Cited by 286Neural and Behavioral Psychology StudiesMemory and Neural MechanismsMotor Control and Adaptation

The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.

A neurobiological theory of automaticity in perceptual categorization

Ashby, F. Gregory, Ennis, John M., Spiering, Brian J.·2007·Psychological Review

Open this paper in CoChat — free account

Abstract

A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.

Integrity signals

Registry

Crossref · 10.1037/0033-295x.114.3.632

Cites

173 of 173 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,831
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory
Shiffrin, R. M., & Schneider, W. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.127 · Cited by 7,335
The Architecture of Cognition
Anderson, J. R. (2013) · doi:10.4324/9781315799438 · Cited by 6,761
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564
A theory of memory retrieval.
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
Journal of Experimental Psychology: Human Learning and Memory
Bourne, L. E. (1977) · Cited by 4,328
Toward an instance theory of automatization.
Logan, G. D. (1988) · Psychological Review · doi:10.1037/0033-295x.95.4.492 · Cited by 3,466
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309

Show all 173 references

Sorted by citations

Items 201–285 of 285

Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making
Kurzawa, N., Summerfield, C., & Bogacz, R. (2016) · Neural Computation · doi:10.1162/neco_a_00917 · Cited by 3
Category learning in a recurrent neural network with reinforcement learning
Zhang, Y., Pan, X., & Wang, Y. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.1008011 · Cited by 3
Pathways for Naturalistic Looking Behavior in Primate II. Superior Colliculus Integrates Parallel Top-down and Bottom-up Inputs
Veale, R., & Takahashi, M. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.03.001 · Cited by 3
Gender and prior science achievement affect categorization on a procedural learning task
Hong, J., Lu, C.-C., Wang, J.-L., Liao, S., Wu, M.-R., Hwang, M., & Lin, P. (2012) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2012.07.005 · Cited by 4
Finding minimal action sequences with a simple evaluation of actions
Shah, A., & GURNEY, K. N. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00151 · Cited by 3
Computational models inform clinical science and assessment: An application to category learning in striatal-damaged patients
Maddox, W. T., Filoteo, J. V., & Zeithamová, D. (2009) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2009.01.004 · Cited by 3
The neurobiology of categorization
Ashby, F. G., & Crossley, M. J. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199549221.003.05 · Cited by 3
Self-citation
*** Manuscript in press in Cognitive Sciences *** THE COGNITIVE NEUROSCIENCE OF AUTOMATICITY: BEHAVIORAL AND BRAIN SIGNATURES
Hélie, S., & Cousineau, D. (2010) · Cited by 3
Enhanced habit formation in Tourette syndrome: dopamine release and striatal disinhibition modulate shortcut connections in a hierarchical model of cortico-basal ganglia loopsPreprint
Scholl, C., Baladron, J., Vitay, J., & Hamker, F. H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.08.430235 · Cited by 3
The neural basis of cognitive processing: A review and a speculative architecture
Verwey, W. B. (2025) · Brain and Cognition · doi:10.1016/j.bandc.2025.106351 · Cited by 4
Differential contributions of striatal dopaminergic pathways to visuomotor conditional learning
Princz‐Lebel, O., Machado, M. M. F., Skirzewski, M., Rai, H., Panjwani, S., Chu, A., Lemieux, C. A., Palmer, D. D., Derhami, N., Prado, V. F., et al. (2026) · Psychopharmacology · doi:10.1007/s00213-026-07165-x · Cited by 2
On what it means to automatize a rule
Kovacs, P., & Ashby, F. G. (2022) · Cognition · doi:10.1016/j.cognition.2022.105168 · Cited by 2
Self-citation
Modeling the attentional control of vocal utterances:
Roelofs, A. (2023) · Routledge eBooks · doi:10.4324/9781003204213-11 · Cited by 2
Perceptual Learning
Dosher, B. A., & Lu, Z. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.11 · Cited by 2
Altered hemispheric lateralization of cortico-basal ganglia-thalamic network associated with gene expression and neurotransmitter profiles as potential biomarkers for panic disorder
Han, Y., Yan, H., Shan, X., Li, H., Liu, F., Li, P., Yuan, Y., Lv, D., & Guo, W. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111441 · Cited by 2
Simulating Parkinson's disease patient deficits using a COVIS-based computational model
Hélie, S., Paul, E. J., & Ashby, F. G. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033223 · Cited by 2
Self-citation
Dopamine, Learning, and Production Rules: The Basal Ganglia and the Flexible Control of Information Transfer in the Brain
Stocco, A., Lebiere, C. J., & Anderson, J. R. (2018) · doi:10.1184/r1/6614492 · Cited by 2
Semisupervised category learning facilitates the development of automaticity
Vandist, K., Storms, G., & den Bussche, E. V. (2018) · Attention Perception & Psychophysics · doi:10.3758/s13414-018-1595-7 · Cited by 2
Theory of the brain and mind: Visions and history
Levine, D. S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96104-2.00004-x · Cited by 3
Theory of the Brain and Mind
Levine, D. S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-815480-9.00009-8 · Cited by 2
Extensions of Multivariate Dynamical Systems to Simultaneously Explain Neural and Behavioral Data
Liu, Q., Petrov, A. A., Lu, Z., & Turner, B. M. (2020) · Computational Brain & Behavior · doi:10.1007/s42113-020-00072-0 · Cited by 2
A model of reward-modulated motor learning with parallelcortical and basal ganglia pathwaysPreprint
Pyle, R., & Rosenbaum, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.03304 · Cited by 2
Decision Making and Reasoning
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.014 · Cited by 2
Bi-temporal processing in music notation reading: a theory linking prediction, memory, and automaticity
Heath, K. L. (2026) · Frontiers in Cognition · doi:10.3389/fcogn.2025.1689600 · Cited by 1
Hybrid neural–cognitive models reveal how memory shapes human reward learning
Eckstein, M. K., Summerfield, C., Daw, N. D., & Miller, K. J. (2026) · Nature Human Behaviour · doi:10.1038/s41562-025-02324-0 · Cited by 19
(In)accurate Intuition: Fast Reasoning in Decision Making
Andrzejewska, M., Berkay, D., Dreesmann, S., Haslbeck, J. M. B., Mechelmans, D. J., & Furlan, S. (2013) · Journal of European Psychology Students · doi:10.5334/jeps.bf · Cited by 1
Filosofía, medicina y razonamiento clínico
Peña, A. (2009) · Medicina Clínica · doi:10.1016/j.medcli.2009.10.014 · Cited by 1
Filosofía, medicina y razonamiento diagnóstico. Segunda parte
Peña, A. (2010) · Medicina Clínica · doi:10.1016/j.medcli.2009.10.049 · Cited by 1
Computational Modeling of Basic Cognitive Functionalities
(2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.014 · Cited by 1
Challenges from Probabilistic Learning for Models of Brain and Behavior
Marchant, N., Canessa, E., & Chaigneau, S. E. (2023) · STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health · doi:10.1007/978-3-031-41862-4_6 · Cited by 1
The subcortex as a trainer for cortical automaticity
Nafcha, O., & Gabay, S. (2024) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2024.101371 · Cited by 2
Category Learning
Seger, C. A., & Braunlich, K. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00274-8 · Cited by 1
Participation of visual association areas in social processing emerges when rTPJ is inhibited
Hevia-Orozco, J. C., Reyes-Aguilar, A., Pasaye, E. H., & Barrios, F. A. (2022) · eNeurologicalSci · doi:10.1016/j.ensci.2022.100407 · Cited by 1
Computational Investigations of Learning and Synchronization in Cognitive Control
Huycke, P., Lesage, E., Boehler, C. N., & Verguts, T. (2022) · Journal of Cognition · doi:10.5334/joc.239 · Cited by 3
Computational Cognitive models of Categorization: Predictions under Conditions of Classification Uncertainty
Marchant, N., & Chaigneau, S. E. (2022) · Psykhe (Santiago) · doi:10.7764/psykhe.2021.37971 · Cited by 1
Computational Cognitive Neuroscience Models of Categorization
Ashby, F. G., & Wang, Y.-W. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.016 · Cited by 1
Self-citation
Tests of a Prior Marksmanship Knowledge Predictor Test
Lipinski, J. J., James, D. R., & Wampler, R. L. (2014) · doi:10.21236/ada597406 · Cited by 1
Heike Behrens and Stefan Pfänder: Experience Counts: Frequency Effects in Language
Kapatsinski, V. M. (2017) · Cognitive Linguistics · doi:10.1515/cog-2016-0097 · Cited by 1
Dissociating Implicit and Explicit Category Learning Systems using Confidence Reports
Schoenherr, J. R. (2014) · doi:10.22215/etd/2014-10362 · Cited by 1
Biomimetic model of corticostriatal micro-assemblies discovers new neural codePreprint
Pathak, A., Brincat, S. L., Organtzidis, H., Strey, H. H., Senneff, S., Antzoulatos, E. G., Mujica-Parodi, L. R., Miller, E. K., & Granger, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.06.565902 · Cited by 1
Modeling Category Learning
Rodrigues, P. M. (2008) · UvA-DARE (University of Amsterdam)
Neurally Inspired Models of Psychological Processes
Mercado, E., & Henderson, C. M. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop203023
Interactions between global and local performance incentives on decision-making and categorization
Worthy, D. A. (2010) · Texas ScholarWorks (Texas Digital Library)
How people learn features in the absence of classification error
Chen, L., Mo, L., & Bott, L. A. (2014) · Journal of Cognitive Psychology · doi:10.1080/20445911.2014.965712
Investigating the molecular substrates of goal-directed and habitual behaviour
Powell, A. L. (2013) · ORCA Online Research @Cardiff (Cardiff University)
ARE NEUROREDUCTIONIST EXPLANATIONS OF COGNITION POSSIBLE
Uttal, W. R. (2014)
Automaticity as a predictor of skill transfer
van Melis, J. J. M. (2010) · Australasian Journal of Paramedicine
Computational Modeling of the Basal Ganglia : Functional Pathways and Reinforcement Learning
Berthet, P. (2015) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
**** Manuscript in press in Neuropsychologia **** A neurocomputational account of cognitive deficits in Parkinson's disease
Hélie, S., Paul, E. J., & Ashby, F. G. (2012)
Self-citation
Comparative Applied Perception Research: The Case of Working Dogs
Helton, W. S., & Helton, N. D. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9780511973017.062
The Interpretive Significance of Pathognomonic Signs
Koziol, L. F., Beljan, P., Bree, K., Mather, J., & Barker, L. A. (2016) · Springer eBooks · doi:10.1007/978-3-319-28222-0_5
Macroscopic Neural Theories of Cognition
Uttal, W. R. (2014) · Defense Technical Information Center (DTIC) · doi:10.21236/ada597175
Psychological and Neuroscientific Connections with Reinforcement Learning (preprint)
Shah, A. (2014)
The Nucleus Accumbens Core Dopamine D1 and Glutamate AMPA/NMDA Receptors Play a Transient Role in the Performance of Pavlovian Approach Behavior
Dobrovitsky, V. (2017) · CUNY Academic Works (City University of New York)
Neurocomputational models of corticostriatal interactions in action selection
Caso, A. (2019) · BIROn (Birkbeck, University of London) · doi:10.18743/pub.00040446
Sensory and Working Memory
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.006
Long-Term Memory Structure
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.007
Long-Term Memory Processes
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.008
Temperament and Visual Category Learning Strategy UsePreprint
Zhu, T., & Minda, J. P. (2021) · doi:10.31234/osf.io/5tpxw
Current World Literature
(2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32831cbb85
Preface
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.001
Problem Solving
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.015
Memory Retrieval
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.009
Categorization
Mack, M. L., Richler, J. J., Palmeri, T. J., & Gauthier, L. (2009) · Wiley eBooks · doi:10.1002/9780470478509.neubb001021
Glossary
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.016
Cognitive Psychologists’ Approach to Research
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.002
Visual Perception
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.004
Visual Imagery
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.013
Language Processing
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.012
Language Structure
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.011
Attention
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.005
References
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.017
Methods of Cognitive Psychology
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.003
Explicit and Procedural-Learning-Based Systems of Perceptual Category Learning
Ell, S. W., Hutchinson, S., & Maddox, W. T. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_1736
Faculty Opinions recommendation of Habits without values.
Milton, A. L. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734918397.793583295
Exemplar Learning and Schematization in Language Development
Abbot‐Smith, K., & Kidd, E. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_896 · Cited by 1
Index
Willingham, D. T., & Riener, C. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316271988.018
Unstructured Categorization with Probabilistic Feedback: Learning Accuracy Versus Response Time
Genova, B., Bocheva, N. B., & Stefanova, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15037-1_9
The Role of the Striatum in Motor Learning
Ivlieva, N. Y. (2022) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-023-01351-6
An fMRI study of inflectional encoding in spoken word production: Role of domain-general inhibition
Ferreira, J., Roelofs, A., Freches, G. B., & Piai, V. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108653
Interactions between the medial prefrontal cortex, dorsomedial striatum, and dorsal hippocampus that support rat category learningPreprint
Broschard, M. B., Kim, J., Halverson, H. E., Farley, S. J., & Freeman, J. H. (2025) · bioRxiv · doi:10.1101/2025.04.21.649785 · Cited by 1
Rules in the mist: Emerging probabilistic rules in uncertain categorization
Marchant, N., Puebla, G., & Chaigneau, S. E. (2025) · Cognition · doi:10.1016/j.cognition.2025.106264
An Exploratory Randomised Trial of a Self-Managed Home-Based Exaggerated Spatial Cueing Intervention for Handwriting in Parkinson’s Disease
Andreoli, D., Reed, A., Coe, S., Dawes, H., & Collett, J. (2025) · Disabilities · doi:10.3390/disabilities5040093
Astrocytic Receptor Systems of the Basal Ganglia
Tushevski, A., Happe, L., Stocco, E., De, R., Macchi, V., Porzionato, A., & Emmi, A. (2025) · Receptors · doi:10.3390/receptors5010002
Etude des dynamiques neurales cortico-ganglions de la base pendant le sommeil chez les oiseaux chanteurs avec une méthodologie inspirée par la Science Ouverte
Centeno, E. G. Z. (2024) · theses.fr (ABES) · doi:10.70675/09e98f5azaa65z4426zaecazc59064e5c7ca

Authors: Ashby, F. G., Ennis, J. M., & Spiering, B. J.
Affiliations: University of California, Santa Barbara (US)
ORCID: Ashby, F. Gregory
Venue: Psychological Review
Publisher: American Psychological Association (APA)
Published: 2007
Volume: 114
Issue: 3
Pages: 632-656
ISSN: 0033-295X (ISSN-L)
1939-1471
DOI: 10.1037/0033-295x.114.3.632
OpenAlex: W2140983215
Semantic Scholar: 5b1560d4cffb0c1274e826f8cb9d7112c08517c0
Cited by: 286
References: 173
Citations by year: 2026: 6 · 2025: 8 · 2024: 7 · 2023: 14 · 2022: 12 · 2021: 8 · 2020: 4 · 2019: 30 · 2018: 9 · 2017: 15 · 2016: 13 · 2015: 22 · 2014: 23 · 2013: 20 · 2012: 20
FWCI: 9.32
Type: journal article
Language: en
Open access: closed
Locations: Psychological Review (publishedVersion)
PubMed (publishedVersion)
Funders: Public Health Service (MH3760-2)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords: automaticity, perceptual categorization, procedural learning, speed model, subcortical pathways, premotor cortex, striatum, globus pallidus, thalamus, dopamine, substantia nigra, Hebbian learning, neuroplasticity, response selection, expertise development, computational neuroscience
MeSH terms: Animals, Attention, Automatism, Brain, Brain Mapping, Cerebral Cortex, Concept Formation, Corpus Striatum, Dominance, Cerebral, Dopamine, Globus Pallidus, Habits, Humans, Neural Pathways, Neuronal Plasticity, Pattern Recognition, Visual, Perception, Reaction Time, Thalamus, Touch, Neural Networks, Computer, Long-Term Potentiation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A neurobiological theory of automaticity in perceptual categorization

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks