A cognitive science curriculum balances two, potentially conflicting considerations. First, cognitive science is inherently multidisciplinary. 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. Thus, each track consists of a coherent, integrated and focused program of coursework that concomitantly integrates perspectives from multiple disciplines.
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), economics (ECON 408/PHIL 408), 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), artificial intelligence (EECS 492), ethology/animal cognition (PSYCH 335), and political science (POLSCI 391, POLSCI 490).
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) and psychology (LING 347/PSYCH 349) of language. Further coursework broadens the investigation of language to include topics in computational linguistics and computer science (e.g., EECS 376, 492, and 595; LING 441 and 442), formal methods (e.g., PHIL 414), and language development and learning (PSYCH 344 and 352, LING 342 and 440).
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 245) 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 315, 316 and 347; PHIL 345, 409 and 414, PSYCH 352 and 445), artificial intelligence (PHIL 417), inference and reasoning (PHIL 303, PSYCH 348), and decision‐making (PSYCH 449, PHIL 443).
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 245) and computer programming (EECS 281 and 492, and the prerequisite courses EECS 203 and 280). Subsequent depth courses emphasize – although not exclusively so—computational and formal methods including machine learning (EECS 545), computational linguistics (LING 441, EECS 595/LING 541), rational choice theory (PHIL 443), and mathematical psychology (PSYCH 448).