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Thursday, November 08, 2018
4:00 PM - 5:00 PM
Beckman Institute Auditorium

T & C Chen Center for Social and Decision Neuroscience Distinguished Lecture

Toward a Normative Account of the Capacity Constraints in Cognitive Control: Shared vs. Separated Representations and Interactive vs. Independent Parallelism
Jonathan Cohen, Robert Bendheim and Lynn Bendheim Thoman Professor, Princeton University,

Abstract: The capacity for cognitive control, one of the defining characteristics of human cognition, is also remarkably limited. Typically, people cannot engage in more than a few — and sometimes only a single — control-demanding task at once. Limited capacity was a defining element in the earliest conceptualizations of cognitive control, it remains one of the most widely accepted axioms of cognitive psychology, and is even the basis for some laws (e.g., against the use of mobile devices while driving). It also plays a central role in normative (e.g., "bounded rationality") models of cognitive control, which assume that the capacity limitation imposes an opportunity cost on the allocation of control, and that control policies are chosen so as to optimize payoff relative to this cost (e.g., the Expected Value of Control theory). Remarkably, however, the reason that the capacity for control is limited remains a mystery. Structural and/or metabolic constraints are commonly, if tacitly, assumed reasons. However, these seem unlikely, given the vast resources available to the human brain. In this talk, I will present an alternative account that offers a normative, computational explanation for the capacity constraints on cognitive control. This account suggests that constraints on controlled processing reflect an inherent bias in learning toward shared representation — that accelerates learning and supports generalization, but at the expense of constraints imposed on concurrency of processing (i.e., multitasking). I will describe theoretical results (involving simulations and analysis) in support of these ideas, the beginnings of an empirical line of research designed to test them, and consequences that this view has for the representation of information and processing in network architectures — both natural and artificial.

Speaker Bio: A graduate of Yale University, Jonathan Cohen earned his M.D. from the University of Pennsylvania. He did his internship and residency in psychiatry at the Stanford University School of Medicine. Jon earned his Ph.D. in cognitive psychology from Carnegie Mellon University in 1990. Cohen, whose specialty is cognitive neuroscience, joined the psychology faculty at Princeton University in 1998. He was named director of the Center for the Study of Brain, Mind and Behavior when it was created in 2000. The Princeton Neuroscience Institute was approved by University trustees in the spring of 2006. Jon and David Tank, the Henry Hillman Professor in Molecular Biology serve as co-directors of the Institute. They view the Institute as a stimulus for teaching and research in neuroscience and related fields, as well as an impetus for collaboration and education in disciplines as wide ranging as economics and philosophy. The Institute places particular emphasis on the close connection between theory, modeling and experimentation using the most advanced technologies. Research in the Cohen Lab focuses on the neurobiological mechanisms underlying cognitive control, and their disturbance in psychiatric disorders such as schizophrenia and depression.

Reception to follow.

This distinguished lecture is sponsored by the T&C Chen Center for Social and Decision Neuroscience, one of five interdisciplinary research centers affiliated with the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech. The Chen Institute at Caltech, founded in 2016 with the generous support of philanthropists Tianqiao Chen and Chrissy Luo, brings together a cross-disciplinary team of scientists and engineers to investigate one of today's greatest challenges and opportunities: understanding the brain and how it works.

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