Cognitive Science is an exciting, fast-growing, and revolutionary area of study which seeks to develop integrated explanations of mind, brain, and behavior. Drawing on concepts and methods from a range of related fields — including linguistics, psychology, philosophy, neuroscience, and computer science — Cognitive Science students acquire a truly interdisciplinary knowledge base and a multifaceted set of analytic skills.
About the Field of Cognitive Science
Given that the term “cognitive science” is relatively new and susceptible to distinct interpretations – some of which are quite narrow – it will be useful to specify what cognitive science does and does not mean.
We start by first setting aside some of the more narrow interpretations that do not form the basis of the major, because they serve as useful contrasts and points of departure for what we are. Contemporary cognitive science has its roots in the cognitive revolution of the 1950s and 60s, including the growth of computer science and artificial intelligence, and the accompanying articulation and exploration of the idea that thought is a kind of computation physically realized by the brain 1. This idea remains central to the brain and behavioral sciences, and its import for scientific practice continues to grow. It was the theoretical and methodological role of computation that most clearly marked cognitive science as an interdisciplinary venture at the outset, and distinguished it most sharply from much older traditions in psychology. However, as a result, for many scientists “cognitive science” came to represent something quite narrow: a commitment to computation and only computation, to the exclusion of explanations at other levels of abstraction – for example, at neural/biological or social levels. An early focus on “cold cognition” to the exclusion of putatively non-cognitive phenomena such as emotion also led to a perception of cognitive science as narrowly circumscribed. Some textbook treatments of cognitive science still implicitly endorse this approach by introducing the field as a catalog of types of computational representations. Subsequent scientific advances – especially in the last 10–15 years – strongly indicate that a more illuminating, useful and accurate characterization of cognitive science as it has matured is one that is radically broader in its conception, even while still embracing a foundational theoretical role for computation. This broader conception serves as the basis for the proposed major, and can be summarized as follows:
Contemporary cognitive science is the scientific study of mind, brain, and behavior that: (a) seeks to develop integrated explanations of the structure of brain and behavior at multiple theoretical levels of abstraction, ranging from neuro‐biological levels to abstract computational levels to evolutionary levels grounded in assumptions about adaptive pressures 2; (b) sharpens fundamental questions that arise within theoretical levels and within specific traditional disciplines by articulating them in ways that allow them to be addressed productively across theoretical levels or via different disciplinary approaches; and (c) draws on whatever methodological and theoretical resources can be exploited to address the scientific problems at hand – that is, cognitive science does not respect disciplinary (or institutional/departmental) boundaries.
Two examples of specific domains that exemplify these characteristics will serve to ground these abstract points. These domains also form the core of two of the proposed tracks in the major, to be described below, and each is a locus of significant recent progress in the field.
Example 1: The Nature of Choice
Understanding the nature of human choice has long been a core problem in psychology and economics (and of course philosophy), but over the last 10–15 years it has enjoyed particularly rapid theoretical and empirical advances. Crucially, these advances take the form of integration of levels of explanation. For example, the new field of neuro-economics is largely based on recent discoveries concerning the neural basis of the component quantities underlying the computation of expected value in classical normative approaches (such as value and probability). In psychology, mathematical and computational approaches to statistical decision theory – especially the optimal sequential integration of evidence – have been successfully reformulated as precise models of the information processes underlying rapid decisions (those taking approximately less than a second – for example, the choice of when and where to move the eyes next when searching for a visual target among a clutter of distractors). These models have in turn led to new understanding of patterns of neuronal firings in humans and other mammals making such decisions. Similarly, algorithms developed in computational reinforcement learning (a branch of artificial intelligence concerned with designing autonomous agents that learn how to act effectively in their environments) have now been mapped onto underlying cognitive and neural systems in ways that have revolutionized our understanding of how the mind and brain learns to control thought and action. In short, contemporary approaches to choice now integrate theoretical levels that range from formal descriptions of what is to be computed (e.g., grounded in expected utility theory and optimal control theory) to information processing accounts of how it is to be computed (e.g., grounded in reinforcement learning theory). And these “how” accounts are themselves specified at various levels ranging across abstract algorithms, brain-circuit systems, and coding schemes of neuronal firing rates. These advances would not be possible without the contributions of traditionally separate disciplines, including psychology, economics, and computer science.
Example 2: The Nature of Language
One of the major catalysts of the cognitive revolution in the 1950s was Chomsky’s (re)formulation of the scientific study of human language as the study of an internal biologically based cognitive system, rather than the systematization and description of external linguistic artifacts (e.g. sets of speech sounds or “sentences”) and their apparent patterns and regularities. The key idea in this reformulation was (and continues to be) the positing of a mental computational capacity for the generation and interpretation of an infinite set of linguistic expressions. In the linguistic sciences, theoretical accounts of this capacity – and crucially, its limits – take the form of formal grammars. One of the theoretical roles of such grammars in the cognitive science of language is analogous to the role of statistical decision theory in contemporary accounts of choice: the grammars constitute a specification of what must be known and computed by the organism for it to be able to engage in the (behavioral) processes of language generation and comprehension, whether speech, reading, or sign language. In the last 10–15 years, a new field of computational psycholinguistics has emerged that provides integrated accounts relating together the formal theories of human linguistic capacity with models of computational processing informed by recent advances in mathematical and cognitive psychology, and machine learning. These integrated theories are in turn reformulating many of the leading edge questions in the brain basis of language – for example, motivating the cognitive neuroscience of language to move beyond questions concerning “where syntax resides in the brain” to questions concerning how brain circuits might implement the dynamic processes posited in the independently motivated computational theories. And these integrations open the door to new perspectives on the even deeper “why” questions that continue to be raised in theoretical linguistics.
- The University of Michigan played a key role in this revolution: major contributions with lasting influence include signal detection theory, which remains a foundation of modern perception research (Tanner & Swets, 1955); computational approaches to adaptation (Holland, 1975); reaction time approaches to human cognition and performance (Edwards, 1965; Meyer & Shvanaveldt, 1973); mathematical approaches to motor control (Fitts, 1967); and mathematical approaches to human decision making (Coombs, Dawes & Tversky, 1970).
- Thus the use of the terms “mind and brain” in cognitive science is not a regression to Cartesian dualism but rather reflects a commitment to multiple levels of theoretical explanation.
About the Cognitive Science Major
Cognitive Science is an interdepartmental major in the College of Literature, Science, and the Arts, jointly administered by the Departments of Linguistics, Philosophy, and Psychology, and supervised by the Weinberg Institute for Cognitive Science Executive Committee.
As a Cognitive Science student at Michigan, you’ll examine the mind and brain from many perspectives, exploring how cognition is realized in humans, other biological systems, and even in machines. You’ll select one of four tracks of study (see below) — computation, decision, language, and philosophy— featuring an array of top-ranked programs, have access to world-class facilities, and learn from renowned faculty across the University.
The Cognitive Science major is intended for students interested in a natural or social science degree in the behavioral and brain sciences with a combined focus and breadth not accommodated by a major within any single department. The major is inherently multidisciplinary yet still affords students the opportunity to focus on their particular interests. The major includes a gateway course (COGSCI 200) among other core courses, a sequence of required courses in one of four parallel tracks of study, and electives selected from a list of approved courses.
About the Cognitive Science Tracks of Study
The cognitive science curriculum balances two, potentially conflicting considerations. First, cognitive science is inherently interdisciplinary. Second, an optimal learning experience brings expertise in a sufficiently focused area in which students engage with key questions, and gradually deepen their knowledge and understanding. Our approach to balancing these competing demands is the structure of the major divided into four tracks, each representing a major area of research within contemporary cognitive science. The tracks provide focus and cohesion in the four content areas, while simultaneously fostering interdisciplinary inquiry through multiple levels of analysis. Thus, each track consists of a coherent, integrated and focused program of coursework that concomitantly integrates perspectives from multiple disciplines.
See the course list for each track.
Computation and Cognition
A foundational idea of cognitive science is that the mind is a kind of computer, and computation remains central to (but not the exclusive domain of) the field. The Computation and Cognition track is modeled broadly on Stanford’s well‐regarded "Symbolic Systems" major. This track requires students to take coursework in psychology (PSYCH 240 or 345) and computer programming (EECS 281, 492, or EECS 445) and the prerequisite courses EECS 203 and 280). Subsequent depth courses emphasize – although not exclusively so—computational and formal methods including computational linguistics (LING 441), rational choice theory (PHIL 443), and mathematical psychology (PSYCH 448).
Decision & Cognition
The study of decision and choice is a lively area of contemporary cognitive science inquiry. The University of Michigan has for decades made seminal contributions to decision research, and there continues to be a large and productive community of decision researchers on campus, but housed across multiple departments. No existing degree program brings together offerings from different departments into a coherent curriculum. The required courses in the Decision and Cognition track give students an introduction to historically influential approaches to decision-making drawn from three major fields: psychology (PSYCH 449), philosophy (PHIL 443 and 444), and philosophy (PHIL 361). Students then have the opportunity to take coursework in a number of disciplines that approach decision-making from diverse but complementary theoretical perspectives. These include neurobiology/neuroscience (e.g., PSYCH 345), ethology/animal cognition (PSYCH 335), and political science (POLSCI 391, POLSCI 490).
Language & Cognition
Because human language is universal in the species and grounded in human cognition and biology, linguistic inquiry was an integral component of the cognitive science revolution. Contemporary approaches to language synthesize models and findings from multiple disciplines, and the proposed curriculum is correspondingly interdisciplinary. The Language and Cognition track gives students a solid theoretical introduction to language through required coursework in linguistics (in sound patterns, syntax, or semantics; LING 313, 315, or 316), and in the philosophy (PHIL 345 or PHIL 409 or PHIL 426/LING 426), and psychology (PSYCH 349/LING 347 or LING 209/PSYCH 242/COGSCI 209). Further coursework broadens the investigation of language to include topics in computational linguistics and computer science (e.g., EECS 595/LING 541/SI 561), and linguistics (LING 441 and 442), and language development and learning (PSYCH 344 and 352, LING 342 and 440).
Philosophy & Cognition
There is extensive interaction between contemporary philosophy, especially philosophy of mind and ethics, and cognitive science. Philosophers have long posed fundamental questions about the nature of mind, the relationship between the mental and physical, and the nature of human agency. Cognitive science provides a rich and ever expanding body of theory, models, and findings that are relevant to these timeless philosophical questions. The Philosophy and Cognition track requires coursework in core philosophical (PHIL 482 or PHIL 340), formal (PHIL 303 or PHIL 305), and cognitive (PSYCH 240 and 345) approaches to mind. More in-depth coursework allows students to deepen their understanding of the philosophical problems and analytical enigmas raised by language and other symbolic systems (e.g., LING 316; PHIL 345 and 414, PSYCH 445/LING 447), inference and reasoning (PHIL 303, PSYCH 348), and foundations of rational choice theory (PHIL 443).