James L. McClelland

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Analogies Emerge from Learning Dyamics in Neural Networks

Andrew K. Lampinen, Shaw Hsu, James L. McClelland, 2017, CogSci.

Unintentional learning of sequential structure

James L. McClelland, Axel Cleeremans, A. Cleeremans, 1990 .

Bayesian analysis of simulation-based models

James L. McClelland, Brandon M. Turner, Per B. Sederberg, 2016 .

Exploiting lawful variability in the signal: The TRACE model of speech perception

James L. McClelland, Jeffrey L. Elman, 1983 .

Tractable Learning of Probability Distributions Using the Contrastive Hebbian Algorithm

James L. McClelland, Craig E.L. Stark, C. Stark, 2019, Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society.

The Development of the Time-Delayed Neural Network Architecture

Geoffrey E. Hinton, James L. McClelland, 1990 .

On learning the past-tenses of English verbs: implicit rules or parallel distributed processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

A simulation-based tutorial system for exploring parallel distributed processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1988 .

Explorations in parallel distributed processing: a handbook of models, programs, and exercises

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1988 .

Explorations in Parallel Distributed Processing - Macintosh version: A Handbook of Models, Programs, and Exercises

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1989 .

Open Questions About Computation in Cerebral Cortex

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

An Introduction to Linear Algebra in Parallel Distributed Processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Speech Perception as a Cognitive Process: The Interactive Activation Model.

Jeffrey L. Elman, James L. McClelland, J. Elman, 1983 .

From symbols to analog magnitudes: A process model of fraction comparison, with fits to experimental data

James L. McClelland, Cameron R. L. McKenzie, 2013, CogSci.

Predicting native English-like performance by native Japanese speakers

Lori L. Holt, James L. McClelland, Erin M. Ingvalson, 2011, J. Phonetics.

Integrating New Knowledge into a Neural Network without Catastrophic Interference: Computational and Theoretical Investigations in a Hierarchically Structured Environment

James L. McClelland, 2019 .

A Neural Network Model for the Concurrent Perception of Multiple Objects

James L. McClelland, Cynthia M. Henderson, 2011 .

Transforming task representations to allow deep learning models to perform novel tasks pdf

Andrew K. Lampinen, James L. McClelland, Andrew Kyle Lampinen, 2020, ArXiv.

Three Short Papers on Language and Connectionism

James L. McClelland, Mark St. John, M. John, 1987 .

Interactive Activation Models of Perception and Comprehension.

Jeffrey L. Elman, James L. McClelland, 1985 .

Learning Representations by Recirculation

Geoffrey E. Hinton, James L. McClelland, 1987, NIPS.

Toward Online Measurement of Decision State

James L. McClelland, Joel Lachter, James C. Johnston, 2009 .

Prediction-error driven learning: the engine of change in cognitive development

James L. McClelland, 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

Supplementary Materials Sections 1 and 2 from Integration of new information in memory: new insights from a complementary learning systems perspective

Bruce L. McNaughton, Andrew K. Lampinen, James L. McClelland, 2020 .

Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020)

James L. McClelland, 2020 .

Control of automatic processes: A parallel distributed-processing account of the Stroop effect. Technical report

Jonathan D. Cohen, James L. McClelland, Kevin Dunbar, 1989 .

Impaired Oral Reading in Surface Dyslexia: Detailed Comparison of a Patient and a Connectionist Model

James L. McClelland, David C. Plaut, Marlene Behrmann, 1998 .

A Hippocampal Model of Recognition Memory

James L. McClelland, Kenneth A. Norman, Randall C. O'Reilly, 1997, NIPS.

Connections and disconnections: Acquired dyslexia in a computational model of reading processes.

James L. McClelland, Mark S. Seidenberg, Karalyn Patterson, 1989 .

Interactive processes in speech perception: the TRACE model

Jeffrey L. Elman, James L. McClelland, J. Elman, 1986 .

Estimating the strength of unlabeled information during semi-supervised learning

James L. McClelland, Brenden M. Lake, B. Lake, 2011, CogSci.

An interactive activation model of context effects in letter perception: part 1.: an account of basic findings

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1988 .

N 400 amplitudes as change in a probabilistic representation of meaning : A neural network model

James L. McClelland, Milena Rabovsky, Steven Stenberg Hansen, 2017 .

The Place of Modeling in Cognitive Science

James L. McClelland, 2009, Top. Cogn. Sci..

Guest Editorial: Convergent Approaches to the Understanding of Autonomous Mental Development

James L. McClelland, Juyang Weng, Kim Plunkett, 2007, IEEE Trans. Evol. Comput..

Mechanisms of Sentence Processing: Assigning Roles to Constituents of Sentences

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

A PDP model of the simultaneous perception of multiple objects

James L. McClelland, Cynthia M. Henderson, 2011, Connect. Sci..

Parallel Distributed Processing: Implications for Cognition and Development

James L. McClelland, 1988 .

A neural network model of learning mathematical equivalence

James L. McClelland, Kevin W. Mickey, K. Mickey, 2014, CogSci.

Can a Recurrent Neural Network Learn to Count Things?

James L. McClelland, Sharon Chen, Mengting Fang, 2018, Annual Meeting of the Cognitive Science Society.

The Unit Circle as a Grounded Conceptual Structure in Precalculus Trigonometry

James L. McClelland, Kevin W. Mickey, K. Mickey, 2017 .

A Differentiation Account of Recognition Memory: Evidence from fMRI

James L. McClelland, Amy H. Criss, Mark E. Wheeler, 2013, Journal of Cognitive Neuroscience.

Connectionist models and their applicationPutting knowledge in its place: A scheme for programming parallel processing structures on the fly**

James L. McClelland, 1985 .

Learning Continuous Probability Distributions with Symmetric Diffusion Networks

Javier R. Movellan, James L. McClelland, J. Movellan, 1993, Cogn. Sci..

Stochastic Interactive Activation and the Effect of Context on Perception

James L. McClelland, 1989 .

P3: A Parallel Network Simulating System

James L. McClelland, David E. Rumelhart, 1987 .

Common Spatial Characteristics of Illusory Conjunctions and Crowding

James L. McClelland, Cynthia M. Henderson, 2012 .

Did, Made, Had, Said: Capturing Quasi-Regularity in Exception

Gary Lupyan, James L. McClelland, 2003 .

Extending Machine Language Models toward Human-Level Language Understanding pdf

Hinrich Schütze, James L. McClelland, Jason Baldridge, 2019, ArXiv.

Finite State Automata and Simple Recurrent Networks

James L. McClelland, Axel Cleeremans, David Servan-Schreiber, 1989, Neural Computation.

Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective

Bruce L. McNaughton, Andrew K. Lampinen, James L. McClelland, 2020, bioRxiv.

Putting Knowledge in its Place: A Scheme for Programming Parallel Processing Structures on the Fly

James L. McClelland, 1988, Cogn. Sci..

Neural Mechanisms for the Control and Monitoring of Memory: A Parallel Distributed Processing Perspective

James L. McClelland, 2014 .

Two Plus Three Is Five: Discovering Efficient Addition Strategies without Metacognition

James L. McClelland, Steven Stenberg Hansen, Cameron R. L. McKenzie, 2014, CogSci.

N400 amplitudes reflect change in a probabilistic representation of meaning: Evidence from a connectionist model

James L. McClelland, Milena Rabovsky, Steven Stenberg Hansen, 2016, CogSci.

Memory and its neural basis

James L. McClelland, 2010 .

Context, cortex, and associations: a connectionist developmental approach to verbal analogies

James L. McClelland, Pavlos Kollias, P. Kollias, 2013, Front. Psychol..

Formal Approaches in Categorization: Semantics without categorization

James L. McClelland, Timothy T. Rogers, T. Rogers, 2011 .

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

The PDP Perspective

James L. McClelland, David E. Rumelhart, 1987 .

Emergent Systematic Generalization in a Situated Agent pdf

Stephen Clark, James L. McClelland, Felix Hill, 2019, ICLR 2020.

LEARNING REPRESENT A TI 0 NS BY RECm CULA TION

James L. McClelland, 1988 .

The Case for Interactionism in Language Processing.

James L. McClelland, 1987 .

Resource requirements of standard and programmable nets

James L. McClelland, 1986 .

Running circles around symbol manipulation in trigonometry

James L. McClelland, Kevin W. Mickey, 2013, CogSci.

Concepts, Control, and Context: A Connectionist Account of Normal and Disordered Semantic Cognition

James L. McClelland, Paul Hoffman, Matthew A. Lambon Ralph, 2018, Psychological review.

Progressive Development of the Number Sense in a Deep Neural Network

James L. McClelland, Will Y. Zou, 2013, CogSci.

Matching Exact Posterior Probabilities in the Multinomial Interactive Activation Model

James L. McClelland, Pranav Khaitan, 2010 .

James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987.

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1989, Journal of Child Language.

Distinct Representations of Magnitude and Spatial Position within Parietal Cortex during Number–Space Mapping

James L. McClelland, Anthony M. Norcia, Peter J. Kohler, 2018, Journal of Cognitive Neuroscience.

Reflections on Cognition and Parallel Distributed Processing

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

A mathematical theory of semantic development in deep neural networks

Surya Ganguli, James L. McClelland, Andrew M. Saxe, 2018, Proceedings of the National Academy of Sciences.

Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition

James L. McClelland, Daniel Mirman, Donald J. Bolger, 2014, Cogn. Sci..

Neural and Conceptual Interpretation of PDP Models

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Effects of Attention on the Strength of Lexical Influences on Speech Perception: Behavioral Experiments and Computational Mechanisms

Lori L. Holt, James L. McClelland, James S. Magnuson, 2008, Cogn. Sci..

Sentence comprehension: A parallel distributed processing approach. Technical report

James L. McClelland, Roman Taraban, M. St John, 1989 .

Failures to Learn and their Remediation: A Hebbian Account

James L. McClelland, 1999 .

Integration of new information in memory: new insights from a complementary learning systems perspective

Andrew K. Lampinen, James L. McClelland, Bruce L. McNaughton, 2020, Philosophical Transactions of the Royal Society B.

Transforming task representations to perform novel tasks

Andrew K. Lampinen, James L. McClelland, 2020, Proceedings of the National Academy of Sciences.

Normal and impaired processing in quasi-regular domains of language: the case of English past-tense verbs

James L. McClelland, Matthew A. Lambon Ralph, Helen Bird, 2000, INTERSPEECH.

Connectionist Models of Development: Mechanistic Dynamical Models with Emergent Dynamical Properties

James L. McClelland, Gautam K. Vallabha, 2009 .

Tutorial Workshop on Contemporary Deep Neural Network Models

James L. McClelland, Andrew M. Saxe, Steven Stenberg Hansen, 2016, CogSci.

Semantic Cognition: A Parallel Distributed Processing Approach

James L. McClelland, Timothy T. Rogers, T. Rogers, 2004 .

Contrastive learning with graded random networks

Javier R. Movellan, James L. McClelland, J. Movellan, 1994, COLT 1994.

A Critique of Pure Hierarchy: Uncovering Cross-Cutting Structure in a Natural Dataset

James L. McClelland, Zahra Sadeghi, Andrew M. Saxe, 2016 .

One-shot and few-shot learning of word embeddings pdf

Andrew K. Lampinen, James L. McClelland, Andrew Kyle Lampinen, 2017, ArXiv.

Explorations In Parallel Distributed Processing

James L. McClelland, 1988 .

Performance Feedback Drives Caudate Activation in a Phonological Learning Task

James L. McClelland, Elizabeth Tricomi, Julie A. Fiez, 2006, Journal of Cognitive Neuroscience.

A general framework for parallel distributed processing

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986 .

Cognitive Science Meets Autonomous Mental Development

Brian Scassellati, James L. McClelland, Juyang Weng, 2010, Cogn. Sci..

Symposium in Memory of Jeff Elman: Language Learning, Prediction, and Temporal Dynamics

James L. McClelland, Ken McRae, K. McRae, 2019, CogSci.

Dissociating Reading Processes on the Basis of Neuronal Interactions

Karl J. Friston, James L. McClelland, Matthew A. Lambon Ralph, 2005, Journal of Cognitive Neuroscience.

Modeling unsupervised perceptual category learning

James L. McClelland, Brenden M. Lake, Gautam K. Vallabha, 2008 .

Why Bilateral Damage Is Worse than Unilateral Damage to the Brain

James L. McClelland, Matthew A. Lambon Ralph, Anna C. Schapiro, 2013, Journal of Cognitive Neuroscience.

Connecting learning, memory, and representation in math education

Vladimir M. Sloutsky, James L. McClelland, Philip J. Kellman, 2015, CogSci.

A simple model from a powerful framework that spans levels of analysis

James L. McClelland, Timothy T. Rogers, T. Rogers, 2008, Behavioral and Brain Sciences.

Learning and Applying Contextual Constraints in Sentence Comprehension

James L. McClelland, Mark F. St. John, M. John, 1990, Artif. Intell..

Learning Subsequential Structure in Simple Recurrent Networks

James L. McClelland, Axel Cleeremans, David Servan-Schreiber, 1988, NIPS.

Connectionist Models of Language

James L. McClelland, 1989, IWPT.

Graded state machines: The representation of temporal contingencies in simple recurrent networks

James L. McClelland, Axel Cleeremans, David Servan-Schreiber, 1991, Machine Learning.

An Analysis of the Delta Rule and the Learning of Statistical Associations

James L. McClelland, David E. Rumelhart, 1987 .

Using Time-Varying Evidence to Test Models of Decision Dynamics: Bounded Diffusion vs. the Leaky Competing Accumulator Model

Marius Usher, James L. McClelland, Konstantinos Tsetsos, 2012, Front. Neurosci..

Modelling the N400 brain potential as change in a probabilistic representation of meaning

James L. McClelland, Milena Rabovsky, Steven S. Hansen, 2018, Nature Human Behaviour.

Geometric Concept Acquisition in a Dueling Deep Q-Network

Mykel J. Kochenderfer, James L. McClelland, Alex Kuefler, 2017, CogSci.

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks pdf

Surya Ganguli, James L. McClelland, Andrew M. Saxe, 2013, ICLR.

Emerging Representations for Counting in a Neural Network Agent Interacting with a Multimodal Environment

James L. McClelland, Trygve Solstad, Silvester Sabathiel, 2020 .

The programmable blackboard model of reading

James L. McClelland, 1986 .

Parallel Distributed Processing and Role Assignment Constraints

James L. McClelland, 1987, TINLAP.

I like coffee with cream and dog? Change in an implicit probabilistic representation captures meaning processing in the brain

James L. McClelland, Milena Rabovsky, Steven Stenberg Hansen, 2017, bioRxiv.

Is a Machine Realization of Truly Human-Like Intelligence Achievable?

James L. McClelland, 2009, Cognitive Computation.

The Logic of Activation Functions

James L. McClelland, David E. Rumelhart, 1987 .

[Neural Networks: A Review from Statistical Perspective]: Comment: Neural Networks and Cognitive Science: Motivations and Applications

James L. McClelland, 1994 .

The parallel distributed processing approach to semantic cognition

James L. McClelland, Timothy T. Rogers, T. Rogers, 2003, Nature Reviews Neuroscience.

Modeling Unsupervised Perceptual Category Learning

James L. McClelland, Brenden M. Lake, Gautam K. Vallabha, 2008, IEEE Transactions on Autonomous Mental Development.

Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension

James L. McClelland, Milena Rabovsky, Milena Rabovsky, 2019, Philosophical Transactions of the Royal Society B.

Data from 617 Healthy Participants Performing the Iowa Gambling Task: A “Many Labs” Collaboration

James L. McClelland, Thorsten Pachur, Helen Steingroever, 2015 .

Biologically Plausible Models of Place Recognition and Goal Location

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1987 .

Studying Individual Differences in Reading

James L. McClelland, Mark D. Jackson, M. D. Jackson, 1978 .

Emergence of Euclidean geometrical intuitions in hierarchical generative models

James L. McClelland, Arianna Yuan, Te-Lin Wu, 2016, CogSci.

Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition

James L. McClelland, Timothy T. Rogers, T. Rogers, 2014, Cogn. Sci..

Computational Autonomous Mental Development: A White Paper for Suggesting a New Initiative

Alex Pentland, Olaf Sporns, James L. McClelland, 2000 .

Toward a theory of information processing in graded, random, and interactive networks

James L. McClelland, 1993 .

Parallel Distributed Processing: Bridging the gap between human and machine intelligence

James L. McClelland, Axel Cleeremans, David Servan-Schreiber, 1990 .

Formation of categories for complex novel auditory stimuli

Lori L. Holt, James L. McClelland, Daniel Mirman, 2002 .

Schemata and Sequential Thought Processes in PDP Models

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986 .

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review

James L. McClelland, 2013, Front. Psychol..

Zero-shot task adaptation by homoiconic meta-mapping

Andrew K. Lampinen, James L. McClelland, 2019 .

Modeling Number Sense Acquisition in A Number Board Game by Coordinating Verbal, Visual, and Grounded Action Components

James L. McClelland, Arianna Yuan, Arianna Yuan, 2019, CogSci.

READING EXCEPTION WORDS AND PSEUDOWORDS - ARE 2 ROUTES REALLY NECESSARY

James L. McClelland, David C. Plaut, Mark S. Seidenberg, 1992 .

Constructive memory and memory distortions: A parallel-distributed processing approach.

James L. McClelland, 1995 .

Why do we have a special learning system in the hippocampus?,(Abstract 580)

Bruce L. McNaughton, James L. McClelland, Randall C. O'Reilly, 1993 .

The appeal of parallel distributed processing

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986 .

Neural responses decrease while performance increases with practice: A neural network model

James L. McClelland, Milena Rabovsky, Steven Stenberg Hansen, 2017, CogSci.

Emergence in Cognitive Science

James L. McClelland, 2010, Top. Cogn. Sci..

Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

Capturing Gradience, Continuous Change, and Quasi‐Regularity in Sound, Word, Phrase, and Meaning

James L. McClelland, 2015 .

Testing Multi-Alternative Decision Models with Non-Stationary Evidence

Marius Usher, James L. McClelland, Konstantinos Tsetsos, 2011, Front. Neurosci..

Different Presentations of a Mathematical Concept Can Support Learning in Complementary Ways

Andrew K. Lampinen, James L. McClelland, Andrew Kyle Lampinen, 2018, Journal of Educational Psychology.

Developing the knowledge of number digits in a child-like robot

James L. McClelland, Alessandro Di Nuovo, A. D. Nuovo, 2019, Nature Machine Intelligence.

Distributed Representations

Geoffrey E. Hinton, James L. McClelland, David E. Rumelhart, 1986, The Philosophy of Artificial Intelligence.

Letter and configuration information in word identification

James L. McClelland, 1977 .

Learning hierarchical categories in deep neural networks

Surya Ganguli, James L. McClelland, Andrew M. Saxe, 2013, CogSci.

A distributed model of human learning and memory

James L. McClelland, David E. Rumelhart, D. Rumelhart, 1986 .

Extending decision making models in the brain: multi-alternatives, nonlinearity and multiple goals/dimensions

Marius Usher, James L. McClelland, Zhang Jiaxiang, 2006 .

Experimental tests of a hierarchical model of word identification

James L. McClelland, James C. Johnston, J. C. Johnston, 1980 .

Attentional Modulation of Lexical Effects on Speech Perception: Computational and Behavioral Experiments

Lori L. Holt, James L. McClelland, Daniel Mirman, 2006 .

Information Factorization in Connectionist Models of Perception

Javier R. Movellan, James L. McClelland, J. Movellan, 1999, NIPS.

Can Generic Neural Networks Estimate Numerosity Like Humans?

James L. McClelland, Sharon Chen, Mengting Fang, 2018, CogSci.

Sentence comprehension: A parallel distributed processing approach

James L. McClelland, Mark St. John, Roman Taraban, 1989, Language and Cognitive Processes.

Tell me why! - Explanations support learning of relational and causal structure pdf

Andrew K. Lampinen, James L. McClelland, Felix Hill, 2021, ArXiv.

What underlies rapid learning and systematic generalization in humans pdf

James L. McClelland, Andrew Joohun Nam, A. Nam, 2021, ArXiv.

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

James L. McClelland, B. McNaughton, R. O’Reilly, 1995, Psychological review.

An interactive activation model of context effects in letter perception: I. An account of basic findings.

James L. McClelland, D. Rumelhart, 1981 .

Computational approaches to cognition: top-down approaches

James L. McClelland, D. Plaut, 1993, Current Opinion in Neurobiology.

Conspiracy effects in word pronunciation.

James L. McClelland, R. Taraban, 1987 .

An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model.

James L. McClelland, D E Rumelhart, J L McClelland, 1982, Psychological review.

The role of familiar units in perception of words and nonwords

James L. McClelland, J. C. Johnston, 1977 .

An Interactive Activation Model of the Effect of Context in Perception. Part II. Report No. 8003.

James L. McClelland, D. Rumelhart, 1980 .

On the time relations of mental processes: An examination of systems of processes in cascade.

James L. McClelland, 1979 .

Psychological and biological models

James L. McClelland, D. Rumelhart, 1986 .

Autonomous Mental Development by Robots and Animals

James L. McClelland, A. Pentland, J. Weng, 2001, Science.

Distributed memory and the representation of general and specific information.

James L. McClelland, D. Rumelhart, 1985, Journal of experimental psychology. General.

Visual factors in word perception

James L. McClelland, J. C. Johnston, 1973 .

Considerations arising from a complementary learning systems perspective on hippocampus and neocortex

James L. McClelland, N. H. Goddard, 1996, Hippocampus.

Hippocampal conjunctive encoding, storage, and recall: Avoiding a trade‐off

James L. McClelland, R. O’Reilly, 1994, Hippocampus.

Learning hierarchical category structure in deep neural networks

James L. McClelland, Andrew M. Saxe, S. Ganguli, 2013 .

PDP models and general issues in cognitive science

James L. McClelland, D. Rumelhart, 1986 .

Parallel Distributed Processing

James L. McClelland, 2005 .

Rethinking infant knowledge: toward an adaptive process account of successes and failures in object permanence tasks.

James L. McClelland, R. Siegler, Y. Munakata, 1997, Psychological review.

Complementary Learning Systems in the Brain: A Connectionist Approach to Explicit and Implicit Cognition and Memory

James L. McClelland, 1998, Annals of the New York Academy of Sciences.

Letting structure emerge: connectionist and dynamical systems approaches to cognition

James L. McClelland, Linda B. Smith, D. Plaut, 2010, Trends in Cognitive Sciences.

Generalization Through the Recurrent Interaction of Episodic Memories

James L. McClelland, D. Kumaran, 2012, Psychological review.

Structural factors in figure perception

James L. McClelland, Jeff Miller, 1979 .

The time course of perceptual choice: the leaky, competing accumulator model.

James L. McClelland, M. Usher, 2001, Psychological review.

Developing a domain-general framework for cognition: What is the best approach?

James L. McClelland, D. Plaut, S. Gotts, 2003, Behavioral and Brain Sciences.

Success and failure in teaching the [r]-[l] contrast to Japanese adults: Tests of a Hebbian model of plasticity and stabilization in spoken language perception

James L. McClelland, Bruce D. McCandliss, J. Fiez, 2002, Cognitive, affective & behavioral neuroscience.

Loss aversion and inhibition in dynamical models of multialternative choice.

James L. McClelland, M. Usher, 2004, Psychological review.

On the control of automatic processes: a parallel distributed processing account of the Stroop effect.

James L. McClelland, J. Cohen, K. Dunbar, 1990, Psychological review.

The TRACE model of speech perception

James L. McClelland, J. Elman, 1986, Cognitive Psychology.

Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition

James L. McClelland, Joan L. Bybee, Joan Bybee, 2005 .

Précis of Semantic Cognition: A Parallel Distributed Processing Approach

James L. McClelland, T. Rogers, 2008, Behavioral and Brain Sciences.

Category Learning: Learning the general but not the specific

James L. McClelland, 1994, Current Biology.

A computational model of semantic memory impairment: modality specificity and emergent category specificity.

James L. McClelland, M. Farah, 1991, Journal of experimental psychology. General.

No Right to Speak? The Relationship between Object Naming and Semantic Impairment:Neuropsychological Evidence and a Computational Model

James L. McClelland, K. Patterson, J. Hodges, 2001, Journal of Cognitive Neuroscience.

Familiarity breeds differentiation: a subjective-likelihood approach to the effects of experience in recognition memory.

James L. McClelland, M. Chappell, 1998, Psychological review.

Connectionist models of development

James L. McClelland, Y. Munakata, 2003 .

A computational model of semantic memory impairment: modality specificity and emergent category specificity.

James L. McClelland, M. Farah, 1991 .

Mechanisms of cognitive development : behavioral and neural perspectives

James L. McClelland, R. Siegler, 2001 .

Levels indeed! A response to Broadbent

James L. McClelland, D. Rumelhart, 1985 .

Understanding normal and impaired word reading: computational principles in quasi-regular domains.

James L. McClelland, D C Plaut, Mark S. Seidenberg, 1996, Psychological review.

Can a perceptual processing deficit explain the impairment of inflectional morphology in developmental dysphasia? A computational investigation.

James L. McClelland, J. Hoeffner, 1993 .

Nonword pronunciation and models of word recognition.

James L. McClelland, Mark S. Seidenberg, D. Plaut, 1994, Journal of experimental psychology. Human perception and performance.

Generalization with Componential Attractors: Word and Nonword Reading in an Attractor Network

James L. McClelland, D. Plaut, 1993 .

More Words but Still No Lexicon: Reply to Besner et al. (1990)

James L. McClelland, Mark S. Seidenberg, 1990 .

More Words but Still No Lexicon : Reply to Besner et al . ( 1990 )

James L. McClelland, Mark S. Seidenberg, 1990 .

Rules or connections in past-tense inflections: what does the evidence rule out?

James L. McClelland, K. Patterson, 2002, Trends in Cognitive Sciences.

You shall know an object by the company it keeps: An investigation of semantic representations derived from object co-occurrence in visual scenes

James L. McClelland, Zahra Sadeghi, Paul Hoffman, 2015, Neuropsychologia.

Running Head : Letting Structure Emerge Letting Structure Emerge : Connectionist and Dynamical Systems Approaches to Understanding Cognition

James L. McClelland, 2009 .

Hick's law in a stochastic race model with speed-accuracy tradeoffs

James L. McClelland, M. Usher, Z. Olami, 2002 .

Connectionist models of cognition.

James L. McClelland, Michael S. C. Thomas, M. Thomas, 2008 .

The neurodynamics of choice, value-based decisions and preference reversal

James L. McClelland, M. Usher, Anat Elhalal, 2008 .

Information integration in perception and communication

James L. McClelland, 乾敏郎, 1996 .

Amnesia and distributed memory

James L. McClelland, D. Rumelhart, 1986 .

Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models

Hinrich Schütze, James L. McClelland, Jason Baldridge, 2020, Proceedings of the National Academy of Sciences.

Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making

James L. McClelland, Juan Gao, R. Tortell, 2011, PloS one.

Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.

James L. McClelland, 2013, Journal of experimental psychology. General.

A single-system account of semantic and lexical deficits in five semantic dementia patients

James L. McClelland, D. Plaut, Katia Dilkina, 2008, Cognitive neuropsychology.

Toward a Unified Theory of Development: Connectionism and Dynamic Systems Theory Re-Considered

James L. McClelland, Michael S. C. Thomas, J. Spencer, 2009 .

Dynamical and connectionist approaches to development: toward a future of mutually beneficial co-evolution

James L. McClelland, Frank D. Baughman, Michael S. C. Thomas, 2009 .

U-Shaped Curves in Development: A PDP Approach

James L. McClelland, T. Rogers, D. Rakison, 2004, Journal of Cognition and Development.

A connectionist model of a continuous developmental transition in the balance scale task

James L. McClelland, A. Schapiro, 2009, Cognition.

Stipulating versus discovering representations

James L. McClelland, D. Plaut, 2000, Behavioral and Brain Sciences.

Locating object knowledge in the brain: comment on Bowers's (2009) attempt to revive the grandmother cell hypothesis.

James L. McClelland, D. Plaut, 2010, Psychological review.

How Language Affects Thought in a Connectionist Model

James L. McClelland, L. Boroditsky, Katia Dilkina, 2007 .

Semantic Cognition : Its Nature , Its Development , and Its Neural Basis

James L. McClelland, M. Gazzaniga, T. Rogers, 2008 .

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics.

James L. McClelland, Alberto Testolin, Youzhi Zou, 2020, Developmental science.

Integration of Sensory and Reward Information during Perceptual Decision-Making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey

James L. McClelland, W. Newsome, A. Rorie, 2010, PloS one.

A parallel distributed processing approach to automaticity.

James L. McClelland, J. Cohen, D. Servan-Schreiber, 1992, The American journal of psychology.

Expectations Increase the Benefit Derived from Parafoveal Visual Information in Reading Words Aloud.

James L. McClelland, J. O'Regan, 1981 .

Deep Learning: Implications for Human Learning and Memory

James L. McClelland, M. Botvinick, 2020 .

Using domain-general principles to explain children's causal reasoning abilities.

James L. McClelland, Richard M. Thompson, 2007, Developmental science.

A reexamination of the evidence for the somatic marker hypothesis: what participants really know in the Iowa gambling task.

James L. McClelland, James L McClelland, T. Maia, 2004, Proceedings of the National Academy of Sciences of the United States of America.

Nature, nurture, and connections: Implications of connectionist models for cognitive development.

James L. McClelland, E. Jenkins, 1991 .

Elsevier Editorial System(tm) for Journal of Memory and Language Manuscript Draft Title: Differentiating the Differentiation Models: a Comparison of the Retrieving Effectively from Memory Model (rem) and the Subjective Likelihood Model (slim) Differentiating the Differentiation Models: a Comparison

James L. McClelland, A. Criss, H. Amy, 2022 .

Connectionism and the Emergence of Mind

James L. McClelland, S. Flusberg, 2017 .

CHAPTER 2 Connectionist Models of Cognition

James L. McClelland, Michael S. C. Thomas, M. Thomas, 2007 .

Deficits in phonology and past-tense morphology: What's the connection?

James L. McClelland, Mark S. Seidenberg, K. Patterson, 2003 .

Learning and Applying Contextual Comprehension *

James L. McClelland, John, Markey St, 1990 .

Two Mechanisms of Human Contingency Learning

James L. McClelland, Daniel A. Sternberg, 2011, Psychological Science.

An interactive Hebbian account of lexically guided tuning of speech perception

James L. McClelland, D. Mirman, L. Holt, 2006, Psychonomic bulletin & review.

SFILE CO ENCODING SEQUENTIAL STRUCTURE N IN SIMPLE RECURRENT NETWORKS

James L. McClelland, 2022 .

Cognitive Psychology: History

James L. McClelland, 2004 .

Payoff Information Biases a Fast Guess Process in Perceptual Decision Making under Deadline Pressure: Evidence from Behavior, Evoked Potentials, and Quantitative Model Comparison

James L. McClelland, James L McClelland, Dahlia Sharon, 2015, The Journal of Neuroscience.

Complementary Learning Systems 1 Why there are Complementary Learning Systems in the Hippocampus and Neocortex : Insights from the Successes and Failures of Connectionist Models of Learning and Memory

Bruce L. McNaughton, James L. McClelland, B. McNaughton, 1999 .

Deficits in phonology and past tense morphology

James L. McClelland, Seidenberg, K. Patterson, 2003 .

Are there interactive processes in speech perception?

Lori L. Holt, James L. McClelland, Daniel Mirman, 2006, Trends in Cognitive Sciences.

Success and failure of new speech category learning in adulthood: Consequences of learned Hebbian attractors in topographic maps

James L. McClelland, James L McClelland, Gautam K. Vallabha, 2007, Cognitive, affective & behavioral neuroscience.

Repetition priming of words, pseudowords, and nonwords.

James L. McClelland, J L McClelland, C. Stark, 2000, Journal of experimental psychology. Learning, memory, and cognition.

Categorization and discrimination of nonspeech sounds: differences between steady-state and rapidly-changing acoustic cues.

James L. McClelland, James L McClelland, Daniel Mirman, 2004, The Journal of the Acoustical Society of America.

Automaticity , Attention and the Strength of Processing : A Parallel Distributed Processing Account of the Stroop Effect

James L. McClelland, Jonathan D. Cohen, K. Dunbar, 2001 .

Supplementary Information : A mathematical theory of semantic development in deep neural networks

James L. McClelland, Andrew M. Saxe, S. Ganguli, 2019 .

Can native Japanese listeners learn to differentiate /r–l/ on the basis of F3 onset frequency?*

James L. McClelland, James L McClelland, Lori L Holt, 2011, Bilingualism: Language and Cognition.

Deficits in irregular past-tense verb morphology associated with degraded semantic knowledge

James L. McClelland, K Patterson, K. Patterson, 2001, Neuropsychologia.

A CONNECTIONIST ACCOUNT OF SURFACE DYSLEXIA

James L. McClelland, D. Plaut, J. Hodges, 1996 .

CONNECTIONS AND DISCONNECTIONS: A CONNECTIONIST ACCOUNT OF SURFACE DYSLEXIA

James L. McClelland, David C. Plaut, Marlene Behrmann, 1996 .

The memory process : neuroscientific and humanistic perspectives

James L. McClelland, Paul M. Matthews, P. Matthews, 2010 .

Mathematical Cognition 1 A Parallel-Distributed Processing Approach to Mathematical Cognition1

James L. McClelland, Arianna Yuan, Kevin Mickey, 2016 .

When a bad metaphor may not be a victimless crime: The role of metaphor in social policy

James L. McClelland, Lera Boroditsky, Paul H. Thibodeau, 2009 .

Integration of information: Reflections on the theme of attention and performance XVI

James L. McClelland, 1996 .

Can Connectionist Models the Symbol-processing

James L. McClelland, 1992 .

Cognitive Modeling, Symbolic

James L. McClelland, R. H. Phaf, D C Plaut, .

Chapter 5: Semantics Without Categorization

James L. McClelland, T. Rogers, 2009 .

Approach to Automaticity Principles of Information Processing Basic Aspects of Automaticity the Stroop Model

James L. McClelland, -. Davidservan, 2005 .

Distributed Models of Cognitive Processes: Applications to Learning and Memory a

James L. McClelland, J L McClelland, 1985, Annals of the New York Academy of Sciences.

Trace synthesis in cued recall

James L. McClelland, Leigh E. Nystrom, L. Nystrom, 1992 .

Are there mental lexicons? The role of semantics in lexical decision

James L. McClelland, David C. Plaut, Katia Dilkina, 2010, Brain Research.

Certain Aspects of the Anatomy and Physiology of the Cerebral Cortex

James L. McClelland, David E. Rumelhart, 1987 .

Connectionist Models

James L. McClelland, Axel Cleeremans, A. Cleeremans, 2010 .

Why Do Kids Say Goed and Brang?

James L. McClelland, Mark S. Seidenberg, M. Seidenberg, 2000, Science.

Electronic Supplementary Material for “ Toward an Executive without a Homunculus ”

James L. McClelland, R. O’Reilly, C. Dawson, 2022 .

Attentional Modulation of Lexical Effects in an Interactive Model of Speech Perception

James L. McClelland, D. Mirman, L. Holt, 2004 .

Dissociating stimulus‐driven semantic and phonological effect during reading and naming

James L. McClelland, James L McClelland, Andrea Mechelli, 2006, Human brain mapping.

Introduction to 30 th Anniversary Perspectives on Cognitive Science : Past , Present , and Future

James L. McClelland, Douglas L. Medin, D. Medin, 2010 .

What underlies the neuropsychological pattern of irregular>regular past-tense verb production?

James L. McClelland, Natalie Braber, Matthew A. Lambon Ralph, 2005, Brain and Language.

An Interactive Activation Model of the Effect of Context in Perception, Part I. Technical Report, April, 1979 through March, 1980. Report No. 8002.

James L. McClelland, David E. Rumelhart, 1980 .

Memory as a Constructive Process

James L. McClelland, 2010 .

Introduction to the Special Issue

James L. McClelland, Anthony D. Marley, M. Liberman, 2017, Research on aging.

Memory as a Constructive Process: The Parallel Distributed Processing Approach

James L. McClelland, 2010 .

Sensory and cognitive determinants of reading speed

James L. McClelland, M. D. Jackson, Mark D. Jackson, 1975 .

Information Processing in Dynamical Systems: Foundations of Harmony Theory

James L. McClelland, David E. Rumelhart, 1987 .

Visual word recognition and pronunciation: A computational model of acquisition, skilled performance, and dyslexia.

James L. McClelland, Mark S. Seidenberg, M. Seidenberg, 1989 .

Phonology and perception : A cognitive scientist ’ s perspective

James L. McClelland, 2009 .

Graded state machines: the representation of temporal contingencies in feedback networks

James L. McClelland, Axel Cleeremans, David Servan-Schreiber, 1995 .

State-Dependent Factors Influencing Neural Plasticity: A Partial Account of the Critical Period

James L. McClelland, David E. Rumelhart, 1987 .

Teaching the /r/–/l/ discrimination to Japanese adults: behavioral and neural aspects

James L. McClelland, Julie A. Fiez, Bruce D. McCandliss, 2002, Physiology & Behavior.

Conjunctive search for one and two identical targets.

James L. McClelland, Robert Ward, 1989 .

A computational model of semantic memory impairment: Modality specificity and emergent category specificity (Journal of Experimental Psychology: General, 120(4), 339–357)

James L. McClelland, Martha J. Farah, 2013 .

Differentiation and integration in human language Reply to Marslen-Wilson and Tyler

James L. McClelland, Karalyn Patterson, K. Patterson, 2003, Trends in Cognitive Sciences.

Response to McQueen et al.: Theoretical and empirical arguments support interactive processing

Lori L. Holt, James L. McClelland, Daniel Mirman, 2006, Trends in Cognitive Sciences.

Explorer Concepts , control and context

James L. McClelland, P. Hoffman, M. L. Ralph, 2018 .

Can native Japanese listeners learn to differentiate /r–l/ on the basis of F3 onset frequency? – CORRIGENDUM

James L. McClelland, L. Holt, Erin M. Ingvalson, 2012, Bilingualism: Language and Cognition.

Learning to discriminate English / r / and / l / in adulthood : Behavioral and modeling studies

James L. McClelland, 2015 .

Solutions to Fodor ’ s Puzzle of Concept Acquisition

James L. McClelland, A. Gopnik, F. Keil, 2005 .

Trophic relationships among Southern Ocean copepods and krill: Some uses and limitations of a stable isotope approach

James L. McClelland, J. Montoya, M. Voss, 2003 .

Learning hierarchical category structure in deep neural networks

James L. McClelland, Andrew M. Saxe, S. Ganguli, 2013 .

Running head : HEBBIAN LEARNING How Far Can You Go with Hebbian Learning , and When Does it Lead you Astray ?

James L. McClelland, 2005 .

" Vector Quantization : A Pattern-Matching Technique for Speech Coding

James L. McClelland, 2017 .

Intrusions into the shadow of attention: A new take on illusory conjunctions

James L. McClelland, Cynthia M. Henderson, C. M. Henderson, 2020, Attention, perception & psychophysics.

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

James L. McClelland, D. Hassabis, D. Kumaran, 2016, Trends in Cognitive Sciences.

A distributed, developmental model of word recognition and naming.

James L. McClelland, Mark S. Seidenberg, 1989, Psychological review.

Tell me why! Explanations support learning relational and causal structure

James L. McClelland, Andrew Kyle Lampinen, Nicholas A. Roy, 2021, ICML.

Computational and behavioral investigations of lexically induced delays in phoneme recognition

James L. McClelland, D. Mirman, L. Holt, 2005 .

Constituent Attachment and Thematic Role Assignment in Sentence Processing: Influences of Content-Based Expectations

James L. McClelland, R. Taraban, 1988 .

Language models show human-like content effects on reasoning

James L. McClelland, Andrew Kyle Lampinen, Stephanie C. Y. Chan, 2022, ArXiv.

Zero-shot task adaptation by homoiconic meta-mapping

James L. McClelland, Andrew Kyle Lampinen, 2019, 1905.09950.

Solutions to Fodor's Problem of Concept Acquisition

James L. McClelland, J. Fodor, F. Keil, 2005 .

Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies

James L. McClelland, Alberto Testolin, 2020, Psychonomic bulletin & review.

Stochastic interactive processes and the effect of context on perception

James L. McClelland, 1991, Cognitive Psychology.

Genetic Assessment of Breeding Patterns and Population Size of the Sicklefin Lemon Shark Negaprion acutidens in a Tropical Marine Protected Area: Implications for Conservation and Management

James L. McClelland, 2020 .

B-Spline Contour Representation and Symmetry Detection

James L. McClelland, B. L. Kalman, J. L. McClelland, 1993 .

A computational model of learning to count in a multimodal, interactive environment

James L. McClelland, T. Solstad, Silvester Sabathiel, 2020, CogSci.

Can language models learn from explanations in context?

James L. McClelland, Andrew Kyle Lampinen, Michael Henry Tessler, 2022, EMNLP.

Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks

James L. McClelland, Yuxuan Li, 2022, 2210.00400.

Learning to Reason With Relational Abstractions

James L. McClelland, Chelsea Finn, Mengye Ren, 2022, ArXiv.

Environmental drivers of systematicity and generalization in a situated agent

James L. McClelland, Andrew Kyle Lampinen, Adam Santoro, 2019, ICLR.

Capturing advanced human cognitive abilities with deep neural networks

James L. McClelland, 2022, Trends in Cognitive Sciences.

Learning to Reason With Relational Abstractions

James L. McClelland, Chelsea Finn, Mengye Ren, 2022, ArXiv.

Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks

James L. McClelland, Yuxuan Li, 2022, ArXiv.

A Parallel Distributed Processing Perspective

James L. McClelland, James L Mcclelland, 1994 .

Transforming task representations to perform novel tasks

James L. McClelland, Andrew Kyle Lampinen, 2020, Proceedings of the National Academy of Sciences.

Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks

James L. McClelland, Axel Cleeremans, D. Servan-Schreiber, 2005, Machine Learning.

When Should We Expect Indirect Effects in Human Contingency Learning

James L. McClelland, Daniel A. Sternberg, 2009 .

Phonology without Phonemes

James L. McClelland, 2004 .

Cognitive penetration of the mechanisms of perception: Compensation for coarticulation of lexically restored phonemes

James L. McClelland, J. Elman, 1988 .

Out-of-Distribution Generalization in Algorithmic Reasoning Through Curriculum Learning

James L. McClelland, A. Nam, Mustafa Abdool, 2022, arXiv.org.

Achieving and Understanding Out-of-Distribution Generalization in Systematic Reasoning in Small-Scale Transformers

James L. McClelland, A. Nam, Mustafa Abdool, 2022, 2210.03275.

Intrusions into the shadow of attention: A new take on illusory conjunctions

James L. McClelland, Cynthia M. Henderson, 2020, Attention, Perception, & Psychophysics.

The Morton-Massaro law of information integration: implications for models of perception.

James L. McClelland, J L McClelland, J R Movellan, 2001, Psychological review.

Learning the structure of event sequences.

James L. McClelland, J L McClelland, A Cleeremans, 1991, Journal of experimental psychology. General.

TWO MECHANISMS OF CONTINGENCY LEARNING 1 " RUNNING HEAD : TWO MECHANISMS OF CONTINGENCY LEARNING Two Mechanisms of Human Contingency Learning

James L. McClelland, Daniel A. Sternberg, 2011 .

Now You See It , Now You Don ' t : A Gradualistic Framework for Understanding Infants ' Successes and Failures in Object Permanence Tasks

James L. McClelland, Mark H. Johnson, R. Siegler, 2001 .

Structure and deterioration of semantic memory: a neuropsychological and computational investigation.

James L. McClelland, James L McClelland, Peter Garrard, 2004, Psychological review.

Scholarship@Western Scholarship@Western

James L. McClelland, James L McClelland, Marc F Joanisse, 2022 .

Investigating the effectiveness of a well‐managed hatchery as a tool for hawksbill sea turtle (Eretmochelys imbricata) conservation

James L. McClelland, M. Brown, S. Dunlop, 2022, Conservation Science and Practice.

Embedded Meta-Learning: Toward more flexible deep-learning models

James L. McClelland, Andrew Kyle Lampinen, 2019, ArXiv.

Are theories necessary to constrain concepts ?

James L. McClelland, T. Rogers, 2003 .

The Basis of Hyperspecificity in Autism: A Preliminary Suggestion Based on Properties of Neural Nets

James L. McClelland, J L McClelland, 2000, Journal of autism and developmental disorders.

The organization of memory. A parallel distributed processing perspective.

James L. McClelland, J L McClelland, 1994, Revue neurologique.

An interactive activation model of speech perception

James L. McClelland, J. Elman, 1981 .

James L. McClelland, D. Plaut, Mcclelland authors, 1999 .

The role of syntactic context in word recognition

James L. McClelland, George O. Goodman, R. Gibbs, 1981, Memory & cognition.

Understanding failures of learning: Hebbian learning, competition for representational space, and some preliminary experimental data.

James L. McClelland, J A Fiez, J L McClelland, 1999, Progress in brain research.

Connectionist models and psychological evidence

James L. McClelland, 1988 .

Neural models of memory

James L. McClelland, Michael E Hasselmo, James L McClelland, 1999, Current Opinion in Neurobiology.

A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization

James L. McClelland, James L McClelland, G Bard Ermentrout, 2006, Proceedings of the National Academy of Sciences.

Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice

Marius Usher, James L. McClelland, James L McClelland, 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

Unsupervised learning of vowel categories from infant-directed speech

James L. McClelland, James L McClelland, Shigeaki Amano, 2007, Proceedings of the National Academy of Sciences.

Left running head: Fox et al. Right running head: Effects of nitrogen loads on macrophytes Macrophyte abundance in Waquoit Bay: Effects of land-derived nitrogen loads on seasonal and multi-year biomass patterns

James L. McClelland, J. Hauxwell, I. Valiela, 2008 .

Graded Constraints on English Word Forms

James L. McClelland, B. V. Wyk, Brent Vander Wyk, 2006 .

Value-based decision making: An interactive activation perspective.

James L. McClelland, James L McClelland, James J Gross, 2020, Psychological review.

Conjunctive search for one and two identical targets.

James L. McClelland, J L McClelland, R. Ward, 1989, Journal of experimental psychology. Human perception and performance.

Language is not Just for Talking

Gary Lupyan, James L. McClelland, James L McClelland, 2007, Psychological science.

LnTHE TRACE MODEL OF SPEECH PERCEPTION

James L. McClelland, J. Elman, 2022 .

Artificial Intelligence and Psychology Project

James L. McClelland, 1988 .

Running head : HEBBIAN LEARNING How Far Can You Go with Hebbian Learning , and When Does it Lead you Astray ?

James L. McClelland, 2005 .

Combining monitoring data from multiple sites to assess population status and trends of White-tailed Tropicbirds (Phaethon lepturus) in the Seychelles

James L. McClelland, L. Calabrese, April J. Burt, 2021, Avian Conservation and Ecology.

is achieved. Prior to stabilization, neural networks do not jump around between points in activation space. Stabiliza-tion is the process whereby a network first generates a de-terminate activation pattern, and thereby arrives at a point in activation space

Geoffrey E. Hinton, James L. McClelland, D. Rumelhart, 2004 .

‘Words or Rules’ cannot exploit the regularity in exceptions

James L. McClelland, James L McClelland, Karalyn Patterson, 2002, Trends in Cognitive Sciences.

' The Artificial Intelligence and Psychology Project ' % : AUG 2 3 1 W 0

James L. McClelland, 2022 .

A computational cognitive neuroscience perspective on word meaning in context

James L. McClelland, Maja R. Rudolph, Felix Hill, 2018 .

Systematic Human Learning and Generalization From a Brief Tutorial With Explanatory Feedback

James L. McClelland, A. Nam, 2021, Open Mind.

Use of isotopic signatures to assess the food web in a tropical shallow marine ecosystem of Southeastern Brazil

James L. McClelland, I. Valiela, L. Soares, 2006, Aquatic Ecology.

A weighted constraint satisfaction approach to human goal-directed decision making

James L. McClelland, Yuxuan Li, 2021, bioRxiv.

How do we get from propositions to behavior?

James L. McClelland, Daniel A. Sternberg, 2009, Behavioral and Brain Sciences.

Toward a Graded Model of English Phonology

James L. McClelland, B. V. Wyk, 2004 .

Toward a New Grand Theory of Development? Connectionism and Dynamic Systems Theory Reconsidered

James L. McClelland, Larissa K. Samuelson, van Paul Geert, 2009 .

Sentence comprehension: A parallel distributed processing approach

James L. McClelland, R. Taraban, M. John, 1989, Language and Cognitive Processes.

Connectionism : with or without rules ? Response to

James L. McClelland, J. L. McClelland, D. Plaut, 2022 .

The Phonological-Similarity Effect Differentiates Between Two Working Memory Tasks

James L. McClelland, R. Klatzky, J. Fiez, 2002, Psychological science.

Interactive Processing Through Spreading Activation

James L. McClelland, D. Rumelhart, 2017 .

Resilient properties of thought and experience

James L. McClelland, 2015 .

Phenomenology of perception.

James L. McClelland, J. L. Mcclelland, 1978, Science.

Processing determinants of reading speed.

James L. McClelland, M. D. Jackson, L. McClelland, 1979, Journal of experimental psychology. General.

Can native Japanese listeners learn to differentiate /r-l/ on the basis of F3 onset

James L. McClelland, L. Holt, Erin M. Ingvalson, 2011 .

Understanding normal and impaired word reading: Computational principles in quasi-regular domains

James L. McClelland, D. Plaut, M. Seidenberg, 2020, Connectionist psychology: A text with readings.

Enlarging the scope: grasping brain complexity

James L. McClelland, J. A. Scott Kelso, Emmanuelle Tognoli, 2013, Front. Syst. Neurosci..

The somatic marker hypothesis: still many questions but no answers Response to Bechara et al.

James L. McClelland, T. Maia, 2005, Trends in Cognitive Sciences.

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

James L. McClelland, D. Rumelhart, 1986 .

Two Mechanisms of Human Contingency Learning

James L. McClelland, Daniel A. Sternberg, 2011, Psychological Science.

Representations and Computations in Transformers that Support Generalization on Structured Tasks

James L. McClelland, Yuxuan Li, 2023 .

Review of semantic cognition : A parallel distributed processing approach

James L. McClelland, J. F. Glazebrook, F. Glazebrook, 2007 .

Parallel Distributed Processing: Explorations in the Micro-structure of Cognition

James L. McClelland, D. Rumelhart, James L. McClelland, 1989, Journal of Child Language.

Hippocampal conjunctive encoding, storage, and recall: Avoiding a trade‐off

James L. McClelland, R. O’Reilly, L. James, 1994, Hippocampus.

Meta‐analysis of brain‐derived neurotrophic factor p.Val66Met in adult ADHD in four European populations

James L. McClelland, B. Franke, K. Lesch, 2010, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

Meta‐analysis of brain‐derived neurotrophic factor p.Val66Met in adult ADHD in four European populations

James L. McClelland, L. Holt, Erin M. Ingvalson, 2010, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

Approaches to Cognitive Modeling Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition

James L. McClelland, Linda B. Smith, D. Plaut, 2010 .

Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.

James L. McClelland, 2013, Journal of experimental psychology. General.

Semantic cognition: Its nature, its development and its neural basis.

James L. McClelland, K. Patterson, M. L. Ralph, 2009 .

Parallel Distributed Processing: Explorations in the Microstructures of Cognition

James L. McClelland, D. Rumelhart, Geoffrey Sampson, 1987, Language.

Connectionist Models

James L. McClelland, A. Cleeremans, T. Rogers, 2019, The SAGE Encyclopedia of Human Communication Sciences and Disorders.

Developing a domain-general framework for cognition : What is the best approach ?

James L. McClelland, 2003 .

Why Bilateral Damage Is Worse than Unilateral Damage to the Brain

James L. McClelland, A. Schapiro, T. Rogers, 2013, Journal of Cognitive Neuroscience.

Modelling the N400 brain potential as change in a probabilistic representation of meaning

James L. McClelland, S. Hansen, Milena Rabovsky, 2018, Nature Human Behaviour.

Modelling Natural Action Selection: Extending a biologically inspired model of choice: multi-alternatives, nonlinearity, and value-based multidimensional choice

James L. McClelland, R. Bogacz, M. Usher, 2011 .

Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies

James L. McClelland, Alberto Testolin, 2020, Psychonomic Bulletin & Review.

Two Mechanisms of Human Contingency Learning

James L. McClelland, Daniel A. Sternberg, 2011, Psychological science.

A single-system account of semantic and lexical deficits in five semantic dementia patients

James L. McClelland, D. Plaut, Katia Dilkina, 2008, Cognitive neuropsychology.