be “resource-rational” even if they fail to conform to ideal canons of rationality. We will explore this idea by considering the structure of errors, biases and illusions in the context of perceptual judgments, more abstract cognitive judgments (perceptions of numerical magnitudes or probabilities), and economic decisions; we will see that there are many analogies between the kinds of characteristic errors that people make in all of these contexts. A potential explanatory framework, which can be applied across contexts, considers what optimal decisions should be like in the case of a decision unit that has only imprecise information about its situation. Hence statistical modeling and statistical inference are key elements in the computational models of human decision making that we wish to discuss

COMS W4170 USER INTERFACE DESIGN. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) Introduction to the theory and practice of computer user interface design, emphasizing the software design of graphical user interfaces. Topics include basic interaction devices and techniques, human factors, interaction styles, dialogue design, and software infrastructure. Design and programming projects are required

COMS W4701 ARTIFICIAL INTELLIGENCE. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and any course on probability. Prior knowledge of Python is recommended. Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) and any course on probability. Prior knowledge of Python is recommended. Provides a broad understanding of the basic techniques for building intelligent computer systems. Topics include state-space problem representations, problem reduction and and-or graphs, game playing and heuristic search, predicate calculus, and resolution theorem proving, AI systems and languages for knowledge representation, machine learning and concept formation and other topics such as natural language processing may be included as time permits

COMS W4705 NATURAL LANGUAGE PROCESSING. 3.00 points .

Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructor's permission. Prerequisites: ( COMS W3134 or COMS W3136 or COMS W3137 ) or the instructors permission. Computational approaches to natural language generation and understanding. Recommended preparation: some previous or concurrent exposure to AI or Machine Learning. Topics include information extraction, summarization, machine translation, dialogue systems, and emotional speech. Particular attention is given to robust techniques that can handle understanding and generation for the large amounts of text on the Web or in other large corpora. Programming exercises in several of these areas

COMS W4731 Computer Vision I: First Principles. 3.00 points .

Prerequisites: Fundamentals of calculus, linear algebra, and C programming. Students without any of these prerequisites are advised to contact the instructor prior to taking the course. Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications

COMS W4771 MACHINE LEARNING. 3.00 points .

Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in MATLAB

COMS W4772 Advanced Machine Learning. 3 points .

Prerequisites: ( COMS W4771 ) or instructor's permission; knowledge of linear algebra & introductory probability or statistics is required.

An exploration of advanced machine learning tools for perception and behavior learning. How can machines perceive, learn from, and classify human activity computationally? Topics include appearance-based models, principal and independent components analysis, dimensionality reduction, kernel methods, manifold learning, latent models, regression, classification, Bayesian methods, maximum entropy methods, real-time tracking, extended Kalman filters, time series prediction, hidden Markov models, factorial HMMS, input-output HMMs, Markov random fields, variational methods, dynamic Bayesian networks, and Gaussian/Dirichlet processes. Links to cognitive science.

CPLS GU4510 The Mind Between Literature and the Brain. 3.00 points .

What — to paraphrase Catherine Malabou — should literary studies do with neuroscience? How should critics and theorists approach the wealth of research about the neural bases of cognition? Should empirical findings about the brain supplant or complement interpretative and speculative theories of the psyche in the literary critic’s toolkit? Is the psyche and its “inner life” still a meaningful level of analysis for literary scholars? The field of “cognitive literary studies,” as the heterogeneous body of work drawing from research psychology, cognitive science and neuroscience is known, has steadily grown in stature over the last few decades, in lockstep with the burgeoning prominence of neuroscience in popular culture and within the academy. Some of its exponents argue that the rise of neuroscience must imply the decline of psychoanalysis and other “folk” psychologies. Others point to the constraints of reproducibility and of the empirical method as insur-mountable handicaps for the study of complex cultural objects such as literature. In this seminar, we will consider the literary experience as a whole — from the act of reading and comprehension, to the affective impact of reading and even the lifelong permanence in one’s memory and imagination of what Eve Sedgwick called “phantasy books” — and ask which parts of the experience can be fruitfully elucidated by reference to empirical knowledge about neural processes. Individual classes will focus on neuro-phenomenology, neuro-psychoanalysis, neuro-aesthetics, the neuroscience of reading, theory of mind, affect studies and critical theory. We complement these theoretical explorations with a small archive of twentieth-century writing (by Virginia Woolf, Samuel Beckett, Alain Robbe-Grillet, W.G. Sebald and Sarah Kane) that questions and subverts our assumptions about the representation of mental life in literary work. The course has no pre-requisites and it is open to undergraduate and graduate students

ECON GU4020 ECON OF UNCERTAINTY & INFORMTN. 3.00 points .

Prerequisites: ECON UN3211 and ECON UN3213 and STAT UN1201 Prerequisites: ECON UN3211 and ECON UN3213 and STAT UN1201 Topics include behavior uncertainty, expected utility hypothesis, insurance, portfolio choice, principle agent problems, screening and signaling, and information theories of financial intermediation

ECON GU4840 BEHAVIORAL ECONOMICS. 3.00 points .

Prerequisites: ECON UN3211 and ECON UN3213 Prerequisites: ECON UN3211 and ECON UN3213 Within economics, the standard model of behavior is that of a perfectly rational, self interested utility maximizer with unlimited cognitive resources. In many cases, this provides a good approximation to the types of behavior that economists are interested in. However, over the past 30 years, experimental and behavioral economists have documented ways in which the standard model is not just wrong, but is wrong in ways that are important for economic outcomes. Understanding these behaviors, and their implications, is one of the most exciting areas of current economic inquiry. The aim of this course is to provide a grounding in the main areas of study within behavioral economics, including temptation and self control, fairness and reciprocity, reference dependence, bounded rationality and choice under risk and uncertainty. For each area we will study three things: 1. The evidence that indicates that the standard economic model is missing some important behavior 2. The models that have been developed to capture these behaviors 3. Applications of these models to (for example) finance, labor and development economics As well as the standard lectures, homework assignments, exams and so on, you will be asked to participate in economic experiments, the data from which will be used to illustrate some of the principals in the course. There will also be a certain small degree of classroom ‘flipping’, with a portion of many lectures given over to group problem solving. Finally, an integral part of the course will be a research proposal that you must complete by the end of the course, outlining a novel piece of research that you would be interested in doing

ECON GU4860 BEHAVIORAL FINANCE. 3.00 points .

Prerequisites: ECON UN3211 and ECON UN3213 and ECON UN3412 Prerequisites: ECON UN3211 and ECON UN3213 and ECON UN3412 Neoclassical finance theory seeks to explain financial market valuations and fluctuations in terms of investors having rational expectations and being able to trade without costs. Under these assumptions, markets are efficient in that stocks and other assets are always priced just right. The efficient markets hypothesis (EMH) has had an enormous influence over the past 50 years on the financial industry, from pricing to financial innovations, and on policy makers, from how markets are regulated to how monetary policy is set. But there was very little in prevailing EMH models to suggest the instabilities associated with the Financial Crisis of 2008 and indeed with earlier crises in financial market history. This course seeks to develop a set of tools to build a more robust model of financial markets that can account for a wider range of outcomes. It is based on an ongoing research agenda loosely dubbed “Behavioral Finance”, which seeks to incorporate more realistic assumptions concerning human rationality and market imperfections into finance models. Broadly, we show in this course that limitations of human rationality can lead to bubbles and busts such as the Internet Bubble of the mid-1990s and the Housing Bubble of the mid-2000s; that imperfections of markets — such as the difficulty of short-selling assets — can cause financial markets to undergo sudden and unpredictable crashes; and that agency problems or the problems of institutions can create instabilities in the financial system as recently occurred during the 2008 Financial Crisis. These instabilities in turn can have feedback effects to the performance of the real economy in the form of corporate investments

LING GU4202 COGNITIVE LINGUISTICS. 3.00 points .

Prerequisites: LING UN3101 previously or concurrently. Reading and discussion of scholarly literature on the cognitive approach to language, including: usage-oriented approaches to language, frame semantics, construction grammar, theories of conceptual metaphor and mental spaces; alongside of experimental research on language acquisition, language memory, prototypical and analogous thinking, and the role of visual imagery in language processing

LING GU4206 ADV GRAMMAR AND GRAMMARS. 3.00 points .

Prerequisites: LING UN3101 LING W3101. An investigation of the possible types of grammatical phenomena (argument structure, tense/aspect/mood, relative clauses, classifiers, and deixis). This typological approach is enriched by the reading of actual grammars of languages from Asia, Africa, Australia, and the Americas in which gramatical descriptions are read with an eye to important notional concepts of grammar: reference and categorization, case and role of arguments with predicates (ergativity), tense/aspect/mood. Discussion of meaning is combined with attention to expression (that is, morphology), which yanks our attention towards language change (grammaticalization)

LING GU4376 PHONETICS & PHONOLOGY. 3.00 points .

Prerequisites: LING UN3101 Prerequisites: LING UN3101 An investigation of the sounds of human language, from the perspective of phonetics (articulation and acoustics, including computer-aided acoustic analysis) and phonology (the distribution and function of sounds in individual languages)

LING GU4903 SYNTAX. 3.00 points .

Prerequisites: LING UN3101 Prerequisites: LING UN3101 Syntax - the combination of words - has been at the center of the Chomskyan revolution in Linguistics. This is a technical course which examines modern formal theories of syntax, focusing on later versions of generative syntax (Government and Binding) with secondary attention to alternative models (HPSG, Categorial Grammar)

MUSI UN2320 Introduction to Music Cognition. 3.00 points .

The aim of music cognition is to understand the musical mind. This course is an introduction to a variety of key topics in this field, including human development, evolution, neural processing, embodied knowledge, memory and anticipation, cross-cultural perspectives, and emotions. The course explores recent research on these topics, as well as ways in which this research can be applied to music scholarship. Readings are drawn from fields as diverse as music theory, psychology, biology, anthropology, and neuroscience, and include general works in cognitive science, theoretical work focused on specific musical issues, and reports of empirical research

MUSI GU4325 Topics in Music Cognition. 3.00 points .

This advanced seminar builds on the Introduction to Music Cognition (MUSIC UN2320) with an in-depth inquiry into selected key topics in the field of Music Cognition. Specific topics vary each year, depending on interest and availability of instructors, and include human development; evolution; communication and music’s relation to language; embodied knowledge; first-person awareness; metaphor; ineffability; neuroscience; mental representations; memory and anticipation; cross-cultural studies; emotions; musical aesthetics; artificial intelligence; agency; creativity; and music’s relation to other art forms. Each semester the course delves into recent research on 3–4 of these topics, focusing in particular on how this research can be applied to questions of musical knowledge. Advanced readings are drawn from fields as diverse as music theory, psychology, biology, anthropology, philosophy, and neuroscience. They include general works in cognitive science, theoretical work focused on specific musical issues, and reports of empirical research

PHIL UN2685 INTRO TO PHIL OF LANGUAGE. 3.00 points .

This course gives students an introduction to various topics in the Philosophy of Language

PHIL UN3685 PHILOSOPHY OF LANGUAGE. 3.00 points .

This course is a survey of analytic philosophy of language. It addresses central issues about the nature of meaning, including: sense and reference, speech acts, pragmatics, and the relationship between meaning and use, meaning and context, and meaning and truth

PHIL UN3840 The Nature and Significance of Animal Minds. 3 points .

Humans have a complicated relationship with other animals. We love them, befriend them and save them. We hunt, farm and eat them. We experiment on and observe them to discover more about them and to discover more about ourselves. For many of us, our pets are amongst the most familiar inhabitants of our world. Yet when we try to imagine what is going on in a dog or cat's mind--let alone that of a crow, octopus or bee--many of us are either stumped about how to go about this, or (the science strongly suggests) getting things radically wrong. Is our thought about and behavior towards animals ethically permissible, or even consistent, Can we reshape our habits of thought about animals to allow for a more rational, richer relationship with the other inhabitants of our planet? In this course, students will reflect on two closely intertwined questions: an ethical question, what sort of relationship ought we to have with animals?; and a metaphysical question, what is the nature of animal minds? Readings will primarily be be from philosophy and ethics and the cognitive sciences, with additional readings from literature and biology.  There are no prerequisites for this class--it will be helpful but certainly not necessary to have taken previous classes in philosophy(especially ethics and philosophy of mind) or in cognitive science.

PHIL GU4495 PERCEPTION. 3.00 points .

This course addresses the fabulously rich range of issues about the nature of perception, including: perceptual mental representation and its content; computational explanation; justifying beliefs; knowledge and thought about perception; and perception of music. Perception is an interdisciplinary subject par excellence. Readings will be drawn from philosophy and psychology, aesthetics, and artificial intelligence

PHIL GU4660 PHILOSOPHY OF MIND. 3.00 points .

PSYC BC2129 DEVELOPMENTAL PSYCHOLOGY-LEC. 3.00 points .

Prerequisites: BC1001 or permission of the instructor. Prerequisites: PSYC BC1001 Introduction to Psychology or COGS UN1001 Introduction to Cognitive Science or permission of the instructor. Lecture course covering cognitive, linguistic, perceptual, motor, social, affective, and personality development from infancy to adolescence. Note that this lecture can be taken without its affiliated lab, PSYC BC2128 , however, if a student completes this lecture, she cannot enroll in the lab in a later semester. The following Columbia University course is considered overlapping and a student cannot receive credit for both the BC course and the equivalent CU course: PSYC UN2280 Introduction to Developmental Psychology

PSYC BC2163 Human Learning and Memory. 3 points .

Prerequisites: BC1001 and at least one psychology lab course, or permission of the instructor. Enrollment limited to 20 students.

Survey of contemporary theories and empirical research on human memory. Topics will include sensory, short term and long term memory, levels of processing, organization, forgetting, and encoding specificity. Special topics include eyewitness testimony, amnesia, implicit memory, and false memory.

PSYC BC3164 PERCEPTION AND LANGUAGE. 4.00 points .

Prerequisites: BC 1001 and one of the following: BC2106/2107, BC2109/2110, BC2118/2119, BC2128/2129, or permission of the instructor. Enrollment limited to 20 students

Psychological investigations of spoken communication from a listener's perspective. Topics include perception and sounds of speech and the apprehension of meaning from words and utterances; the perceptual basis for rhyme and rhythm in speech; and the natural history of vocal communication.

PSYC BC3369 Language Development. 4 points .

Not offered during 2023-2024 academic year.

Prerequisites: BC1001, one Psychology laboratory course, one of the following: PSYC W2240, BC1128 /1129, BC1129, or LIN BC V1101, and permission of the instructor. Enrollment limited to 15 students.

Examines the acquisition of a first language by children, from babbling and first words to complex sentence structure and wider communicative competence. Signed and spoken languages, cross-linguistic variation and universalities, language genesis and change, and acquisition by atypical populations will be discussed.

PSYC BC3372 Comparative Cognition. 4 points .

Prerequisites: BC1001 and one additional course in psychology. Or permission of the instructor. Enrollment limited to 20 students.

Review and critical evaluation of current empirical research investigating cognitive processes in both human and non-human species. Topics include comparisons in episodic memory, metacognition, theory of mind, self-awareness, and language abilities.

PSYC BC3381 Theory of Mind and Intentionality. 4 points .

Prerequisites: BC1001 and one other Psychology course, or permission of the instructor. Enrollment limited to 15 students.

Survey and critical analysis of the developmental and neurological research on theory of mind -the attribution of mental states like belief, desire, and knowledge to others- in humans and nonhuman animals. Emphasis on the role of intentionality, stages of acquisition, neurological and genetic bases, and deficits in theory of mind.

PSYC BC3384 Social Cognition. 4 points .

Prerequisites: BC 1001 and one of the following: BC1138/1137 Social Psychology, BC1115/1114 Cognitive Psychology, or permission of the instructor.

Survey of research from the field of social cognition, exploring cognitive processes involved in social functioning.  Topics include attention, interpretation, evaluation, judgment, attribution, and memory processes.  Both controlled and automatic processes will be considered, and the roles of motives, goals, and affective variables will be discussed.

PSYC BC3390 CANINE COGNITION. 4.00 points .

Prerequisites: BC1001 and one other Psychology course. Enrollment limited to 15 students. Permission of the instructor is required. An examination of the scientific study of the domestic dog. Emphasis will be on the evolutionary history of the species; the dogs social cognitive skills; canid perceptual and sensory capacities; dog-primate comparative studies; and dog-human interaction

PSYC BC3399 HUMAN AND MACHINES. 4.00 points .

Prerequisites: ( PSYC BC1001 ) and Instructor approval Prerequisites: ( PSYC BC1001 ) and Instructor approval This course will examine the social psychology of Human-Machine interactions, exploring the idea that well-established social psychological processes play critical roles in interactions with non-social objects. The first half of the seminar will examine the social psychology of perception across distinct sensory modalities (shape, motion, voice, touch), whereas the second half will focus on social psychological processes between humans and non-human entities (objects, computers, robots)

PSYC UN2250 Evolution of Cognition. 3 points .

Prerequisites: PSYC UN1001 or PSYC UN1010 or the instructor's permission.

A systematic review of different forms of cognition as viewed in the context of the theory of evolution. Specific topics include the application of the theory of evolution to behavior, associative learning, biological constraints on learning, methods for studying the cognitive abilities of animals, levels of representation, ecological influences on cognition, and evidence of consciousness in animals.

PSYC UN2280 Developmental Psychology. 3.00 points .

CC/GS: Partial Fulfillment of Science Requirement Enrollment may be limited. Attendance at the first two classes is mandatory.

Prerequisites: PSYC UN1001 or PSYC UN1010 or the equivalent. Prerequisites: PSYC UN1001 or PSYC UN1010 or the equivalent. Introduction to the scientific study of human development, with an emphasis on psychobiological processes underlying perceptual, cognitive, and emotional development

PSYC UN3270 COMPUT APPROACHES-HUMAN VISION. 3.00 points .

This course will be offered in Fall 2016.

Prerequisites: some background in psychology and/or neurophysiology (e.g., PSYC UN1001 , PSYC UN1010 , PSYC UN2230, PSYC UN2450 ; BIOL UN3004 or BIOL UN3005 ) is desirable. See instructor if you have questions about your background. Some background in mathematics and computer science (e.g., calculus or linear algebra, a programming language) is highly recommended. Prerequisites: some background in psychology and/or neurophysiology (e.g. PSYC UN1001 , PSYC UN1010 , PSYC UN2230, PSYC UN2450 ; BIOL UN3004 or BIOL UN3005 ) is desirable. See instructor if you have questions about your background. Some background in mathematics and computer science (e.g. calculus or linear algebra, a programming language) is highly recommended. Study of human vision--both behavioral and physiological data--within a framework of computational and mathematical descriptions. Please contact Prof. Graham by e-mail ([email protected]) if you are interested in this course

PSYC UN3290 Self: A Cognitive Exploration (Seminar). 4 points .

Prerequisites: PSYC UN1001 or PSYC UN1010 , or the equivalent, plus the instructor's permission.

What does it mean to have a sense of self? Is it uniquely human? Taking a cognitive perspective, we will discuss these questions as well as self-reflective and self-monitoring abilities, brain structures relevant to self-processing, and disorders of self. We will also consider the self from evolutionary, developmental, neuroscience, and psychopathological perspectives.

PSYC UN3445 THE BRAIN AND MEMORY. 4.00 points .

Prerequisites: ( PSYC UN1010 ) or Equivalent introductory course in neuroscience or cognitive psychology and the instructor's permission Prerequisites: ( PSYC UN1010 ) or Equivalent introductory course in neuroscience or cognitive psychology and the instructors permission This seminar will give a comprehensive overview of episodic memory research: what neuroimaging studies, patient studies, and animal models have taught us about how the brain creates, stores, and retrieves memories

PSYC UN3450 Evolution of Intelligence, Animal Communication, & Language. 3.00 points .

Prerequisites: PSYC UN1001 or PSYC UN1010 , and the instructor's permission. Prerequisites: PSYC UN1001 , and the instructors permission. A systematic review of the evolution language covering the theory of evolution, conditioning theory, animal communication, ape language experiments, infant cognition, preverbal antecedents of language and contemporary theories of language

PSYC GU4202 Theories of Change in Human Development. 4.00 points .

What are the agents of developmental change in human childhood? How has the scientific community graduated from nature versus nurture, to nature and nurture? This course offers students an in-depth analysis of the fundamental theories in the study of cognitive and social development

PSYC GU4222 The Cognitive Neuroscience of Aging (Seminar). 4 points .

Prerequisites: courses in introductory psychology and cognitive psychology; and the instructor's permission.

Comprehensive overview of various conceptual and methodologic approaches to studying the cognitive neuroscience of aging. The course will emphasize the importance of combining information from cognitive experimental designs, epidemiologic studies, neuroimaging, and clinical neuropsychological approaches to understand individual differences in both healthy and pathological aging.

PSYC GU4223 MEMORY & EXEC FUNCT:LIFESPAN. 4.00 points .

Prerequisites: the instructor's permission, plus PSYC UN1001 or PSYC UN1010 , or the equivalent. Optimal preparation will include some background in experimental design and statistics. Prerequisites: the instructors permission, plus PSYC UN1001 or PSYC UN1010 , or the equivalent. Optimal preparation will include some background in experimental design and statistics. Memory and executive processing are critical cognitive functions required for successfully navigating everyday life. In lifespan studies, both exhibit relatively long developmental trajectories followed by stasis and then relative decline in old age. Yet, neither memory nor executive function is a unitary construct. Rather, each is comprised of separable components that may show different developmental trajectories and declines or maintenance at older ages. Moreover, memory is malleable and is a reconstruction of past experience, not an exact reproduction. We will discuss a range of topics related to the development, maintenance and potential decline in memory and executive function from infancy through old age

PSYC GU4225 CONSCIOUSNESS & ATTENTION. 4.00 points .

Prerequisites: the instructor's permission; some basic knowledge of cognitive science and neuroanatomy is desirable, but not necessary. Modern theories attempt to characterize the human mind in terms of information processing. But machines that process information do not seem to feel anything; a computer may for instance receive inputs from a video camera, yet it would be hard to imagine that it sees or experiences the vividness of colors like we do. Nobody has yet provided a convincing theory as to how to explain the subjective nature of our mental lives in objective physical terms. This is called the problem of consciousness, and is generally considered to be one of the last unsolved puzzles in science. Philosophers even debate whether there could be a solution to this problem at all. Students in this course may be recruited for participation in a voluntary research study. Students who choose not to participate in the study will complete the same course requirements as those who do, and an individual's choice will not affect their grade or status as a student in the course

PSYC GU4229 ATTENTION AND PERCEPTION. 4.00 points .

Prerequisites: ( PSYC UN1010 ) or Equivalent introductory course in neuroscience or cognitive psychology Prerequisites: ( PSYC UN1010 ) or Equivalent introductory course in neuroscience or cognitive psychology This seminar aims to provide an in-depth overview of neuroscientific knowledge regarding two critical cognitive functions: attention and perception. For each topic, results from behavioral studies are combined with those from recent neurocognitive approaches – primarily neuropsychological and functional brain imaging studies – that reveal the underlying neural networks and brain mechanisms

PSYC GU4239 COG NEURO NARRATIVE FILM. 3.00 points .

Prerequisites: ( PSYC UN1010 or Equivalent introductory course in neuroscience or cognitive psychology Prerequisites: ( PSYC UN1010 or Equivalent introductory course in neuroscience or cognitive psychology This seminar will provide a broad survey of how narrative stories, films, and performances have been used as tools to study cognition in psychology and neuroscience

PSYC GU4242 Evolution of Language (seminar). 3.00 points .

Prerequisites: PSYC UN1001 or

This seminar will consider the evolution of language at the levels of the word and grammar, in each instance, phylogenetically and ontogenetically. Since humans are the only species that use language, attention will be paid to how language differs from animal communication.

PSYC GU4244 LANGUAGE AND MIND. 4.00 points .

Prerequisites: PSYC UN1001 and Preferably, an additional course in psychology, focusing on cognition, development, or research methods. Instructor permission required. Prerequisites: PSYC UN1001 and Preferably, an additional course in psychology, focusing on cognition, development, or research methods. Instructor permission required. This seminar explores the relationship between language and thought by investigating how language is mentally represented and processed; how various aspects of language interact with each other; and how language interacts with other aspects of cognition including perception, concepts, world knowledge, and memory. Students will examine how empirical data at the linguistic, psychological, and neuroscientific levels can bear on some of the biggest questions in the philosophy of mind and language and in psychology

PSYC GU4270 COGNITIVE PROCESSES. 3.00 points .

Prerequisites: For undergraduates: one course in cognitive psychology or cognitive neuroscience, or the equivalent, and the instructor's permission. Prerequisites: For undergraduates: one course in cognitive psychology or cognitive neuroscience, or the equivalent, and the instructors permission. Metacognition and control processes in human cognition. Basic issues include the cognitive mechanisms that enable people to monitor what they know and predict what they will know, the errors and biases involved in self-monitoring, and the implications of metacognitive ability for peoples self-determined learning, behavior, and their understanding of self

PSYC GU4280 CORE KNOWLEDGE. 4.00 points .

Prerequisites: For undergraduates: courses in introductory psychology, cognitive or developmental psychology, and the instructor's permission. Prerequisites: For undergraduates: courses in introductory psychology, cognitive or developmental psychology, and the instructors permission. Core Knowledge explores the origins and development of knowledge in infants and children, with an additional emphasis on evolutionary cognition. In this course, we will examine evidence from cognitive psychology, developmental psychology, comparative psychology, neuroscience, and linguistics to look at the childs conception of objects, number, space, language, agency, morality and the social world. We will look at which aspects of knowledge are uniquely human, which are shared with other animals, and how this knowledge changes as children develop

PSYC GU4281 The Psychology of Curiosity. 4.00 points .

Prerequisites: PSYC UN1001 or equivalent introductory psychology course Prerequisites: PSYC UN1001 or equivalent introductory psychology course What is curiosity and how do we study it? How does curiosity facilitate learning? This course will explore the various conceptual and methodological approaches to studying curiosity and curiosity-driven learning, including animal and human studies of brain and behavior

PSYC GU4287 DECISION ARCHITECTURE. 4.00 points .

Prerequisites: ( PSYC UN2235 ) or an equivalent course on judgment and decision making ,AND the instructor's permission Prerequisites: ( PSYC UN2235 ) or an equivalent course on judgment and decision making ,AND the instructors permission This course reviews current research in the domain of decision architecture: the application of research in cognitive and social psychology to real-world situations with the aim of influencing behavior. This seminar will discuss recent and classic studies, both of decision theory and of applied decision research, to explore the effectiveness—as well as the limitations—of a selection of these behavioral “nudges.”

PSYC GU4289 THE GAMES PEOPLE PLAY:PSYCH OF STRAT DEC. 3.00 points .

Prerequisites: ( PSYC UN2235 ) or equivalent course on judgment and decision-making Prerequisites: ( PSYC UN2235 ) or equivalent course on judgment and decision-making A seminar course exploring strategic decision making (also known as behavioral game theory). This course examines the psychology underlying situations in which outcomes are determined by choices made by multiple decision makers. The prime objective will be to examine the use of experimental games to test psychological theories

PSYC GU4430 Learning and the Brain (Seminar). 4 points .

Prerequisites: courses in introductory psychology and/or neuroscience, and the instructor's permission.

What are the neural mechanisms that support learning, memory, and choices? We will review current theories in the cognitive neuroscience of human learning, discuss how learning and decision making interact, and consider the strengths and weaknesses of two influential methods in the study of human brain and behavior--functional imaging and patient studies.

PSYC GU4435 NON-MNEMONIC FUNC OF MEMORY SYSTEMS. 4.00 points .

Prerequisites: ( PSYC UN1010 ) or equivalent introductory course in neuroscience or cognitive psychology Prerequisites: ( PSYC UN1010 ) or equivalent introductory course in neuroscience or cognitive psychology The past decade has produced an extraordinary amount of evidence that challenges the classic view of a “medial temporal lobe memory system”, namely, the idea that the medial temporal lobe plays a necessary role in long-term memory but not other cognitive functions. This course will introduce these challenges to the traditional perspective by exploring functions of the so-called memory system in domains outside of long-term memory

SOAR AV4000 SOUND:Music, Math, and Mind. 3.00 points .

This course is a detailed and hands-on (ears-on) exploration of the fundamental physical, physiological, and psychological aspects of sound. Topics covered include sound waves and their physical nature, the propagation and speed of sound in different mediums, geological and other non-living sound sources, animal and insect sound generating strategies, sound perception mechanisms and abilities in different species, the physiology of human hearing and the structure of the human ear, psycho-acoustics and human sound perception, sonic illusions and tricks of the ear. In-class experiments and research make up the majority of the class. Each student will design and lead at least one experiment/demo session. Students also respond to creative weekly prompts about sound topics on courseworks. We also have visits with a number of special guests during the term

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College Admissions , College Essays

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The personal statement might just be the hardest part of your college application. Mostly this is because it has the least guidance and is the most open-ended. One way to understand what colleges are looking for when they ask you to write an essay is to check out the essays of students who already got in—college essays that actually worked. After all, they must be among the most successful of this weird literary genre.

In this article, I'll go through general guidelines for what makes great college essays great. I've also compiled an enormous list of 100+ actual sample college essays from 11 different schools. Finally, I'll break down two of these published college essay examples and explain why and how they work. With links to 177 full essays and essay excerpts , this article is a great resource for learning how to craft your own personal college admissions essay!

What Excellent College Essays Have in Common

Even though in many ways these sample college essays are very different from one other, they do share some traits you should try to emulate as you write your own essay.

Visible Signs of Planning

Building out from a narrow, concrete focus. You'll see a similar structure in many of the essays. The author starts with a very detailed story of an event or description of a person or place. After this sense-heavy imagery, the essay expands out to make a broader point about the author, and connects this very memorable experience to the author's present situation, state of mind, newfound understanding, or maturity level.

Knowing how to tell a story. Some of the experiences in these essays are one-of-a-kind. But most deal with the stuff of everyday life. What sets them apart is the way the author approaches the topic: analyzing it for drama and humor, for its moving qualities, for what it says about the author's world, and for how it connects to the author's emotional life.

Stellar Execution

A killer first sentence. You've heard it before, and you'll hear it again: you have to suck the reader in, and the best place to do that is the first sentence. Great first sentences are punchy. They are like cliffhangers, setting up an exciting scene or an unusual situation with an unclear conclusion, in order to make the reader want to know more. Don't take my word for it—check out these 22 first sentences from Stanford applicants and tell me you don't want to read the rest of those essays to find out what happens!

A lively, individual voice. Writing is for readers. In this case, your reader is an admissions officer who has read thousands of essays before yours and will read thousands after. Your goal? Don't bore your reader. Use interesting descriptions, stay away from clichés, include your own offbeat observations—anything that makes this essay sounds like you and not like anyone else.

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Technical correctness. No spelling mistakes, no grammar weirdness, no syntax issues, no punctuation snafus—each of these sample college essays has been formatted and proofread perfectly. If this kind of exactness is not your strong suit, you're in luck! All colleges advise applicants to have their essays looked over several times by parents, teachers, mentors, and anyone else who can spot a comma splice. Your essay must be your own work, but there is absolutely nothing wrong with getting help polishing it.

And if you need more guidance, connect with PrepScholar's expert admissions consultants . These expert writers know exactly what college admissions committees look for in an admissions essay and chan help you craft an essay that boosts your chances of getting into your dream school.

Check out PrepScholar's Essay Editing and Coaching progra m for more details!

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Links to Full College Essay Examples

Some colleges publish a selection of their favorite accepted college essays that worked, and I've put together a selection of over 100 of these.

Common App Essay Samples

Please note that some of these college essay examples may be responding to prompts that are no longer in use. The current Common App prompts are as follows:

1. Some students have a background, identity, interest, or talent that is so meaningful they believe their application would be incomplete without it. If this sounds like you, then please share your story. 2. The lessons we take from obstacles we encounter can be fundamental to later success. Recount a time when you faced a challenge, setback, or failure. How did it affect you, and what did you learn from the experience? 3. Reflect on a time when you questioned or challenged a belief or idea. What prompted your thinking? What was the outcome? 4. Reflect on something that someone has done for you that has made you happy or thankful in a surprising way. How has this gratitude affected or motivated you? 5. Discuss an accomplishment, event, or realization that sparked a period of personal growth and a new understanding of yourself or others. 6. Describe a topic, idea, or concept you find so engaging that it makes you lose all track of time. Why does it captivate you? What or who do you turn to when you want to learn more?

7. Share an essay on any topic of your choice. It can be one you've already written, one that responds to a different prompt, or one of your own design.

Now, let's get to the good stuff: the list of 177 college essay examples responding to current and past Common App essay prompts. 

Connecticut college.

  • 12 Common Application essays from the classes of 2022-2025

Hamilton College

  • 7 Common Application essays from the class of 2026
  • 7 Common Application essays from the class of 2022
  • 7 Common Application essays from the class of 2018
  • 8 Common Application essays from the class of 2012
  • 8 Common Application essays from the class of 2007

Johns Hopkins

These essays are answers to past prompts from either the Common Application or the Coalition Application (which Johns Hopkins used to accept).

  • 1 Common Application or Coalition Application essay from the class of 2026
  • 6 Common Application or Coalition Application essays from the class of 2025
  • 6 Common Application or Universal Application essays from the class of 2024
  • 6 Common Application or Universal Application essays from the class of 2023
  • 7 Common Application of Universal Application essays from the class of 2022
  • 5 Common Application or Universal Application essays from the class of 2021
  • 7 Common Application or Universal Application essays from the class of 2020

Essay Examples Published by Other Websites

  • 2 Common Application essays ( 1st essay , 2nd essay ) from applicants admitted to Columbia

Other Sample College Essays

Here is a collection of essays that are college-specific.

Babson College

  • 4 essays (and 1 video response) on "Why Babson" from the class of 2020

Emory University

  • 5 essay examples ( 1 , 2 , 3 , 4 , 5 ) from the class of 2020 along with analysis from Emory admissions staff on why the essays were exceptional
  • 5 more recent essay examples ( 1 , 2 , 3 , 4 , 5 ) along with analysis from Emory admissions staff on what made these essays stand out

University of Georgia

  • 1 “strong essay” sample from 2019
  • 1 “strong essay” sample from 2018
  • 10 Harvard essays from 2023
  • 10 Harvard essays from 2022
  • 10 Harvard essays from 2021
  • 10 Harvard essays from 2020
  • 10 Harvard essays from 2019
  • 10 Harvard essays from 2018
  • 6 essays from admitted MIT students

Smith College

  • 6 "best gift" essays from the class of 2018

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Books of College Essays

If you're looking for even more sample college essays, consider purchasing a college essay book. The best of these include dozens of essays that worked and feedback from real admissions officers.

College Essays That Made a Difference —This detailed guide from Princeton Review includes not only successful essays, but also interviews with admissions officers and full student profiles.

50 Successful Harvard Application Essays by the Staff of the Harvard Crimson—A must for anyone aspiring to Harvard .

50 Successful Ivy League Application Essays and 50 Successful Stanford Application Essays by Gen and Kelly Tanabe—For essays from other top schools, check out this venerated series, which is regularly updated with new essays.

Heavenly Essays by Janine W. Robinson—This collection from the popular blogger behind Essay Hell includes a wider range of schools, as well as helpful tips on honing your own essay.

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Analyzing Great Common App Essays That Worked

I've picked two essays from the examples collected above to examine in more depth so that you can see exactly what makes a successful college essay work. Full credit for these essays goes to the original authors and the schools that published them.

Example 1: "Breaking Into Cars," by Stephen, Johns Hopkins Class of '19 (Common App Essay, 636 words long)

I had never broken into a car before.

We were in Laredo, having just finished our first day at a Habitat for Humanity work site. The Hotchkiss volunteers had already left, off to enjoy some Texas BBQ, leaving me behind with the college kids to clean up. Not until we were stranded did we realize we were locked out of the van.

Someone picked a coat hanger out of the dumpster, handed it to me, and took a few steps back.

"Can you do that thing with a coat hanger to unlock it?"

"Why me?" I thought.

More out of amusement than optimism, I gave it a try. I slid the hanger into the window's seal like I'd seen on crime shows, and spent a few minutes jiggling the apparatus around the inside of the frame. Suddenly, two things simultaneously clicked. One was the lock on the door. (I actually succeeded in springing it.) The other was the realization that I'd been in this type of situation before. In fact, I'd been born into this type of situation.

My upbringing has numbed me to unpredictability and chaos. With a family of seven, my home was loud, messy, and spottily supervised. My siblings arguing, the dog barking, the phone ringing—all meant my house was functioning normally. My Dad, a retired Navy pilot, was away half the time. When he was home, he had a parenting style something like a drill sergeant. At the age of nine, I learned how to clear burning oil from the surface of water. My Dad considered this a critical life skill—you know, in case my aircraft carrier should ever get torpedoed. "The water's on fire! Clear a hole!" he shouted, tossing me in the lake without warning. While I'm still unconvinced about that particular lesson's practicality, my Dad's overarching message is unequivocally true: much of life is unexpected, and you have to deal with the twists and turns.

Living in my family, days rarely unfolded as planned. A bit overlooked, a little pushed around, I learned to roll with reality, negotiate a quick deal, and give the improbable a try. I don't sweat the small stuff, and I definitely don't expect perfect fairness. So what if our dining room table only has six chairs for seven people? Someone learns the importance of punctuality every night.

But more than punctuality and a special affinity for musical chairs, my family life has taught me to thrive in situations over which I have no power. Growing up, I never controlled my older siblings, but I learned how to thwart their attempts to control me. I forged alliances, and realigned them as necessary. Sometimes, I was the poor, defenseless little brother; sometimes I was the omniscient elder. Different things to different people, as the situation demanded. I learned to adapt.

Back then, these techniques were merely reactions undertaken to ensure my survival. But one day this fall, Dr. Hicks, our Head of School, asked me a question that he hoped all seniors would reflect on throughout the year: "How can I participate in a thing I do not govern, in the company of people I did not choose?"

The question caught me off guard, much like the question posed to me in Laredo. Then, I realized I knew the answer. I knew why the coat hanger had been handed to me.

Growing up as the middle child in my family, I was a vital participant in a thing I did not govern, in the company of people I did not choose. It's family. It's society. And often, it's chaos. You participate by letting go of the small stuff, not expecting order and perfection, and facing the unexpected with confidence, optimism, and preparedness. My family experience taught me to face a serendipitous world with confidence.

What Makes This Essay Tick?

It's very helpful to take writing apart in order to see just how it accomplishes its objectives. Stephen's essay is very effective. Let's find out why!

An Opening Line That Draws You In

In just eight words, we get: scene-setting (he is standing next to a car about to break in), the idea of crossing a boundary (he is maybe about to do an illegal thing for the first time), and a cliffhanger (we are thinking: is he going to get caught? Is he headed for a life of crime? Is he about to be scared straight?).

Great, Detailed Opening Story

More out of amusement than optimism, I gave it a try. I slid the hanger into the window's seal like I'd seen on crime shows, and spent a few minutes jiggling the apparatus around the inside of the frame.

It's the details that really make this small experience come alive. Notice how whenever he can, Stephen uses a more specific, descriptive word in place of a more generic one. The volunteers aren't going to get food or dinner; they're going for "Texas BBQ." The coat hanger comes from "a dumpster." Stephen doesn't just move the coat hanger—he "jiggles" it.

Details also help us visualize the emotions of the people in the scene. The person who hands Stephen the coat hanger isn't just uncomfortable or nervous; he "takes a few steps back"—a description of movement that conveys feelings. Finally, the detail of actual speech makes the scene pop. Instead of writing that the other guy asked him to unlock the van, Stephen has the guy actually say his own words in a way that sounds like a teenager talking.

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Turning a Specific Incident Into a Deeper Insight

Suddenly, two things simultaneously clicked. One was the lock on the door. (I actually succeeded in springing it.) The other was the realization that I'd been in this type of situation before. In fact, I'd been born into this type of situation.

Stephen makes the locked car experience a meaningful illustration of how he has learned to be resourceful and ready for anything, and he also makes this turn from the specific to the broad through an elegant play on the two meanings of the word "click."

Using Concrete Examples When Making Abstract Claims

My upbringing has numbed me to unpredictability and chaos. With a family of seven, my home was loud, messy, and spottily supervised. My siblings arguing, the dog barking, the phone ringing—all meant my house was functioning normally.

"Unpredictability and chaos" are very abstract, not easily visualized concepts. They could also mean any number of things—violence, abandonment, poverty, mental instability. By instantly following up with highly finite and unambiguous illustrations like "family of seven" and "siblings arguing, the dog barking, the phone ringing," Stephen grounds the abstraction in something that is easy to picture: a large, noisy family.

Using Small Bits of Humor and Casual Word Choice

My Dad, a retired Navy pilot, was away half the time. When he was home, he had a parenting style something like a drill sergeant. At the age of nine, I learned how to clear burning oil from the surface of water. My Dad considered this a critical life skill—you know, in case my aircraft carrier should ever get torpedoed.

Obviously, knowing how to clean burning oil is not high on the list of things every 9-year-old needs to know. To emphasize this, Stephen uses sarcasm by bringing up a situation that is clearly over-the-top: "in case my aircraft carrier should ever get torpedoed."

The humor also feels relaxed. Part of this is because he introduces it with the colloquial phrase "you know," so it sounds like he is talking to us in person. This approach also diffuses the potential discomfort of the reader with his father's strictness—since he is making jokes about it, clearly he is OK. Notice, though, that this doesn't occur very much in the essay. This helps keep the tone meaningful and serious rather than flippant.

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An Ending That Stretches the Insight Into the Future

But one day this fall, Dr. Hicks, our Head of School, asked me a question that he hoped all seniors would reflect on throughout the year: "How can I participate in a thing I do not govern, in the company of people I did not choose?"

The ending of the essay reveals that Stephen's life has been one long preparation for the future. He has emerged from chaos and his dad's approach to parenting as a person who can thrive in a world that he can't control.

This connection of past experience to current maturity and self-knowledge is a key element in all successful personal essays. Colleges are very much looking for mature, self-aware applicants. These are the qualities of successful college students, who will be able to navigate the independence college classes require and the responsibility and quasi-adulthood of college life.

What Could This Essay Do Even Better?

Even the best essays aren't perfect, and even the world's greatest writers will tell you that writing is never "finished"—just "due." So what would we tweak in this essay if we could?

Replace some of the clichéd language. Stephen uses handy phrases like "twists and turns" and "don't sweat the small stuff" as a kind of shorthand for explaining his relationship to chaos and unpredictability. But using too many of these ready-made expressions runs the risk of clouding out your own voice and replacing it with something expected and boring.

Use another example from recent life. Stephen's first example (breaking into the van in Laredo) is a great illustration of being resourceful in an unexpected situation. But his essay also emphasizes that he "learned to adapt" by being "different things to different people." It would be great to see how this plays out outside his family, either in the situation in Laredo or another context.

cognitive science college essay

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Example 2: By Renner Kwittken, Tufts Class of '23 (Common App Essay, 645 words long)

My first dream job was to be a pickle truck driver. I saw it in my favorite book, Richard Scarry's "Cars and Trucks and Things That Go," and for some reason, I was absolutely obsessed with the idea of driving a giant pickle. Much to the discontent of my younger sister, I insisted that my parents read us that book as many nights as possible so we could find goldbug, a small little golden bug, on every page. I would imagine the wonderful life I would have: being a pig driving a giant pickle truck across the country, chasing and finding goldbug. I then moved on to wanting to be a Lego Master. Then an architect. Then a surgeon.

Then I discovered a real goldbug: gold nanoparticles that can reprogram macrophages to assist in killing tumors, produce clear images of them without sacrificing the subject, and heat them to obliteration.

Suddenly the destination of my pickle was clear.

I quickly became enveloped by the world of nanomedicine; I scoured articles about liposomes, polymeric micelles, dendrimers, targeting ligands, and self-assembling nanoparticles, all conquering cancer in some exotic way. Completely absorbed, I set out to find a mentor to dive even deeper into these topics. After several rejections, I was immensely grateful to receive an invitation to work alongside Dr. Sangeeta Ray at Johns Hopkins.

In the lab, Dr. Ray encouraged a great amount of autonomy to design and implement my own procedures. I chose to attack a problem that affects the entire field of nanomedicine: nanoparticles consistently fail to translate from animal studies into clinical trials. Jumping off recent literature, I set out to see if a pre-dose of a common chemotherapeutic could enhance nanoparticle delivery in aggressive prostate cancer, creating three novel constructs based on three different linear polymers, each using fluorescent dye (although no gold, sorry goldbug!). Though using radioactive isotopes like Gallium and Yttrium would have been incredible, as a 17-year-old, I unfortunately wasn't allowed in the same room as these radioactive materials (even though I took a Geiger counter to a pair of shoes and found them to be slightly dangerous).

I hadn't expected my hypothesis to work, as the research project would have ideally been led across two full years. Yet while there are still many optimizations and revisions to be done, I was thrilled to find -- with completely new nanoparticles that may one day mean future trials will use particles with the initials "RK-1" -- thatcyclophosphamide did indeed increase nanoparticle delivery to the tumor in a statistically significant way.

A secondary, unexpected research project was living alone in Baltimore, a new city to me, surrounded by people much older than I. Even with moving frequently between hotels, AirBnB's, and students' apartments, I strangely reveled in the freedom I had to enjoy my surroundings and form new friendships with graduate school students from the lab. We explored The Inner Harbor at night, attended a concert together one weekend, and even got to watch the Orioles lose (to nobody's surprise). Ironically, it's through these new friendships I discovered something unexpected: what I truly love is sharing research. Whether in a presentation or in a casual conversation, making others interested in science is perhaps more exciting to me than the research itself. This solidified a new pursuit to angle my love for writing towards illuminating science in ways people can understand, adding value to a society that can certainly benefit from more scientific literacy.

It seems fitting that my goals are still transforming: in Scarry's book, there is not just one goldbug, there is one on every page. With each new experience, I'm learning that it isn't the goldbug itself, but rather the act of searching for the goldbugs that will encourage, shape, and refine my ever-evolving passions. Regardless of the goldbug I seek -- I know my pickle truck has just begun its journey.

Renner takes a somewhat different approach than Stephen, but their essay is just as detailed and engaging. Let's go through some of the strengths of this essay.

One Clear Governing Metaphor

This essay is ultimately about two things: Renner’s dreams and future career goals, and Renner’s philosophy on goal-setting and achieving one’s dreams.

But instead of listing off all the amazing things they’ve done to pursue their dream of working in nanomedicine, Renner tells a powerful, unique story instead. To set up the narrative, Renner opens the essay by connecting their experiences with goal-setting and dream-chasing all the way back to a memorable childhood experience:

This lighthearted–but relevant!--story about the moment when Renner first developed a passion for a specific career (“finding the goldbug”) provides an anchor point for the rest of the essay. As Renner pivots to describing their current dreams and goals–working in nanomedicine–the metaphor of “finding the goldbug” is reflected in Renner’s experiments, rejections, and new discoveries.

Though Renner tells multiple stories about their quest to “find the goldbug,” or, in other words, pursue their passion, each story is connected by a unifying theme; namely, that as we search and grow over time, our goals will transform…and that’s okay! By the end of the essay, Renner uses the metaphor of “finding the goldbug” to reiterate the relevance of the opening story:

While the earlier parts of the essay convey Renner’s core message by showing, the final, concluding paragraph sums up Renner’s insights by telling. By briefly and clearly stating the relevance of the goldbug metaphor to their own philosophy on goals and dreams, Renner demonstrates their creativity, insight, and eagerness to grow and evolve as the journey continues into college.

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An Engaging, Individual Voice

This essay uses many techniques that make Renner sound genuine and make the reader feel like we already know them.

Technique #1: humor. Notice Renner's gentle and relaxed humor that lightly mocks their younger self's grand ambitions (this is different from the more sarcastic kind of humor used by Stephen in the first essay—you could never mistake one writer for the other).

My first dream job was to be a pickle truck driver.

I would imagine the wonderful life I would have: being a pig driving a giant pickle truck across the country, chasing and finding goldbug. I then moved on to wanting to be a Lego Master. Then an architect. Then a surgeon.

Renner gives a great example of how to use humor to your advantage in college essays. You don’t want to come off as too self-deprecating or sarcastic, but telling a lightheartedly humorous story about your younger self that also showcases how you’ve grown and changed over time can set the right tone for your entire essay.

Technique #2: intentional, eye-catching structure. The second technique is the way Renner uses a unique structure to bolster the tone and themes of their essay . The structure of your essay can have a major impact on how your ideas come across…so it’s important to give it just as much thought as the content of your essay!

For instance, Renner does a great job of using one-line paragraphs to create dramatic emphasis and to make clear transitions from one phase of the story to the next:

Suddenly the destination of my pickle car was clear.

Not only does the one-liner above signal that Renner is moving into a new phase of the narrative (their nanoparticle research experiences), it also tells the reader that this is a big moment in Renner’s story. It’s clear that Renner made a major discovery that changed the course of their goal pursuit and dream-chasing. Through structure, Renner conveys excitement and entices the reader to keep pushing forward to the next part of the story.

Technique #3: playing with syntax. The third technique is to use sentences of varying length, syntax, and structure. Most of the essay's written in standard English and uses grammatically correct sentences. However, at key moments, Renner emphasizes that the reader needs to sit up and pay attention by switching to short, colloquial, differently punctuated, and sometimes fragmented sentences.

Even with moving frequently between hotels, AirBnB's, and students' apartments, I strangely reveled in the freedom I had to enjoy my surroundings and form new friendships with graduate school students from the lab. We explored The Inner Harbor at night, attended a concert together one weekend, and even got to watch the Orioles lose (to nobody's surprise). Ironically, it's through these new friendships I discovered something unexpected: what I truly love is sharing research.

In the examples above, Renner switches adeptly between long, flowing sentences and quippy, telegraphic ones. At the same time, Renner uses these different sentence lengths intentionally. As they describe their experiences in new places, they use longer sentences to immerse the reader in the sights, smells, and sounds of those experiences. And when it’s time to get a big, key idea across, Renner switches to a short, punchy sentence to stop the reader in their tracks.

The varying syntax and sentence lengths pull the reader into the narrative and set up crucial “aha” moments when it’s most important…which is a surefire way to make any college essay stand out.

body-crying-upset-cc0

Renner's essay is very strong, but there are still a few little things that could be improved.

Connecting the research experiences to the theme of “finding the goldbug.”  The essay begins and ends with Renner’s connection to the idea of “finding the goldbug.” And while this metaphor is deftly tied into the essay’s intro and conclusion, it isn’t entirely clear what Renner’s big findings were during the research experiences that are described in the middle of the essay. It would be great to add a sentence or two stating what Renner’s big takeaways (or “goldbugs”) were from these experiences, which add more cohesion to the essay as a whole.

Give more details about discovering the world of nanomedicine. It makes sense that Renner wants to get into the details of their big research experiences as quickly as possible. After all, these are the details that show Renner’s dedication to nanomedicine! But a smoother transition from the opening pickle car/goldbug story to Renner’s “real goldbug” of nanoparticles would help the reader understand why nanoparticles became Renner’s goldbug. Finding out why Renner is so motivated to study nanomedicine–and perhaps what put them on to this field of study–would help readers fully understand why Renner chose this path in the first place.

4 Essential Tips for Writing Your Own Essay

How can you use this discussion to better your own college essay? Here are some suggestions for ways to use this resource effectively.

#1: Get Help From the Experts

Getting your college applications together takes a lot of work and can be pretty intimidatin g. Essays are even more important than ever now that admissions processes are changing and schools are going test-optional and removing diversity standards thanks to new Supreme Court rulings .  If you want certified expert help that really makes a difference, get started with  PrepScholar’s Essay Editing and Coaching program. Our program can help you put together an incredible essay from idea to completion so that your application stands out from the crowd. We've helped students get into the best colleges in the United States, including Harvard, Stanford, and Yale.  If you're ready to take the next step and boost your odds of getting into your dream school, connect with our experts today .

#2: Read Other Essays to Get Ideas for Your Own

As you go through the essays we've compiled for you above, ask yourself the following questions:

  • Can you explain to yourself (or someone else!) why the opening sentence works well?
  • Look for the essay's detailed personal anecdote. What senses is the author describing? Can you easily picture the scene in your mind's eye?
  • Find the place where this anecdote bridges into a larger insight about the author. How does the essay connect the two? How does the anecdote work as an example of the author's characteristic, trait, or skill?
  • Check out the essay's tone. If it's funny, can you find the places where the humor comes from? If it's sad and moving, can you find the imagery and description of feelings that make you moved? If it's serious, can you see how word choice adds to this tone?

Make a note whenever you find an essay or part of an essay that you think was particularly well-written, and think about what you like about it . Is it funny? Does it help you really get to know the writer? Does it show what makes the writer unique? Once you have your list, keep it next to you while writing your essay to remind yourself to try and use those same techniques in your own essay.

body-gears-cogs-puzzle-cc0

#3: Find Your "A-Ha!" Moment

All of these essays rely on connecting with the reader through a heartfelt, highly descriptive scene from the author's life. It can either be very dramatic (did you survive a plane crash?) or it can be completely mundane (did you finally beat your dad at Scrabble?). Either way, it should be personal and revealing about you, your personality, and the way you are now that you are entering the adult world.

Check out essays by authors like John Jeremiah Sullivan , Leslie Jamison , Hanif Abdurraqib , and Esmé Weijun Wang to get more examples of how to craft a compelling personal narrative.

#4: Start Early, Revise Often

Let me level with you: the best writing isn't writing at all. It's rewriting. And in order to have time to rewrite, you have to start way before the application deadline. My advice is to write your first draft at least two months before your applications are due.

Let it sit for a few days untouched. Then come back to it with fresh eyes and think critically about what you've written. What's extra? What's missing? What is in the wrong place? What doesn't make sense? Don't be afraid to take it apart and rearrange sections. Do this several times over, and your essay will be much better for it!

For more editing tips, check out a style guide like Dreyer's English or Eats, Shoots & Leaves .

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What's Next?

Still not sure which colleges you want to apply to? Our experts will show you how to make a college list that will help you choose a college that's right for you.

Interested in learning more about college essays? Check out our detailed breakdown of exactly how personal statements work in an application , some suggestions on what to avoid when writing your essay , and our guide to writing about your extracurricular activities .

Working on the rest of your application? Read what admissions officers wish applicants knew before applying .

Want to improve your SAT score by 160 points or your ACT score by 4 points? We've written a guide for each test about the top 5 strategies you must be using to have a shot at improving your score. Download it for free now:

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The recommendations in this post are based solely on our knowledge and experience. If you purchase an item through one of our links PrepScholar may receive a commission.

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Anna scored in the 99th percentile on her SATs in high school, and went on to major in English at Princeton and to get her doctorate in English Literature at Columbia. She is passionate about improving student access to higher education.

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Yale College Programs of Study 2023–2024

  • Yale University Publications /
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  • Subjects of Instruction /

Cognitive Science

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Director of undergraduate studies: Joshua Knobe , 102 C, 432-1699; www.yale.edu/cogsci

Cognitive science explores the nature of cognitive processes such as perception, reasoning, memory, attention, language, decision making, imagery, motor control, and problem solving. The goal of cognitive science, stated simply, is to understand how the mind works. Cognitive science is an inherently interdisciplinary endeavor, drawing on tools and ideas from fields such as psychology, computer science, linguistics, philosophy, economics, and neuroscience. Approaches include empirical studies of the ontogenetic and phylogenetic development of cognitive abilities, experimental work on cognitive processing in adults, attempts to understand perception and cognition based on patterns of breakdown in pathology, computational and robotic research that strives to simulate aspects of cognition and behavior, neuroscientific investigations of the neural bases of cognition using neural recording and brain scanning, and the development of philosophical theories of the nature of mind.

Prerequisite

An introductory survey course, CGSC 110 , is normally taken by the end of the fall term of the sophomore year and prior to admission to the major.

Requirements of the Major 

The requirements of the major for the B.S. and B.A. degrees are the same, except for the skills requirement and the senior requirement. Fourteen term courses, for a total of thirteen and one half course credits, are required for the major, including the introductory course and the senior requirement. Each major program must include the elements described below. The particular selection of courses must be approved by the director of undergraduate studies (DUS) in order to assure overall coherence. No course may be used to fulfill more than one requirement for the major.

Breadth requirement  A breadth requirement introduces students to the subfields of cognitive science. Each major is required to take a course from four of the following six areas:

1. Computer science: CPSC 201

2. Economics and decision making: ECON 159

3. Linguistics: LING 110 , 116 , 130 , 217 , 232 , 253

4. Neuroscience: CGSC 201 , MCDB 320 , NSCI 340 , PSYC 160 , 270   

5. Philosophy: PHIL 126 , 182 , 269 , 270 , 271

6. Psychology: PSYC 110 , S139E , 140

Depth requirement  Students fulfill a depth requirement by completing six courses that focus on a specific topic or area in cognitive science. The depth courses must be chosen from at least two disciplines, and are typically drawn from the six cognitive science subfields. It may be possible to draw depth courses from other fields when necessary to explore the student's focal topic, in consultation with the DUS. All six depth courses must be at the intermediate or advanced level; for most disciplines, courses numbered 300 or above fulfill the requirement. With permission of the DUS, up to two directed reading or research courses may count toward the depth requirement.

Skills requirement  Because formal techniques are fundamental to cognitive science, one skills course is required, preferably prior to the senior year. Courses that fulfill the skills requirement for the B.A. include CPSC 112 , 202 , LING 224 , PSYC 200 , and 270 , and S&DS 100 , 220 and 230 . Other courses may fulfill this requirement with permission of the DUS. The skills requirement for the B.S. is fulfilled by PSYC 200  or another course with permission of the DUS.

Junior colloquium  In the junior year, students are required to take CGSC 395 , a half-credit colloquium in which majors discuss current issues and research in cognitive science and select a senior essay topic.

Repeat for credit  Only one term of CGSC 471 , 472 , 473 , or 474 may be offered toward the major. 

Credit/D/Fail  Courses taken Credit/D/Fail may not be counted toward the requirements of the major, except with permission of the DUS.

Senior Requirement 

In the senior year, students take CGSC 491 , a full-credit capstone course in which the senior essay is written. Students in the course meet regularly with one another and with the faculty to discuss current work in cognitive science and their own developing research projects. Students must take this course during their last spring term at Yale. If spring is not the student's final term, (e.g., a planned December graduation date), then it is possible to attend the class and complete some of the assignments, but not turn in the finished thesis until November. In this case, a grade of INC will be given for the Spring term. (Unlike other incomplete grades at Yale, an incomplete for a thesis does not expire.)

B.S. degree program  The B.S. degree is typically awarded to students who conduct empirical research as part of their senior requirement. This normally includes designing an experiment and collecting and analyzing data.

B.A. degree program  The B.A. degree is typically awarded to students who conduct a nonempirical senior essay. There are no restrictions on the research format for the B.A.

Advising and Application to the Major 

Students may apply to enter the major at any point after the first year. Applications must be made in writing to the DUS. Applications must include (1) an official or unofficial transcript of work at Yale, (2) a brief statement of purpose, which indicates academic interests and expected focus within the areas of the Cognitive Science major, and (3) a list of the six upper-level courses that the student plans to take as part of the research focus. Application forms and answers to frequently asked questions are available on the program website .

SUMMARY OF MAJOR REQUIREMENTS

Prerequisite  CGSC 110

Number of courses  14 term courses, for a total of 13.5 course credits (incl prereq and senior req)

Specific course required  CGSC 395

Distribution of courses  1 course each in 4 of 6 subfields, as specified for breadth req; 6 courses in a specific topic or area, as specified for depth req; 1 skills course, as specified

Senior requirement   B.S.—e mpirical research and senior essay in CGSC 491 ; B.A. —nonempirical senior essay in CGSC 491

Cognitive science is an interdisciplinary field devoted to exploring the nature of cognitive processes such as perception, reasoning, memory, attention, language, imagery, motor control, and problem solving.   The goal of cognitive science, stated simply, is to understand how the mind works.   Cognitive science is an inherently interdisciplinary endeavor, drawing on tools and ideas from traditional academic fields such as psychology, computer science, linguistics, philosophy, and neuroscience.

Students may apply to enter the major in Cognitive Science at any point after the first year.  CGSC 110 is prerequisite to the major.  Interested students are also encouraged to take an introductory course in computer science, economics, linguistics, neuroscience, philosophy, or psychology.   For more information, see the   program website .

FACULTY ASSOCIATED WITH THE PROGRAM IN COGNITIVE SCIENCE

Professors  Woo-kyoung Ahn ( Psychology ), Stephen Anderson ( Emeritus ), Amy Arnsten ( School of Medicine ), Richard Aslin ( Haskins Laboratories), John Bargh ( Psychology ), Paul Bloom ( Emeritus ) ( Psychology ) , Hal Blumenfeld ( School of Medicine ) , Claire Bowern ( Linguistics ), Marvin Chun ( Psychology ) , Veneeta Dayal ( Linguistics ), Michael Della Rocca ( Philosophy ) , Ravi Dhar ( School of Management ) , Julie Dorsey ( Computer Science ) , Robert Frank ( Linguistics ), Shane Frederick ( School of Management ) , David Gelernter ( Computer Science ) , Tamar Gendler ( Philosophy ), Laurence Horn ( Emeritus ) ( Linguistics ), Marcia Johnson ( Emeritus ), Christine Jolls ( Law School ) , Dan Kahan ( Law School ), Frank Keil ( Psychology, Linguistics ) , Joshua Knobe ( Philosophy ), Gregory McCarthy ( Psychology ), Nathan Novemsky ( School of Management, Psychology ), Kenneth Pugh ( School of Medicine ), Ian Quinn ( Music ), Holly Rushmeier ( Computer Science ), Laurie Santos ( Psychology ), Brian Scassellati ( Computer Science, Mechanical Engineering ), Brian Scholl ( Chair ) ( Psychology ), Sun-Joo Shin ( Philosophy ), Jason Stanley ( Philosophy ), Zoltán Szabó ( Philosophy ), Nick Turk-Browne ( Psychology ), Tom Tyler ( Law School ) , Julie Van Dyke ( Haskins Laboratories ), Fred Volkmar ( School of Medicine ), David Watts ( Anthropology ), Karen Wynn ( Emeritus ) ( Psychology ), Gideon Yaffe ( Law School ), Raffaella Zanuttini ( Linguistics ), Gal Zauberman ( School of Management ), Steven Zucker ( Computer Science, Biomedical Engineering )

Associate Professors  Philip Corlett ( School of Medicine ) , Jason Dana ( School of Management ) , Yarrow Dunham ( Psychology ), Hedy Kober ( School of Medicine ) , James McPartland ( Child Study Center ) , Maria Piñango ( Linguistics )

Assistant Professors Ryan Bennett ( Linguistics ) , Steve Chang ( Psychology ), Philip Corlett ( School of Medicine ), Julian Jara-Ettinger ( Psychology ), Julia Leonard ( Psychology ) ,  Samuel McDougle (Psychology), Al Powers ( School of Medicine ),   Robb Rutledge ( Psycholog y) , Marynel Vázquez ( Computer Science ), Ilker Yildirim ( Psychology )

Lecturer Daylian Cain ( School of Management )

See  visual roadmap  of the requirements.

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cognitive science college essay

Best Cognitive Science colleges in the U.S. 2024

Best cognitive science colleges in the u.s. for 2024.

cognitive science college essay

Arizona State University Campus Immersion offers 1 Cognitive Science degree programs. It's a very large, public, four-year university in a midsize city. In 2022, 7 Cognitive Science students graduated with students earning 7 Certificates.

cognitive science college essay

Henderson State University offers 1 Cognitive Science degree programs. It's a small, public, four-year university in a faraway town.

cognitive science college essay

California State University-Stanislaus offers 1 Cognitive Science degree programs. It's a large, public, four-year university in a small suburb. In 2022, 5 Cognitive Science students graduated with students earning 5 Bachelor's degrees.

cognitive science college essay

California State University-Fresno offers 1 Cognitive Science degree programs. It's a very large, public, four-year university in a large city. In 2022, 1 Cognitive Science students graduated with students earning 1 Bachelor's degree.

cognitive science college essay

University of California-Berkeley offers 1 Cognitive Science degree programs. It's a very large, public, four-year university in a midsize city. In 2022, 230 Cognitive Science students graduated with students earning 230 Bachelor's degrees.

cognitive science college essay

University of California-Davis offers 1 Cognitive Science degree programs. It's a very large, public, four-year university in a small suburb. In 2022, 188 Cognitive Science students graduated with students earning 188 Bachelor's degrees.

cognitive science college essay

University of California-Irvine offers 3 Cognitive Science degree programs. It's a very large, public, four-year university in a large city. In 2022, 20 Cognitive Science students graduated with students earning 15 Bachelor's degrees, 3 Doctoral degrees, and 2 Master's degrees.

cognitive science college essay

University of California-Los Angeles offers 1 Cognitive Science degree programs. It's a very large, public, four-year university in a large city. In 2022, 222 Cognitive Science students graduated with students earning 222 Bachelor's degrees.

cognitive science college essay

University of California-San Diego offers 3 Cognitive Science degree programs. It's a very large, public, four-year university in a large city. In 2022, 768 Cognitive Science students graduated with students earning 759 Bachelor's degrees, 6 Doctoral degrees, and 3 Master's degrees.

cognitive science college essay

University of California-Santa Cruz offers 2 Cognitive Science degree programs. It's a large, public, four-year university in a small city. In 2022, 129 Cognitive Science students graduated with students earning 129 Bachelor's degrees.

Find local colleges with Cognitive Science majors in the U.S.

List of all cognitive science colleges in the u.s..

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How to learn better

Adam Boxer

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Applying cognitive science principles in the science classroom could be the route to better teaching and learning

Illustration of a small man in a larger man's head sorting different coloured balls

Source: © Yenpitsu Nemoto/Ikon Images

Illustration of a small man in a larger man’s head sorting different coloured balls

In essence, cognitive science is the study of thought, learning and memory. It draws together neuroscience, anthropology and computational modelling to understand how the mind works: how it responds to stimuli, manages tasks, makes decisions and creates memories. It can help us discern how learning and recall happen, and how to improve them. Through its findings, cognitive science also challenges some common aspects of teaching practice, and suggests new paths to more effective education. Let’s look at some ways it can help your teaching.

How is cognitive science studied?

To build their predictions, cognitive scientists draw evidence from lab-based control trials just as you’d find in medical research, as well as finding verification in neuroscientific investigations and making projections through computational modelling. To see if these theories hold true, studies then move to real-world classrooms.

Is cognitive science ready for the classroom?

The idea of teaching in a way that lets students absorb information more efficiently is attractive, so it’s no surprise that cognitive science has quickly found a home in education, but is your own classroom ready for it? There are certainly warnings about its implementation. For instance, the Education Endowment Foundation (EEF)’s recent summary was positive overall, but found there was still cause for hesitation around some of cognitive science’s ideas.

To start with, some of the evidence that cognitive science provides is not yet robust enough and therefore its success is uncertain in practice. What’s more, there are issues around the replication of studies outside of the laboratory and especially attempts to replicate successes when scaling up to larger groups.

Poor implementation can also be dangerous. When individuals try to implement an idea without fully understanding it, at best it can be non-effective and at worst, harmful to learning; results can suffer, and teacher workloads increase.

What can we take from cognitive science?

When assessing new practices or ideas, it’s important to distinguish between positive and negative claims. The positive claims of cognitive science specify what you should do in the classroom, and negative claims state what you shouldn’t . With the nature of these studies, positive claims should only count as a progressive rule of thumb rather than definitive advice. For negative claims there is, to my mind, greater certainty, but these, too, should be taken in context.

Teaching tips from cognitive science

Here are some of the positive claims that cognitive science makes about efficient learning. Each negative claim specifies something to avoid in teaching, but offers a positive rule of thumb or ‘best guess, given the evidence’ about what we should do instead.

Discovery learning is not beneficial for novice students

When I trained as a teacher, I was fully convinced that students learned best by discovering things for themselves – that being taught something explicitly was less effective. Cognitive science categorically refutes this position. First, it says there is no privileged status for self-taught concepts versus taught concepts. Second, it tells us that when we are novices, our minds can become quickly overloaded with new information . So unstructured discovery learning, or inquiry-based learning, almost always features too much information for a novice to adequately process.

Try this instead: As the teacher, take the content your students need to learn and carefully break it apart , introducing it slowly, piece by piece, in order to not overload them.

A plenary is not proof of learning

Cognitive science casts doubt on the practice of mini plenaries as proof of learning. With some consideration, this makes perfect sense. When you go for a jog, you might do so to break your personal best. That’s a short-term goal. However, if you jog to get fit, it’s a long-term goal. Nobody would ever say you are now fit after the jog, because this can only be judged successfully over a longer period.

Learning is like getting fit, and cognitive scientists say it is something we can only judge over the long term. Do students remember information two weeks, two months or two years later? If not, learning has been unsuccessful. That means we can’t judge whether learning has happened in the short term; just as you wouldn’t claim to be at peak fitness after one jog. We can’t teach something or do a mini plenary and think learning has happened. Punctuating a lesson to check student understanding is beneficial, but it doesn’t prove they have learned. For that, you need to take a much longer-term approach, repeatedly quizzing students on old learning points and incorporating them into your assessments.

Try this instead: Use long-term approaches to assess if learning was successful.

Students don’t learn according to a particular style

Learning style theories maintain that different people learn in fundamentally different ways. Some people might learn best visually, others kinaesthetically and so on. Cognitive science shows that this is not the case , and the mechanism by which we all learn is roughly the same. Of course, there are differences between individuals, but these are generally a matter of degree: if one student knows more chemistry than another, they’ll be better able to learn even more chemistry. Crucially, though, this is not a qualitative difference – they are not fundamentally better at learning chemistry on some genetic or cognitive level.

Try this instead: Consider prior knowledge as the most important difference between your students when planning lessons.

Fractured teaching leads to poor learning

Here’s an example of a traditional approach to learning and assessment: teach A > revision lesson on A > exam on A > teach B > revision lesson on B > exam on B > repeat for CDE > year-end exam on ABCDE. Cognitive science has shown that this approach is not effective over the long term.

Cramming content immediately before an exam is effective in terms of passing that exam, but the memories vanish shortly after, leading to a stop-start model of learning and a lack of build-up over time. Instead, revisiting past material should be spaced out , so students can look at content repeatedly over many weeks and months.

Try this instead: Embed revisiting into your practice, ensuring that students are given regular opportunities to look back at past material.

Remember, context is king

Cognitive science has the potential to revolutionise our classrooms and help our students develop into brilliant chemists. But while its negative claims might be definitive, the suggestions for improvement are not cast-iron rules. They’re principles that should be embedded in your teaching. You are still the most important driver for your students’ success, because cognitive learning’s evidence base will never perfectly prescribe what you should do with year 9 on a rainy Thursday afternoon.

Adam Boxer

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cognitive science college essay

List of All U.S. Colleges with a Cognitive Science Major

Cognitive Science is an interdisciplinary study of the brain and its functions from multiple perspectives like psychology and neuroscience. If you’re interested in studying Cognitive Science, here’s a complete list of schools that offer this major.

Search for schools with a Cognitive Science major and see your chances of acceptance .

How to Use This List

This list of colleges is organized by state. To learn more about each college, including acceptance rates, costs, enrollment, and more, click on the name of the school.

How to Find Best-Fit Schools

Having a specific major is just one aspect of many when evaluating your fit with a potential college. Some other factors to consider are:

  • Academic rigor, resources, and support
  • School and class size
  • Cost and financial aid generosity
  • Your chances of acceptance
  • Flexibility in the curriculum

To find a school with a Cognitive Science major that also suits your other preferences, use our free chancing engine and school search tool . You’ll be able to filter for schools based on major and all the aforementioned preferences. We’ll also let you know how you stack up against other applicants and how to improve your profile.

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2024 Best Colleges with Cognitive Science Degrees in America

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1-25 of 60 results

Yale University

New Haven, CT •

  • • Rating 4.02 out of 5   1,043 reviews

Alum: What I love most about Yale is the world-class academics and incredible community of professors and students. It's hard to describe just how great it is to be among intellectually curious people who genuinely love learning. The college fosters a collaborative culture where the people you meet and the quality of the community you live in matters more than prestige of the university brand. The resources for student projects and activities are incredible, so there are endless opportunities for extracurriculars and outstanding social life. ... Read 1,043 reviews

  • grade  A+ Overall Niche Grade

Acceptance rate 5%

Net price $20,605

SAT range 1480-1580

#1 Best Colleges in America .

Blue checkmark.

NEW HAVEN, CT ,

1043 Niche users give it an average review of 4 stars.

Featured Review: Alum says What I love most about Yale is the world-class academics and incredible community of professors and students. It's hard to describe just how great it is to be among intellectually curious people who... .

Read 1043 reviews.

Overall Niche Grade : A+ ,

Acceptance Rate : 5% ,

Net Price : $20,605 ,

SAT Range : 1480-1580 ,

Stanford University

Stanford, CA •

  • • Rating 4.1 out of 5   1,275 reviews

Sophomore: My experience at Stanford University was incredibly enriching and transformative. The academic rigor challenged me to push my boundaries and think critically in diverse fields of study. The vibrant campus culture fostered deep connections with peers and faculty, sparking countless engaging discussions and collaborations. The resources and opportunities available, from research initiatives to extracurricular activities, were unparalleled, allowing me to pursue my interests and passions fully. However, I believe there's room for improvement in terms of diversity and inclusion efforts, ensuring that all students feel equally supported and represented within the Stanford community. Additionally, enhancing accessibility to resources for students from various socioeconomic backgrounds would further enrich the university experience for all. ... Read 1,275 reviews

Acceptance rate 4%

Net price $14,402

SAT range 1470-1570

#2 Best Colleges in America .

STANFORD, CA ,

1275 Niche users give it an average review of 4.1 stars.

Featured Review: Sophomore says My experience at Stanford University was incredibly enriching and transformative. The academic rigor challenged me to push my boundaries and think critically in diverse fields of study. The vibrant... .

Read 1275 reviews.

Acceptance Rate : 4% ,

Net Price : $14,402 ,

SAT Range : 1470-1570 ,

Massachusetts Institute of Technology

Cambridge, MA •

  • • Rating 4.16 out of 5   661 reviews

Freshman: There was rigorous schedule but overall they provided an amazing and conducive learning environment which contributed to the student overall’s success. They also provided students with internship programs and other opportunities to make them better prepared for life outside college. ... Read 661 reviews

Net price $30,958

SAT range 1510-1580

#3 Best Colleges in America .

CAMBRIDGE, MA ,

661 Niche users give it an average review of 4.2 stars.

Featured Review: Freshman says There was rigorous schedule but overall they provided an amazing and conducive learning environment which contributed to the student overall’s success. They also provided students with internship... .

Read 661 reviews.

Net Price : $30,958 ,

SAT Range : 1510-1580 ,

Carleton College

NORTHFIELD, MN

  • • Rating 3.87 out of 5   548

Haverford College

HAVERFORD, PA

  • • Rating 3.93 out of 5   268

University of Evansville

EVANSVILLE, IN

  • • Rating 3.68 out of 5   744

University of Pennsylvania

Philadelphia, PA •

  • • Rating 3.89 out of 5   1,351 reviews

Alum: Penn was a great school for me. I went to Wharton for undergrad, majoring in economics and concentrating in finance and social impact. I liked the comprehensiveness of the core curriculum, as well as the breadth of elective options and relative freedom to customize your degree. The professors were for the most part stellar, curriculum really well designed, and administration very supportive of students. The campus is lively and student clubs aplenty. In terms of what I'd like to see change, I'd like to see more opportunity within on campus recruiting for more than just consulting and investment banking industries. ... Read 1,351 reviews

Acceptance rate 6%

Net price $14,578

SAT range 1480-1570

#7 Best Colleges in America .

PHILADELPHIA, PA ,

1351 Niche users give it an average review of 3.9 stars.

Featured Review: Alum says Penn was a great school for me. I went to Wharton for undergrad, majoring in economics and concentrating in finance and social impact. I liked the comprehensiveness of the core curriculum, as well as... .

Read 1351 reviews.

Acceptance Rate : 6% ,

Net Price : $14,578 ,

SAT Range : 1480-1570 ,

Dartmouth College

Hanover, NH •

  • • Rating 3.87 out of 5   742 reviews

Freshman: Great school! Not super diverse, but it’s easy to find your people. Profs are for the most part amazing and super inspirational. Definitely less competitive than the other Ivies. I have truly had the best time here and gotten to come out of my shell. ... Read 742 reviews

Net price $24,078

SAT range 1440-1560

#8 Best Colleges in America .

HANOVER, NH ,

742 Niche users give it an average review of 3.9 stars.

Featured Review: Freshman says Great school! Not super diverse, but it’s easy to find your people. Profs are for the most part amazing and super inspirational. Definitely less competitive than the other Ivies. I have truly had the... .

Read 742 reviews.

Net Price : $24,078 ,

SAT Range : 1440-1560 ,

Rice University

Houston, TX •

  • • Rating 4.09 out of 5   1,114 reviews

Freshman: I genuinely love Rice University so much. The professors are very well-versed on what they're teaching, and are also incredibly passion. The student body is diverse, kind, and brilliant. The overall experience is amazing and I highly recommend it to anyone interested. ... Read 1,114 reviews

Acceptance rate 9%

Net price $18,521

SAT range 1490-1570

#9 Best Colleges in America .

HOUSTON, TX ,

1114 Niche users give it an average review of 4.1 stars.

Featured Review: Freshman says I genuinely love Rice University so much. The professors are very well-versed on what they're teaching, and are also incredibly passion. The student body is diverse, kind, and brilliant. The overall... .

Read 1114 reviews.

Acceptance Rate : 9% ,

Net Price : $18,521 ,

SAT Range : 1490-1570 ,

  • Will you get in? Understand your chances of getting accepted into any college in the country, and it's completely free

Brown University

Providence, RI •

  • • Rating 3.84 out of 5   1,079 reviews

Freshman: Attending Brown University was an enriching and transformative experience. I cherished the vibrant community that fostered intellectual curiosity and diversity. The open curriculum allowed me to explore a wide range of subjects, fostering interdisciplinary connections and personal growth. The faculty were not only experts in their fields but also approachable mentors who genuinely cared about students' academic and personal development. I appreciated the emphasis on critical thinking and the encouragement to question assumptions. However, I would suggest enhancing resources for mental health support and increasing accessibility to financial aid for students from underprivileged backgrounds. Overall, Brown provided a nurturing environment that empowered me to thrive academically and personally. ... Read 1,079 reviews

Net price $25,028

SAT range 1460-1570

#10 Best Colleges in America .

PROVIDENCE, RI ,

1079 Niche users give it an average review of 3.8 stars.

Featured Review: Freshman says Attending Brown University was an enriching and transformative experience. I cherished the vibrant community that fostered intellectual curiosity and diversity. The open curriculum allowed me to... .

Read 1079 reviews.

Net Price : $25,028 ,

SAT Range : 1460-1570 ,

Duke University

Durham, NC •

  • • Rating 3.93 out of 5   1,204 reviews

Other: My experience at Duke University was nothing short of exceptional. The university's commitment to academic excellence made it a truly enriching environment to be a part of Academic Rigor: Duke's rigorous academic programs challenged me to think critically and develop a deep understanding of my field of study. World-Class Faculty: The faculty members at Duke are experts in their respective fields, and their passion for teaching and research was evident in every class and interaction. Overall, my experience at Duke University was incredibly rewarding, and I am grateful for the opportunities it provided me to grow academically, personally, and professionally. ... Read 1,204 reviews

Net price $27,297

#13 Best Colleges in America .

DURHAM, NC ,

1204 Niche users give it an average review of 3.9 stars.

Featured Review: Other says My experience at Duke University was nothing short of exceptional. The university's commitment to academic excellence made it a truly enriching environment to be a part of Academic Rigor: Duke's rigorous academic programs challenged me to think critically and develop a deep understanding of my field of study. World-Class Faculty: The faculty members at Duke are experts in their respective fields, and their passion for teaching and research was evident in every class and interaction. Overall, my experience at Duke University was incredibly rewarding, and I am grateful for the opportunities it provided me to grow academically, personally, and professionally. .

Read 1204 reviews.

Net Price : $27,297 ,

Vanderbilt University

Nashville, TN •

  • • Rating 4.01 out of 5   1,343 reviews

Alum: Going to Vanderbilt was the best decision! As someone interested in health and the social sciences, I felt that their Anthropology and Medicine, Health & Society programs and faculty were fantastic. The class sizes were smaller than I expected, which allowed for wonderful discussions and opportunities to develop great relationships with my peers. The electives were so much fun too, and I was especially grateful to be able to take courses across the different colleges and schools - my courses at Peabody College and Blair School of Music were awesome. ... Read 1,343 reviews

Acceptance rate 7%

Net price $27,553

#14 Best Colleges in America .

NASHVILLE, TN ,

1343 Niche users give it an average review of 4 stars.

Featured Review: Alum says Going to Vanderbilt was the best decision! As someone interested in health and the social sciences, I felt that their Anthropology and Medicine, Health & Society programs and faculty were fantastic.... .

Read 1343 reviews.

Acceptance Rate : 7% ,

Net Price : $27,553 ,

Washington University in St. Louis

Saint Louis, MO •

  • • Rating 4.11 out of 5   1,568 reviews

Sophomore: The professors overall are engaged, available, and care about their students. Campus food services are not always the best (in terms of cafeteria food, the quality isn't bad. But there's not a ton of variety in the main dining halls, and restaurants close pretty early. Most of the food locations aren't open on weekends either.) People on campus have always been helpful and inviting. Classmates are supportive in terms of forming study groups and sharing notes if you need help. There are resources for students (similar to many other universities) like a writing center, academic advising, and peer mentoring. They advertise a ton of different student groups to get involved in, and there really is something for everyone, and the people are really inviting. But some of the groups can be really competitive! ... Read 1,568 reviews

Acceptance rate 13%

Net price $28,298

#16 Best Colleges in America .

SAINT LOUIS, MO ,

1568 Niche users give it an average review of 4.1 stars.

Featured Review: Sophomore says The professors overall are engaged, available, and care about their students. Campus food services are not always the best (in terms of cafeteria food, the quality isn't bad. But there's not a ton of... .

Read 1568 reviews.

Acceptance Rate : 13% ,

Net Price : $28,298 ,

Pomona College

Claremont, CA •

  • • Rating 4.23 out of 5   422 reviews

Alum: Having studied in a few places now, I can say that Pomona is utterly unique in the American higher-ed landscape. As an intimate liberal arts college situated in what is essentially a 6,000-person university campus, it gives you all of the perks of a top LAC (small classes, unparalleled mentorship, intimate campus life) while offering the research opportunities and intellectual community of an R1 (caveat: you won't study under stars like Noam Chomsky or Jennifer Doudna, but you won't find that outside a handful of schools, and those people rarely ever mentor undergrads). It's also in Southern California, which offers access to the LA culture scene, proximity to natural beauty, impeccable weather, and in my opinion, facilitates a culture that sheds some of the elitist baggage you see at East Coast schools. In short, Pomona functions like an all-inclusive resort where every single person is smart, ambitious, and intensely curious about the world. Being there was an immense privilege. ... Read 422 reviews

Net price $17,000

#18 Best Colleges in America .

CLAREMONT, CA ,

422 Niche users give it an average review of 4.2 stars.

Featured Review: Alum says Having studied in a few places now, I can say that Pomona is utterly unique in the American higher-ed landscape. As an intimate liberal arts college situated in what is essentially a 6,000-person... .

Read 422 reviews.

Net Price : $17,000 ,

University of California - Los Angeles

Los Angeles, CA •

  • • Rating 3.98 out of 5   5,561 reviews

Freshman: I found the university to be fantastic with the various opportunities offered. The class size, although huge with a graduating class of around 6500, offers a lot of people for making friends. But the class size makes enrolling in classes quite difficult, forcing students like myself to make backup plans for classes. Besides the overall feel of the Romanesque-Revival campus and the quality of education, the facilities are quite well-renovated for most buildings. I would like to see change in the quality of administration, being oftentimes unresponsive to the concerns of students, regardless of situation, for a well-trained staff adaptable to student concerns along with the increasing enrollment of the university. I would recommend this university to California residents who like to study in pre-professional fields, film and the arts, and nursing, liking the large class size with a great brand name in sports. ... Read 5,561 reviews

Acceptance rate 11%

Net price $13,393

SAT range —

#19 Best Colleges in America .

LOS ANGELES, CA ,

5561 Niche users give it an average review of 4 stars.

Featured Review: Freshman says I found the university to be fantastic with the various opportunities offered. The class size, although huge with a graduating class of around 6500, offers a lot of people for making friends. But the... .

Read 5561 reviews.

Acceptance Rate : 11% ,

Net Price : $13,393 ,

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Carnegie Mellon University

Pittsburgh, PA •

  • • Rating 3.71 out of 5   1,474 reviews

Alum: I adored CMU. The Professors are at the forefront of their field. The Students are driven and creative. At times, students are a bit too busy to be social or explore outside of school! CMU does a great job of offering free events and perks in the Pittsburgh area. ... Read 1,474 reviews

Acceptance rate 14%

Net price $37,450

SAT range 1480-1560

#20 Best Colleges in America .

PITTSBURGH, PA ,

1474 Niche users give it an average review of 3.7 stars.

Featured Review: Alum says I adored CMU. The Professors are at the forefront of their field. The Students are driven and creative. At times, students are a bit too busy to be social or explore outside of school! CMU does a... .

Read 1474 reviews.

Acceptance Rate : 14% ,

Net Price : $37,450 ,

SAT Range : 1480-1560 ,

University of Michigan - Ann Arbor

Ann Arbor, MI •

  • • Rating 3.95 out of 5   4,675 reviews

Junior: The University of Michigan really lives up to its name. There's an opportunity for everyone here, no matter how obscure or unique your interests are. I am a current film student and have had no difficulties feeling at home and finding clubs despite the large STEM influence. The school does the best to support each student with opportunities, and has phenomenal resources available for students that need aid. For example, they give free tuition for families who make below a particular bench-line, which helps make education more accessible! Overall, this school truly is great. The campus, town, events, resources, education level -- you name it, and it's great. I have truly felt so lucky to be here the past few years. I encourage everyone to apply, and GO BLUE! ... Read 4,675 reviews

Acceptance rate 20%

Net price $19,205

SAT range 1340-1520

#21 Best Colleges in America .

ANN ARBOR, MI ,

4675 Niche users give it an average review of 4 stars.

Featured Review: Junior says The University of Michigan really lives up to its name. There's an opportunity for everyone here, no matter how obscure or unique your interests are. I am a current film student and have had no... Overall, this school truly is great. The campus, town, events, resources, education level -- you name it, and it's great. I have truly felt so lucky to be here the past few years. I encourage... .

Read 4675 reviews.

Acceptance Rate : 20% ,

Net Price : $19,205 ,

SAT Range : 1340-1520 ,

Johns Hopkins University

Baltimore, MD •

  • • Rating 3.68 out of 5   1,404 reviews

Alum: Johns Hopkins has top-notch academics/faculty and a truly global reach. Hopkins was a great place to go to graduate school (although definitely very challenging at times due to grade deflation and accelerated program workloads). There are maybe 2-3 other schools (Harvard, UPenn, Stanford) in the world where you can get a world-class graduate degree (MS, MBA, PhD, etc.) while having the opportunity to cross-pollinate with classes at the world #1 Public Health/#1 Nursing School/#1 International Studies schools, as well as top 15 Engineering/Science/Education/A&S schools as well as an innovative and rapidly rising Business School. I know it's not everyone's thing, but I truly fell in love with Baltimore after living their for an extended period of time. I try to get back to Fells Point, Canton, Charles Village, Mount Vernon, and the East Baltimore JHMI campus (which has really come a long way in the last 10 years) at least once or twice every few years. ... Read 1,404 reviews

Acceptance rate 8%

Net price $20,680

SAT range 1470-1560

#24 Best Colleges in America .

BALTIMORE, MD ,

1404 Niche users give it an average review of 3.7 stars.

Featured Review: Alum says Johns Hopkins has top-notch academics/faculty and a truly global reach. Hopkins was a great place to go to graduate school (although definitely very challenging at times due to grade deflation and... .

Read 1404 reviews.

Acceptance Rate : 8% ,

Net Price : $20,680 ,

SAT Range : 1470-1560 ,

Claremont McKenna College

  • • Rating 3.95 out of 5   432 reviews

Sophomore: Transferred in from a flagship state school and have no regrets. Found more welcoming communities, interesting professional/research opportunities, and more engaging classes. Facilities and the overall area (being in between LA County and San Bernardino) are somewhat underwhelming but can be made more enjoyable with a car. Food and residential life is good, and you'll find most people are friendly across the 5Cs. More specific to CMC, students love having profound conversations and engaging in multiple productive pursuits. It's definitely a grind, but a fulfilling one -- especially if you're pre-professional interested in IB/Consulting. ... Read 432 reviews

Net price $21,663

SAT range 1420-1530

#25 Best Colleges in America .

432 Niche users give it an average review of 3.9 stars.

Featured Review: Sophomore says Transferred in from a flagship state school and have no regrets. Found more welcoming communities, interesting professional/research opportunities, and more engaging classes. Facilities and the... .

Read 432 reviews.

Net Price : $21,663 ,

SAT Range : 1420-1530 ,

University of Southern California

  • • Rating 3.98 out of 5   4,029 reviews

Freshman: I like the diverse, really friendly community, thoughtful curriculum, experienced teachers, and good campus life. I feel that the community is supportive and that I have many avenues to learn, change and grow here. The people are really nice! What I would like to see changed is a better work environment for the students and staff, more mediated spaces for discussions on difficult topics, and a better-organized structure for knowing what opportunities I have to contribute to the community or make the most of my educational experience. ... Read 4,029 reviews

Net price $26,021

SAT range 1410-1540

#26 Best Colleges in America .

4029 Niche users give it an average review of 4 stars.

Featured Review: Freshman says I like the diverse, really friendly community, thoughtful curriculum, experienced teachers, and good campus life. I feel that the community is supportive and that I have many avenues to learn, change... What I would like to see changed is a better work environment for the students and staff, more mediated spaces for discussions on difficult topics, and a better-organized structure for knowing what... .

Read 4029 reviews.

Net Price : $26,021 ,

SAT Range : 1410-1540 ,

Swarthmore College

Swarthmore, PA •

  • • Rating 3.63 out of 5   427 reviews

Alum: Absolutely transformative. The impact wasn't just due to the exceptional quality of professors and the favorable teacher-to-student ratio but also owed much to the remarkable student body. Immersing myself in an environment where everyone was achieving at a high level had a profound influence on me. The constant exposure to major achievers compelled me to elevate my standards significantly just to keep pace. This habit, cultivated during my time there, has remained a guiding force throughout my life. It laid the foundation for a fulfilling and successful journey in both life and work. Proud to be a Swattie! Gus Woltmann ... Read 427 reviews

Net price $19,733

SAT range 1430-1560

#33 Best Colleges in America .

SWARTHMORE, PA ,

427 Niche users give it an average review of 3.6 stars.

Featured Review: Alum says Absolutely transformative. The impact wasn't just due to the exceptional quality of professors and the favorable teacher-to-student ratio but also owed much to the remarkable student body. Immersing... .

Read 427 reviews.

Net Price : $19,733 ,

SAT Range : 1430-1560 ,

Tufts University

Medford, MA •

  • • Rating 3.7 out of 5   1,071 reviews

Alum: The student body and culture is my favorite thing. Students are smart and competitive, but also compassionate. There is a lot of support and resources for continued education and graduate schools. However, I would love to see more effort put into career services and connections for upcoming graduates deciding to go into the industry. ... Read 1,071 reviews

Net price $31,630

SAT range 1440-1550

#34 Best Colleges in America .

MEDFORD, MA ,

1071 Niche users give it an average review of 3.7 stars.

Featured Review: Alum says The student body and culture is my favorite thing. Students are smart and competitive, but also compassionate. There is a lot of support and resources for continued education and graduate schools.... .

Read 1071 reviews.

Net Price : $31,630 ,

SAT Range : 1440-1550 ,

New York University

New York, NY •

  • • Rating 3.75 out of 5   5,711 reviews

Senior: I love NYU! Throughout my college experience I transferred schools three time trying to find the right fit. I finally found NYU and fell in love with it. The professors are amazing. They all want to see you succeed and will help you in anyway they can. At previous universities that I have attended it always felt like the professors were trying to trick you, rather than be a resource to help you. NYU could not be more different than that. ... Read 5,711 reviews

Net price $50,991

SAT range 1450-1570

#45 Best Colleges in America .

NEW YORK, NY ,

5711 Niche users give it an average review of 3.8 stars.

Featured Review: Senior says I love NYU! Throughout my college experience I transferred schools three time trying to find the right fit. I finally found NYU and fell in love with it. The professors are amazing. They all want to... .

Read 5711 reviews.

Net Price : $50,991 ,

SAT Range : 1450-1570 ,

University of California - Berkeley

Berkeley, CA •

  • • Rating 3.71 out of 5   4,454 reviews

Freshman: I have loved my time at Berkeley. There is an atmosphere of inclusion and celebration of diversity. I have the most amazing professors who make me love learning! I am majoring in Molecular Environmental Biology with a minor in African American Studies. I am hoping to become a medical doctor and I know my time at Berkeley is pushing me to reach that goal. Being in a city is new to me, there is always something exciting happening and something to see. Being able to see the Berkeley Hills and the Golden Gate Bridge when I wake up is amazing. Not to mention that the people I have met here push me to be a better person and inspire me. There are many organizations to join, so far I have joined Berkeley Engineers And Mentors (BEAM), Cal Surf Rider, greek life and the women's club water polo team. There is something for everyone, and for every version of myself aspire to be. I am grateful to be learning and living at UC Berkeley ! GO BEARS! ... Read 4,454 reviews

Net price $17,652

SAT range 1310-1530

#47 Best Colleges in America .

BERKELEY, CA ,

4454 Niche users give it an average review of 3.7 stars.

Featured Review: Freshman says I have loved my time at Berkeley. There is an atmosphere of inclusion and celebration of diversity. I have the most amazing professors who make me love learning! I am majoring in Molecular... .

Read 4454 reviews.

Net Price : $17,652 ,

SAT Range : 1310-1530 ,

University of Georgia

Athens, GA •

  • • Rating 3.99 out of 5   5,760 reviews

Junior: As a freshman, I was unsure about going to such a large school. However, the University of Georgia can be your "oyster" if you make it so. There are so many activities geared towards different niches. Freshman specifically have a lot of events that are made for them to meet other freshman students! There are events for on-campus students and off-campus students, and there are clubs for every ideology, racial group, hobby, and niche. Class difficulty varies. In my experience, chemistry has been the most difficult gen. ed. course. The work load and content are equally challenging and the labs are incredibly long. However, there are plenty of resources to help students throughout all of their courses. TAs and teachers are usually willing to provide extra help via email or office hours. The school also uses Penji to provide free undergraduate tutoring. The University of Georgia is an academically challenging school that provides a combination of social engagement and rigorous courses. ... Read 5,760 reviews

Acceptance rate 40%

Net price $16,902

SAT range 1270-1450

#49 Best Colleges in America .

ATHENS, GA ,

5760 Niche users give it an average review of 4 stars.

Featured Review: Junior says As a freshman, I was unsure about going to such a large school. However, the University of Georgia can be your "oyster" if you make it so. There are so many activities geared towards different... Class difficulty varies. In my experience, chemistry has been the most difficult gen. ed. course. The work load and content are equally challenging and the labs are incredibly long. However, there... The University of Georgia is an academically challenging school that provides a combination of social engagement and rigorous courses. .

Read 5760 reviews.

Acceptance Rate : 40% ,

Net Price : $16,902 ,

SAT Range : 1270-1450 ,

University of Illinois Urbana-Champaign

Champaign, IL •

  • • Rating 3.79 out of 5   4,986 reviews

Freshman: The campus itself is huge and full of life, and it might take some time to get familiar with everything. But that's part of the adventure, right? The engineering and computer science programs here are especially well-regarded, with opportunities for hands-on learning and research. You'll find a mix of people from different backgrounds, which can be amazing for broadening perspectives and making lifelong friends. The diversity of thought and culture at UIUC is something many students appreciate. Balancing academics, social life, and personal time can be a challenge, but there's support available, whether it's through academic resources, student services, or simply connecting with professors and peers. Remember, your experience at UIUC will be unique to you. Embrace the journey, explore what interests you, and don't hesitate to seek out resources or advice whenever you need it. ... Read 4,986 reviews

Acceptance rate 60%

Net price $14,272

SAT range 1320-1510

#50 Best Colleges in America .

CHAMPAIGN, IL ,

4986 Niche users give it an average review of 3.8 stars.

Featured Review: Freshman says The campus itself is huge and full of life, and it might take some time to get familiar with everything. But that's part of the adventure, right? The engineering and computer science programs here... .

Read 4986 reviews.

Acceptance Rate : 60% ,

Net Price : $14,272 ,

SAT Range : 1320-1510 ,

University of California - Irvine

Irvine, CA •

  • • Rating 3.77 out of 5   4,258 reviews

Other: UC Irvine is a fantastic place for students because it's so diverse, meaning people from all over come here. This makes it cool because you get to meet lots of different folks and learn about their cultures. The atmosphere here is super nice, which means it's a really good environment to study and hang out with friends. Plus, the food is really tasty! There are lots of options to choose from, so you never get bored of eating the same thing. Overall, UC Irvine is just a really great place to be for students! ... Read 4,258 reviews

Acceptance rate 29%

Net price $11,633

SAT range 1230-1430

#57 Best Colleges in America .

IRVINE, CA ,

4258 Niche users give it an average review of 3.8 stars.

Featured Review: Other says UC Irvine is a fantastic place for students because it's so diverse, meaning people from all over come here. This makes it cool because you get to meet lots of different folks and learn about their... .

Read 4258 reviews.

Acceptance Rate : 29% ,

Net Price : $11,633 ,

SAT Range : 1230-1430 ,

University of California - San Diego

La Jolla, CA •

  • • Rating 3.63 out of 5   3,959 reviews

Freshman: Although I have not been a member of the UC San Diego community for long, I have definitely enjoyed it so far. As a commuter, I worried I wouldn’t be able to participate much in campus activities but luckily I was. The people at UCSD are extremely understanding of student schedules, making sure that a variety of pastimes are available at any time of day. This allowed me to participate in campus activities such as the “De-stress with Puppies” opportunity, which ended up really helping me out with finals week coming up and allowed me to gain new friends as the experience prompted conversation among those present. Not only are the recreations extremely versatile, but so is the campus food. There are an abundance of options to pick: from Mexican and Greek cuisine to Indian and Thai cuisine. As a Mexican-American student, finding a restaurant made me feel recognized, especially considering the fact that one barely sees authentic Mexican food in a non-Latinx majority community. ... Read 3,959 reviews

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Essays on Cognitive Science

Cognitive Science is the interdisciplinary study of mind and intelligence. It draws from many different fields including psychology, neuroscience, philosophy, linguistics, anthropology and computer science to understand how humans think and make decisions. Cognitive Science focuses on understanding how information is processed in the brain, how mental processes are represented in language and action, and how thinking shapes behavior.Cognitive scientists use a variety of methods to study human cognition such as experiments, surveys, interviews and computational modeling. They seek to answer questions about topics such as decision-making, problem solving and memory formation through the analysis of data gathered from these techniques. By investigating the cognitive processes underlying human behavior they can build better theories that explain why people act in certain ways. One important topic within Cognitive Science involves artificial intelligence (AI). AI is focused on building machines that have similar capabilities as humans – often referred to as ‘intelligent agents’ ” which can learn from their environment (e.g., a robotic car navigating its way around a city) or respond to spoken commands (e.g., Siri). In order for machines to perform intelligent tasks like this they must be programmed with knowledge about their environment or task domain; this programming needs to be based on an understanding of cognitive principles if it is going to work properly otherwise it will not produce meaningful results when confronted with new situations or unfamiliar problems. Researchers within Cognitive Science thus develop models of cognition that describe how humans perceive their world in terms of objects, categories and goals so that these algorithms can be applied appropriately by AI systems. Another key area in Cognitive Sciences studies individual differences between people ” e.g., age-related changes in learning abilities or differences due cultural background ” so that we can gain insight into why some individuals may find certain tasks easier than others while performing them at similar levels overall but with different strategies employed along the way (for example children might rely more heavily on visual cues while older adults might rely more heavily on verbal instructions). This kind of research helps us design interventions tailored specifically towards certain groups who might benefit most from them so they can reach their full potential regardless of any pre-existing conditions or environmental factors affecting them negatively right now.. Overall Cognitive Science contributes significantly towards our understanding of human thought processes by bridging together various disciplines to bring together theories about reasoning skills such as problem solving or decision making across contexts ranging from everyday life all the way up towards advanced Artificial Intelligence applications used for robotics or automated cars driving themselves around cities safely without accidents happening regularly due outdated algorithms being unaware what’s going on around them since last update.

What Is The Theme In Interpreter Of Maladies? Social and emotional maladjustment is definitely an overarching theme in Interpreter of Maladies. Shukumar and Shoba’s marriage is within trouble in “A Temporary Matter.” Mrs. Das is affected by guilt, and Mr. JhumpaLahiri’s “Interpreter of Maladies” concentrates on communication among the universal styles through the book. The […]

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Top Benefits of Cognitive Science

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Why You Must Choose Cognitive Science as the Main Subject?

1. cognitive science involves many disciplines, 2. cognitive science offers best career opportunities, 3. it offers many jobs and handsome salary, 4. interesting subject, 5. improves knowledge.

Cognitive science is all about the human mind and the way it processes things and analyzes as well as manipulates information. The study includes research on humans and animals. Some of the common subfields of cognitive science are as follows:

  • Education   – it involves the study of how humans learn
  • Artificial Intelligence   – AI is the study of machines and their ability to perform human-based tasks.
  • Philosophy   – It is the study of human existence, their intelligence, mental health, and reality
  • Linguistics : It involves the study of different languages
  • Psychology   – Psychology is the study of human’s mind as well as their interest and behavior
  • Anthropology   – It is the study of human’s existence, their history, culture, and evolution
  • Neuroscience   – it is the study of human’s nervous system

Cognitive science is a combination of the studies mentioned above. In simplest terms, cognitive science helps students to become familiar with their brain and the functioning of the human nervous system. But that's not it! As the course involves philosophy, languages, anthropology, neuroscience, artificial intelligence, etc; you get to learn different concepts. From the nervous system to human culture to their evolution to the study of languages; cognitive science is the ultimate solution for aspiring scientists.

The question is what makes cognitive science a better subject choice? Why you should choose cognitive science over other major subjects in college? What benefits does this course have to offer? Can you expect a good salary and annual income? What job opportunities are available for cognitive science students? Well, there are many questions when it comes to deciding the main subject. However, if your goal is to become a scientist and dedicate your life to research all while earning a six-figure salary; then cognitive science is definitely your pick. Let's find out more about this course.

The development of the cognitive abilities of a human is the result of the series of events that human's come across in their daily life. To ensure proper development of the person i.e. from an infant to an adult; these changes are essential. Are you searching for the reasons students should opt for cognitive science? Well, you have come to the right place then! In this article, we will show you the 5 important reasons why cognitive science is important and how can it benefit you in the long run. Let’s get started.

The best part about cognitive science is the involvement of several disciplines. As mentioned above, cognitive science is not limited to the study of the human brain. Of course, the human brain and its functioning is the major element of cognitive science. But apart from the brain, this subject includes a lot of other disciplines. That being said, you are highly likely to find a number of jobs in this field.

We all know how important artificial intelligence is in the current era. Nowadays, companies rely on AI to grow their business, increase sales, achieve customer satisfaction, give tough competition to the competitor companies, and take the organization to heights. Artificial Intelligence has made our life a whole lot easier and affordable. The tasks that consume most of our time are now carried out by machines. Now that cognitive science includes Artificial Intelligence, the demand for employees with an education background in this field is on the rise. The reason is simple: people who know about artificial intelligence and its functioning can prove an asset to the companies who want to survive in the competitive market.

We have already mentioned how cognitive science is a combination of several disciplines. From neurology to artificial intelligence to anthropology to psychology; this field has everything the aspiring scientists want to advance their careers. From working as a data analyst to the digital marketer to a researcher to a psychologist to a neurologist; cognitive science can be your ultimate course for a better career.

As it involves a wide variety of subjects, the cognitive science students can prepare themselves for working in different fields such as information systems, government, education, research, robotics, medical, and the list goes on.

Have you seen the online job opportunities for cognitive science students? Well, there are many career options for you – to name a few – staff scientists, cognitive modeling, machine learning scientists, research scientists, and much more. According to the sources, more than 13,000 jobs related to cognitive science are found on online job sites such as Indeed. The best part is that more than 5,000 job opportunities start at 6-figure salaries.

According to the research, the starting salary of the person who manages to secure a high-paying position in a popular company is around &70,000. The figure clearly suggests how this study can turn out profitable for your future.

Have you ever wondered how the human mind works? How it processes things? How it manipulates the information it receives? How you get hallucinations? How does your central nervous system work? How people adapt themselves to different cultures? How people in the ancient era survived illnesses and medical disorders?

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If you are curious to know the functioning of the human brain and body, then cognitive science can be a super interesting subject. Believe it or not, this course has everything that will keep up your interest and tempt you to explore more. The course involves never-ending topics for research and learning.

Cognitive science is all about the human brain. Sure, it involves theories and research; but it offers an amazing opportunity for students to improve their knowledge. From linguistic to philosophy to the life of the ancestors to human evolution; you will surely become a professional by choosing cognitive science as a subject.

These were the top benefits of studying cognitive science in your college. So, why wait? Pick cognitive science and master this course. Good Luck!

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Dr Mary Gobl, a first year specialising in surgery, attempts a simulated appendectomy at the Surgical Innovation Centre at St Mary's hospital in Paddington, west London

Researchers study brain activity of surgeons for signs of cognitive overload

Team at Imperial College London say techniques could be used to flag warning signs during surgery

It is a high-stakes scenario for any surgeon: a 65-year-old male patient with a high BMI and a heart condition is undergoing emergency surgery for a perforated appendix.

An internal bleed has been detected, an anaesthetics monitor is malfunctioning and various bleepers are sounding – before an urgent call comes in about an ectopic pregnancy on another ward.

This kind of drama routinely plays out in operating theatres, but in this case trainee surgeon Mary Goble is being put through her paces by a team of researchers at Imperial College London who are studying what goes on inside the brains of surgeons as they perform life-or-death procedures.

Goble looks cool and collected as she laparoscopically excises the silicon appendix, while fending off a barrage of distractions. But her brain activity, monitored through a cap covered in optical probes, may tell a different story.

The researchers, led by Daniel Leff, a senior researcher and consultant breast surgeon at Imperial College healthcare NHS Trust, are working to detect telltale signs of cognitive overload based on brain activity. In future, they say, this could help flag warning signs during surgery.

“The operating theatre can be a very chaotic environment and, as a surgeon, you have to keep your head and stay calm when everyone is losing theirs,” said Leff. “As the cognitive load increases, it has major implications for patient safety. There’s no tool we can use to know that surgeon is coping with the cognitive demands of that environment. What happens when the surgeon is maxed out?”

In the future, Leff envisages a system that could read out brain activity in real-time in the operating theatre and trigger an intervention if a surgeon is at risk of overload.

“If you really like listening to Whitney Houston, you could automatically play calming music. Or it might alert the lead theatre nurse so that she manages the inevitable nonsense that happens in a theatre room,” said Leff. “It’s like Minority Report for surgery.”

More controversially, it might also be possible to use brain stimulation to augment a surgeon’s performance if they were losing concentration.

The cap worn by Goble uses functional near-infrared spectroscopy (fNIRS), a noninvasive technique to measure changes in blood oxygenation in the brain – a proxy for the underlying neural activity. Previously the team has shown that novices had greater pre-frontal brain activity than experienced doctors when performing surgery. They also found pre-frontal activity appeared to be disrupted more easily in doctors whose performance dipped during stressful situations .

The latest work is attempting to map out the fNIRS signatures of cognitive overload, when a doctor’s performance begins to dip because they can no longer cope with the influx of information and demands being placed on them. The study, using trainee surgeons, will track brain activity and surgical performance as progressively more demands are introduced. The simulated environment means that every movement of the laparoscopic instruments can be traced, and copper wires embedded in the silicon appendix detect if incisions are on target.

“You often don’t really see any external signs from people,” said Leff, adding that doctors stereotypically have a “don’t hesitate to cope” mentality.

After the run-through, Dr Goble, a surgical trainee at Kings College NHS trust and study participant, said her stress levels were soaring even though it was a simulation. “Surgery is a stressful environment,” she said. “On a night shift, when you’re by yourself and you have to deal with competing clinical priorities, it’s really easy to get really overwhelmed. I work on my breathing as a sort of concentration method.”

Simulated surgery is increasingly used in teaching at medical schools and so this kind of monitoring could be integrated into training to identify trainees who need more support, and to track progress, according to Leff. Future patient safety policies could also be informed by better evidence on how operating theatre environments affect performance, in a similar way to how findings on fatigue led to new rules on safe working patterns for doctors.

“I think if this is framed in a way that is about helping people become the best doctors they can be and that it’s about patient safety, the acceptance is greater,” said Leff. “The moment you try to use these things to say that someone is or isn’t capable, you start to run into problems.”

It is not yet possible to read out brain activity in real-time while surgeons operate – and this application is likely to be more than a decade away. But rapid advances are under way in brain-computer-interface technologies, including non-invasive helmets designed to measure brain activity in healthy individuals.

The Imperial team is also investigating the possibility of using a non-invasive technique called transcranial direct current stimulation (tDCS) to enhance performance. It involves a weak electrical current being passed between two sponge electrodes placed on the scalp – just enough to feel a slight tingling. Previously, they found that trainee surgeons learning to suture laparoscopically improved more quickly and reached a higher level of performance if they received tDBS while practising. Experienced surgeons did not see the same gains, however.

“When it comes to neuro-augmentation that’s certainly more challenging ground and people become more sceptical,” said Leff. “It’s an area that’s going to struggle to garner much support as you are talking about sending signals to someone’s brain. fNIRS is harmless monitoring of what’s happening and we have seen that’s way more acceptable to people.”

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Report Helps Answer the Question: Is a College Degree Worth the Cost?

The analysis found that former students at most colleges had an annual income higher than high school graduates a decade after enrollment.

A diploma being swiped through a green device with a clock on it.

By Ann Carrns

Most people go to college to improve their financial prospects, though there are other benefits to attending a postsecondary institution. But as the average cost of a four-year degree has risen to six figures, even at public universities, it can be hard to know if the money is well spent .

A new analysis by HEA Group, a research and consulting firm focused on college access and success, may help answer the question for students and their families. The study compares the median earnings of former college students, 10 years after they enrolled, with basic income benchmarks.

The analysis found that a majority of colleges exceed minimum economic measures for their graduates, like having a typical annual income that is more than that of a high school graduate with no higher education ($32,000, per federal Scorecard data ).

Still, more than 1,000 schools fell short of that threshold, though many of them were for-profit colleges concentrating in short-term credentials rather than traditional four-year degrees.

Seeing whether a college’s former students are earning “reasonable” incomes, said Michael Itzkowitz, HEA Group’s founder and president, can help people weigh whether they want to cross some institutions off their list. Someone deciding between similar colleges, for example, can see the institution that has produced students with significantly higher incomes.

While income isn’t necessarily the only criterion to consider when comparing schools, Mr. Itzkowitz said, “it’s a very good starting point.”

The report used data from the Education Department’s College Scorecard to assess the earnings of about five million former students who had attended about 3,900 institutions of higher education, 10 years after they first enrolled. (The analysis includes data for people who didn’t complete their degree.) The report includes public colleges as well as private nonprofit and for-profit schools; the schools may offer nondegree certificates, associate degrees and bachelor’s degrees.

The analysis found that schools where students earned less than their peers who never attended college were generally those offering nondegree certificates, which can often be completed in 18 months or less, as well as for-profit institutions, although the list also includes some public and private nonprofit schools. At 71 percent of for-profit schools, a majority of students were earning less than high school graduates 10 years after enrolling, compared with 14 percent of public institutions and 9 percent of private nonprofit schools, Mr. Itzkowitz said.

“College is, indeed, worth it,” Mr. Itzkowitz said, but paying for it can be “substantially riskier” depending on the type of school you attend or the credential you seek.

(Another report found that former students of for-profit colleges tend to experience more financial risk than those who attended similarly selective public colleges. Those risks include having to take on more debt for higher education, a greater likelihood of defaulting on student loans and a lower likelihood of finding a job.)

Jason Altmire, president and chief executive of Career Education Colleges and Universities, a trade group representing for-profit career colleges, said lumping together schools offering mainly short-term certificate programs with colleges offering four-year degrees didn’t make sense. People who want to work in certain careers — hairdressing, for instance — generally can’t work in the field unless they earn a certificate, he said.

Mr. Altmire also said that income data from for-profit certificate schools might be skewed by “gender bias” because the programs had a higher proportion of women, who were more likely than men to work part time while raising families, lowering a school’s reported median income.

The HEA report also compared colleges’ performance with other benchmarks, like the federal poverty line ($15,000 annual income for an individual), which is used to determine eligibility for benefits for government programs like subsidized health insurance and Medicaid. Incomes at the “vast majority” of colleges exceeded this cutoff, the report found, although 18 — nearly all of them for-profit schools offering nondegree certificate programs in beauty or hairstyling — had students with median incomes below that threshold.

Majors also matter, since those in science, technology, engineering and nursing typically lead to significantly higher salaries than majors in the arts or humanities. (Last year, HEA published a separate analysis of the college majors that pay the most.)

When comparing the earnings after college, students and families shouldn’t look at the data in a vacuum, said Kristina Dooley, a certified educational planner in Hudson, Ohio. Many schools where former students go on to be top earners have programs focusing on health sciences, technology or business, but that may not be what you want to study.

“Use it as one piece of information,” Ms. Dooley said.

She said that students shouldn’t rule out a college just because it wasn’t at the pinnacle of the income list. Do ask questions, though — like whether its career services office helps with setting up internships and making alumni connections to assist you in finding a good-paying job.

Amy S. Jasper, an independent educational consultant in Richmond, Va., said postgraduate income might matter more to students and families who had to get a loan for college. “How much debt do they want to incur?” she said. “That is something that needs to be taken into consideration.”

But, she said, the benefits of college are not just financial. “I’d like to think that picking the right school is also about becoming a better person and contributing to the world.”

Here are some questions and answers about college costs:

What colleges had the highest median incomes?

Marquee names, like most Ivy League schools, Stanford and the Massachusetts Institute of Technology, are heavily represented at the top of HEA’s analysis. Their students had median incomes of at least $90,000 a decade after enrollment. (A handful of for-profit schools, focused on careers like nursing and digital production, can be found there as well.) But the highest-earning colleges on the list? Samuel Merritt University, a nursing and health sciences school in Oakland, Calif., and the University of Health Sciences and Pharmacy in St. Louis, each with incomes above $129,000. You can see the data on the HEA website .

How much does college cost?

The average estimated “sticker” price for college — the published cost for tuition, fees, housing, meals, books and supplies, transportation and personal items — ranges from about $19,000 a year at a two-year community college to about $28,000 for in-state students at a public four-year university to almost $58,000 at a four-year private college, according to 2022-23 data from the College Board . Some students, however, may pay much less because of financial aid.

Are some college programs required to meet income benchmarks?

A federal “gainful employment” rule , which aims to make career programs more accountable, is scheduled to take effect in July. The new rule, which mostly affects for-profit schools but also applies to certificate programs at all types of colleges, requires schools to show that at least half of their graduates earn more than a typical high school graduate in their state and that their graduates have affordable student loan payments. Colleges that miss either benchmark must alert students that the school could lose access to federal financial aid. Schools that fail the same standard twice in three years will become ineligible for federal aid programs.

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COMMENTS

  1. Introduction to Majoring in Cognitive Science

    Degree Requirements. Each college or university will have different degree requirements for their cognitive science major. Since the field of cognitive science spans many disciplines, degree candidates are often required to take at least one course in each of the following areas: Psychology. Computer Science. Language and linguistics. Philosophy.

  2. Why I Majored in Cognitive Science

    Like most people at Pomona, I chose a liberal arts college because I wanted to explore as many different areas of study as possible. Cognitive science allows me to do that as my major! I love how diverse my schedule is—I study linguistics, anthropology, sociology, neuroscience and psychology all in one week! The LGCS Department genuinely ...

  3. Researching Your Cognitive Science Essays

    Find the books, articles and other sources you need to write an excellent essay on your topic in Cognitive Science. Online Handbooks & Encyclopedias Start your research with a handbook or encyclopedia to get an overview of a topic.

  4. Cognitive Science Major: Your Ultimate Guide to Unraveling the

    Here are some key skills you can expect to develop during your cognitive science studies: Critical thinking and problem-solving: Cognitive science majors learn to analyze complex problems, evaluate evidence, and develop logical arguments.This skill set is applicable across numerous disciplines and careers, making you a versatile and valuable asset in the workforce.

  5. To Help Struggling College Students, Look To Cognitive Science

    A survey of 1,000 undergraduates found that 87% said a professor has made at least one of their classes too difficult. Two-thirds said the professor should have been forced to make the class ...

  6. Senior Essays

    Senior Essays. This page lists all of the senior projects from previous cognitive science majors, organized by year. If a project title is blue, you may click on it to download a PDF of it. For current majors, you can find a guide to research and the senior thesis at this link.

  7. Welcome to the Cognitive Science program at University College

    An Expanding Field. The Cognitive Science program is fast-growing -- enrolment is up 76 per cent since 2009! We strive to enhance scholarship and travel opportunities for students and to foster outreach programming such as our biennial undergraduate conference, "Interdisciplinary Symposium on the Mind.".

  8. Cognitive Science

    Cognitive Science. Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations ...

  9. What we don't yet know about cognitive science in the classroom

    The most serious problem is that the role of teachers and teacher professional development has not been widely studied. With only a small number of exceptions, cognitive science studies have sought to scientifically control or minimise the influence of teachers, other students, and curriculum content. This is done through use of standardised ...

  10. Fostering Metacognition to Support Student Learning and Performance

    In fact, as of today, the most highly cited paper in CBE—Life Sciences Education is an essay on "Promoting Student Metacognition" ... College science courses provide opportunities for developing evaluation skills because students use metacognition when they find learning ... European Journal of Cognitive Psychology, 19(4-5), 559-579 ...

  11. What happened to cognitive science?

    Main. The cognitive revolution of the 1950s and 1960s brought the exciting prospect of investigating "the mind" scientifically, in a well-integrated interdisciplinary field with a coherent ...

  12. Cognitive Science < Columbia College

    326 Milbank Hall 212-854-4689. Barnard Director: Professor John Morrison, [email protected] Columbia Director: Professor Mariusz S. Kozak, [email protected]. Department Assistant: Maia Bernstein, [email protected]. Cognitive Science is the cross-disciplinary study of how the mind works, with a focus on perception, reasoning, memory ...

  13. Cognitive science Essays

    1467 Words | 6 Pages. gnitivism (brain science) - Wikipedia In brain science, cognitivism is a hypothetical system for understanding the mind that picked up assurance in the 1950s. The development was a reaction to behaviorism, which cognitivists said fail to clarify perception. Psychological brain research got its name from the Latin ...

  14. Yale CogSci FAQs

    Cognitive science is an interdisciplinary field devoted to exploring the nature of cognitive processes such as perception, reasoning, memory, attention, language, imagery, motor control, and problem-solving. The goal of cognitive science is to understand (1) the representations and processes in our minds that underwrite these capacities, (2 ...

  15. 177 College Essay Examples for 11 Schools + Expert Analysis

    Technique #1: humor. Notice Renner's gentle and relaxed humor that lightly mocks their younger self's grand ambitions (this is different from the more sarcastic kind of humor used by Stephen in the first essay—you could never mistake one writer for the other). My first dream job was to be a pickle truck driver.

  16. Cognitive Science < Yale University

    The goal of cognitive science, stated simply, is to understand how the mind works. Cognitive science is an inherently interdisciplinary endeavor, drawing on tools and ideas from traditional academic fields such as psychology, computer science, linguistics, philosophy, and neuroscience.

  17. Best Cognitive Science Degree Colleges in the U.S.

    5 College Essay Tips How to Determine Your Chances Of Getting Into A College 8 College Prep Tools for Freshmen Types of Degrees and ... public, four-year university in a large city. In 2022, 768 Cognitive Science students graduated with students earning 759 Bachelor's degrees, 6 Doctoral degrees, and 3 Master's degrees. 3.6944 Based on 9 Reviews.

  18. Is a Cognitive Science Major Worth It? : r/ApplyingToCollege

    r/ApplyingToCollege is the premier forum for college admissions questions, advice, and discussions, from college essays and scholarships to SAT/ACT test prep, career guidance, and more. ... Cognitive science does not have a correspondingly obvious "job" at the undergraduate level. This is true of almost all majors - college, especially in the ...

  19. Improve learning with cognitive science

    Applying cognitive science principles in the science classroom could be the route to better teaching and learning. In essence, cognitive science is the study of thought, learning and memory. It draws together neuroscience, anthropology and computational modelling to understand how the mind works: how it responds to stimuli, manages tasks, makes ...

  20. List of All U.S. Colleges with a Cognitive Science Major

    Cognitive Science is an interdisciplinary study of the brain and its functions from multiple perspectives like psychology and neuroscience. If you're interested in studying Cognitive Science, here's a complete list of schools that offer this major. Search for schools with a Cognitive Science major and see your chances of acceptance.

  21. 2024 Best Colleges with Cognitive Science Degrees

    Acceptance rate 4%. Net price $14,402. SAT range 1470-1570. My experience at Stanford University was incredibly enriching and transformative. The academic rigor challenged me to push my boundaries and think critically in diverse fields of study.

  22. Cognitive Science Essay Samples

    Essays on Cognitive Science 🎓Use these essay samples and get inspiration for writing your own paper!📕 ... Cognitive Science focuses on understanding how information is processed in the brain, how mental processes are represented in language and action, and how thinking shapes behavior.Cognitive scientists use a variety of methods to study ...

  23. Top Benefits of Cognitive Science

    3. It Offers Many Jobs and Handsome Salary. 4. Interesting Subject. 5. Improves Knowledge. Cognitive science is all about the human mind and the way it processes things and analyzes as well as manipulates information. The study includes research on humans and animals. Some of the common subfields of cognitive science are as follows:

  24. Researchers study brain activity of surgeons for signs of cognitive

    Team at Imperial College London say techniques could be used to flag warning signs during surgery It is a high-stakes scenario for any surgeon: a 65-year-old male patient with a high BMI and a ...

  25. Report Helps Answer the Question: Is a College Degree Worth the Cost

    The average estimated "sticker" price for college — the published cost for tuition, fees, housing, meals, books and supplies, transportation and personal items — ranges from about $19,000 ...