Last updated March 28, 2019

James L. (Jay) McClelland
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Jay McClelland Lucie Stern Professor in the Social Sciences
Director, Center for Mind, Brain, Computation and Technology
Department of Psychology, Stanford University

Room 344 Bldg 420, 450 Jane Stanford Way
Stanford, CA 94305.

Curriculum Vitae and Career Highlights.
Publications and Resources for simulation and self-study.

Recorded talks

Public Lecture: Are people Still Smarter than Machines?
Heineken Prize Lecture: Mathematical Cognition: A PDP Perspective.
Rumelhart prize lecture: The Emergence of Semantic Cognition.

Interviews and news articles. This page in Spanish, Georgian, and Ukrainian.

Welcome and Overview

Welcome and thank you for visiting my home page. I am a Professor in the Psychology Department and Director of the Center for Mind, Brain, Computation and Technology at Stanford. My research addresses a broad range of topics in cognitive science and cognitive neuroscience, including perception and perceptual decision making; learning and memory; language and reading; semantic and mathematical cognition; and cognitive development. I view cognitive functions as emerging from the parallel, distributed processing activity of neural populations, with learning occurring through the adaptation of connections among participating neurons, as discussed in Parallel Distributed Processing (Rumelhart, McClelland, and the PDP Research Group, 1986). Research in my lab revolves around efforts to develop explicit computational models based on these ideas; to test, refine, and extend the principles embodied in the models; and then to apply the models to substantive research questions through behavioral experiment, computer simulation, and mathematical analysis.

My career highlights are listed below. A full list of relevant publications is available on my publications page, and links to other resources are provided next to my photograph above.

Mathematical Cognition

Recently, I have begun what I expect will be a broad-ranging and long-term program of research in mathematical cognition (watch this video or rea d this paper for a description of the approach). The work grows out of my long-standing interest in developmental transitions and in readiness to learn from new experiences as well as from the hope that a Parallel-Distributed Processing approach may shed light on some of the most awe-inspiring achievements of human thought --- the insights and structured reasoning systems that have been created by mathematicians. In this effort, we are combining experimental studies and computational modeling studies. The lab is seeking to recruit experimentally and/or computationally oriented students interested in contributing to this effort.

The goal is to understand the development of human abilities in mathematics at all levels, from numerosity and the initial stages of counting to arithmetic, algebra, geometry, and even multivariate mathematics and calculus. At the heart of the effort is the belief that mathematics is best viewed as a matter of learning a set of models that characterize the objects of mathematical thought and their properties, and to carry out operations on expressions that have meaning in terms of objects represented with such models. On this view, mathematics can be thought of as providing a way of seeing properties of (real or imagined, often idealized) objects or sets of objects that bring out useful relationships that are captured in symbolic expressions but that are often understood in terms of intuitively grasped relationships that gives these expressions their meaning. There is also an emphasis on understanding how gradual learning processes can eventually lead to insight and qualitatively different levels of understanding and mathematical ability, and on determining how best to support learners as they attempt to acquire such models.

The approach contrasts with rule-based approaches to mathematics in two ways: First, it treats formal systems of symbolic representation as ways of notating elements of a structured system for representing properties of objects and their relations, rather than simply as arrangements of symbols subject to processing according to structure-sensitive rules. Second, it distinguishes between the explicit knowledge of a formal rule and implicit knowledge embedded in acquired ways of perceiving and deriving inferences. For example, we can consult an explicit rule corresponding to the commutativity principle (for all a and b, a+b=b+a), or we may possess the implicit knowledge that the total quantity resulting from the additive combination of two part quantities is the same regardless of the order in which the part quantities are combined. I adhere to the view that an explicit rule is useful as a part of a system for formally establishing the validity of an understanding or insight, but that the understanding itself may come from the implicit knowledge, rather than from the manipulation of symbolic expressions in accordance with explicit rules. Thus, an essential part of teaching mathematics is finding ways to reinforce students' acquisition of the relevant models, rather than simply encouraging them to memorize a list of formulas.

Specific projects currently underway in the lab include: (a) an extension of deep learning that captures the gradual emergence of a representation of the numerosity of items in a visual scene, capturing the development of increasingly precise representations of numerosity across the first two decades of life; (b) a learning-based model aimed at capturing the graded, magnitude-dependent performance of children in tasks tapping their knowledge and ability to perform correctly in simple exact number tasks thought to tap the so-called 'cardinality principle'; (c) empirical and model-based assessment of mechanisms of numerical magnitude comparison, applied to comparison of fractions and both negative as well as positive numbers; and (d) investigations of the role of visuospatial representations in trigonometric reasoning. Papers on several of these topics are in the pipeline. In addition I have the long-term plan to create a simulated agent based on a neural network that can learn the principles of number, algebra, and geometry well enough to pass the New York State Regent's exam in Geometry. Some elements of this work are described in the lecture mentioned above.

Career Highlights

James L. (Jay) McClelland received his Ph.D. in Cognitive Psychology from the University of Pennsylvania in 1975. He served on the faculty of the University of California, San Diego, before moving to Carnegie Mellon in 1984, where he became a University Professor and held the Walter Van Dyke Bingham Chair in Psychology and Cognitive Neuroscience. He was a founding Co-Director of the Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon and the University of Pittsburgh. In 2006 McClelland moved to the Department of Psychology at Stanford University, where he founded the Center for Mind, Brain, and Computation in 2007 and served as department chair from fall 2009 through summer 2012. He is currently the Lucie Stern Professor in the Social Sciences and Co-Director of the Center for Mind, Brain, Computation and Technology.

Over his career, McClelland has contributed to both the experimental and theoretical literatures in a number of areas, most notably in the application of connectionist/parallel distributed processing models to problems in perception, cognitive development, language learning, and the neurobiology of memory. He was a co-founder with David E. Rumelhart of the Parallel Distributed Processing (PDP) research group, and together with Rumelhart he led the effort leading to the publication in 1986 of the two-volume book, Parallel Distributed Processing, in which the parallel distributed processing framework was laid out and applied to a wide range of topics in cognitive psychology and cognitive neuroscience. McClelland and Rumelhart jointly received the 1993 Howard Crosby Warren Medal from the Society of Experimental Psychologists, the 1996 Distinguished Scientific Contribution Award (see citation) from the American Psychological Association, the 2001 Grawemeyer Prize in Psychology, and the 2002 IEEE Neural Networks Pioneer Award for this work.

McClelland has served as Senior Editor of Cognitive Science, as President of the Cognitive Science Society, as a member of the National Advisory Mental Health Council, and as President of the Federation of Associations in the Behavioral and Brain Sciences (FABBS). He is a member of the National Academy of Sciences and a corresponding Fellow of the British Academy. He has received the APS William James Fellow Award for lifetime contributions to the basic science of psychology, the David E. Rumelhart prize for contributions to the theoretical foundations of Cognitive Science, the NAS Atkinson Prize in Psychological and Cognitive Sciences, and the Heineken Prize in Cognitive Science.

McClelland currently teaches on the PDP approach to cognition and its neural basis in the Psychology Department and in the Symbolic Systems Program at Stanford and conducts research on learning, memory, conceptual development, langauge processing, and mathematical cognition at Stanford and as a consulting research scientist at DeepMind.