A neurobiological theory of automaticity in perceptual categorization
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.
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The Ashby et al. model accounts for results showing that automatic behaviors are striatum- and dopamine-independent, but it does not account for results showing activation of sensorimotor striatum after automaticity has developed.
“are striatum- and dopamine-independent, but it does not account for the results reviewed earlier showing activation of sensorimotor striatum after automaticity has developed.”
From Cortical and basal ganglia contributions to habit learning and automaticity (2010) · cites this paper as [48]
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Self-citationA Stochastic Version of General Recognition Theory
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Self-citationResponses of striatal neurons in the behaving monkey. 2. Visual processing in the caudal neostriatum
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The role of the basal ganglia in category learning
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MULTIPLE SYSTEMS OF PERCEPTUAL CATEGORY LEARNING: THEORY AND COGNITIVE TESTS
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The Neurobiology of Category Learning
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Self-citationFunctional Properties of Primate Putamen Neurons During the Categorization of Tactile Stimuli
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Cortical and basal ganglia contributions to habit learning and automaticity
Ashby, F. G., Turner, B. O., & Horvitz, J. C. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.02.001 · Cited by 481 · see citing works
Self-citationCategory Learning in the Brain
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Basal ganglia contributions to motor control: a vigorous tutor
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Dual processes in decision making and developmental neuroscience: A fuzzy-trace model
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Stimulus–response bindings in priming
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Dopaminergic Mechanisms in Actions and Habits: Figure 1.
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The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment
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The Dreyfus model of clinical problem-solving skills acquisition: a critical perspective
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The Neurocognition of Developmental Disorders of Language
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Speech perception as an active cognitive process
Heald, S. L. M., & Nusbaum, H. C. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00035 · Cited by 192 · see citing works
Summerfield, C., & Tsetsos, K. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00070 · Cited by 167 · see citing works
Computational models of motivated action selection in corticostriatal circuits
Frank, M. J. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.02.013 · Cited by 175 · see citing works
Learning robust cortico-cortical associations with the basal ganglia: An integrative review
Hélie, S., Ell, S. W., & Ashby, F. G. (2014) · Cortex · doi:10.1016/j.cortex.2014.10.011 · Cited by 172 · see citing works
Self-citationA critical review of habit learning and the basal ganglia
Seger, C. A., & Spiering, B. J. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00066 · Cited by 170 · see citing works
Increases in Functional Connectivity between Prefrontal Cortex and Striatum during Category Learning
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Adaptive effort investment in cognitive and physical tasks: A neurocomputational model
Verguts, T., Vassena, E., & Silvetti, M. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00057 · Cited by 118 · see citing works
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy
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Mesolimbic confidence signals guide perceptual learning in the absence of external feedback
Guggenmos, M., Wilbertz, G., Hebart, M. N., & Sterzer, P. (2016) · eLife · doi:10.7554/elife.13388 · Cited by 164 · see citing works
Implicit and explicit categorization: A tale of four species
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Self-citationThe dynamics of insight: Mathematical discovery as a phase transition
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Tinnitus modulation by deep brain stimulation in locus of caudate neurons (area LC)
Cheung, S. W., & Larson, P. (2010) · Neuroscience · doi:10.1016/j.neuroscience.2010.06.007 · Cited by 120 · see citing works
Toward an integration of deep learning and neuroscience
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Tool-Making New Caledonian Crows Have Large Associative Brain Areas
Mehlhorn, J., Hunt, G., Gray, R., Rehkámper, G., & Güntürkün, O. (2010) · Brain, Behavior and Evolution · doi:10.1159/000295151 · Cited by 111 · see citing works
Caligiore, D., Arbib, M. A., Miall, R. C., & Baldassarre, G. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.02.008 · Cited by 105 · see citing works
A Reinforcement Learning Mechanism Responsible for the Valuation of Free Choice
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Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream
Chevillet, M. A., Jiang, X., Rauschecker, J. P., & Riesenhuber, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1870-12.2013 · Cited by 103 · see citing works
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Tracing the Trajectory of Sensory Plasticity across Different Stages of Speech Learning in Adulthood
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The visual corticostriatal loop through the tail of the caudate: circuitry and function
Seger, C. A. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2013.00104 · Cited by 95 · see citing works
A Review on the Role of the Neuroscience of Flow States in the Modern World
Gold, J., & Ciorciari, J. (2020) · Behavioral Sciences · doi:10.3390/bs10090137 · Cited by 99 · see citing works
Queuing network modeling of the psychological refractory period (PRP).
Wu, C., & Liu, Y. (2008) · Psychological Review · doi:10.1037/a0013123 · Cited by 94 · see citing works
Cortical and striatal contributions to automaticity in information-integration categorization
Waldschmidt, J. G., & Ashby, F. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.011 · Cited by 86 · see citing works
Self-citationVenkatesh, V., Davis, F. D., & Zhu, Y. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102644 · Cited by 95 · see citing works
Handbook of Reward and Decision Making
Dreher, J., & Tremblay, L. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.x0001-5 · Cited by 88 · see citing works
When more is less: Feedback effects in perceptual category learning
Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008) · Cognition · doi:10.1016/j.cognition.2008.03.010 · Cited by 86 · see citing works
Evidence for Cortical Automaticity in Rule-Based Categorization
Hélie, S., Roeder, J. L., & Ashby, F. G. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2393-10.2010 · Cited by 85 · see citing works
Self-citationIntegration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia
Bogacz, R., & Larsen, T. (2011) · Neural Computation · doi:10.1162/neco_a_00103 · Cited by 83 · see citing works
Automaticity in rule-based and information-integration categorization
Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010) · Attention Perception & Psychophysics · doi:10.3758/app.72.4.1013 · Cited by 83 · see citing works
Self-citationPigeons’ categorization may be exclusively nonanalytic
Smith, J. D., Ashby, F. G., Berg, M. E., Murphy, M. S., Spiering, B. J., Cook, R. G., & Grace, R. C. (2011) · Psychonomic Bulletin & Review · doi:10.3758/s13423-010-0047-8 · Cited by 77 · see citing works
Self-citationHow may the basal ganglia contribute to auditory categorization and speech perception?
Lim, S.-J., Fiez, J. A., & Holt, L. L. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00230 · Cited by 75 · see citing works
On the adequacy of current empirical evaluations of formal models of categorization.
Wills, A. J., & Pothos, E. M. (2011) · Psychological Bulletin · doi:10.1037/a0025715 · Cited by 75 · see citing works
Deferred Feedback Sharply Dissociates Implicit and Explicit Category Learning
Smith, J. D., Boomer, J. B., Zakrzewski, A. C., Roeder, J. L., Church, B. A., & Ashby, F. G. (2013) · Psychological Science · doi:10.1177/0956797613509112 · Cited by 73 · see citing works
Self-citationHabit learning and memory in mammals: Behavioral and neural characteristics
Gasbarri, A., Pompili, A., Packard, M. G., & Tomaz, C. A. B. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.06.010 · Cited by 72 · see citing works
Hélie, S., Chakravarthy, V. S., & Moustafa, A. A. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00174 · Cited by 71 · see citing works
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Rule-based and information-integration category learning in normal aging
Maddox, W. T., Pacheco, J. L., Reeves, M., Zhu, B., & Schnyer, D. M. (2010) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2010.06.008 · Cited by 71 · see citing works
Schroll, H., Vitay, J., & Hamker, F. H. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.10.008 · Cited by 67 · see citing works
Is memory organized by temporal contiguity?
Hintzman, D. L. (2015) · Memory & Cognition · doi:10.3758/s13421-015-0573-8 · Cited by 67 · see citing works
Multiple Systems of Perceptual Category Learning
Ashby, F. G., & Valentin, V. V. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-101107-2.00007-5 · Cited by 66 · see citing works
Self-citationStriatal degeneration impairs language learning: evidence from Huntington's disease.
de Diego-Balaguer, R., Couette, M., Dolbeau, G., Dürr, A., Youssov, K., & Bachoud-Lévi, A. (2008) · Brain : a journal of neurology · doi:10.1093/brain/awn242 · Cited by 69 · see citing works
Automaticity and multiple memory systems
Ashby, F. G., & Crossley, M. J. (2012) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1172 · Cited by 62 · see citing works
Self-citationKoziol, L. F., Budding, D. E., & Chidekel, D. (2010) · The Cerebellum · doi:10.1007/s12311-010-0192-7 · Cited by 65 · see citing works
The neural bases of the learning and generalization of morphological inflection
Nevat, M., Ullman, M. T., Eviatar, Z., & Bitan, T. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.08.026 · Cited by 62 · see citing works
A neural interpretation of exemplar theory.
Ashby, F. G., & Rosedahl, L. A. (2017) · Psychological Review · doi:10.1037/rev0000064 · Cited by 62 · see citing works
Self-citationAdi‐Japha, E., Karni, A., Parnes, A., Loewenschuss, I., & Vakil, E. (2008) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0013217 · Cited by 62 · see citing works
Delayed motor skill acquisition in kindergarten children with language impairment
Adi‐Japha, E., Strulovich-Schwartz, O., & Julius, M. S. (2011) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2011.05.005 · Cited by 63 · see citing works
Analogical transfer in perceptual categorization
Casale, M. B., Roeder, J. L., & Ashby, F. G. (2011) · Memory & Cognition · doi:10.3758/s13421-011-0154-4 · Cited by 61 · see citing works
Self-citationA Computational Model of How Cholinergic Interneurons Protect Striatal-dependent Learning
Ashby, F. G., & Crossley, M. J. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21523 · Cited by 57 · see citing works
Self-citationDysfunctional and compensatory synaptic plasticity in P arkinson's disease
Schroll, H., Vitay, J., & Hamker, F. H. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12434 · Cited by 59 · see citing works
Boraud, T., Leblois, A., & Rougier, N. P. (2018) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2018.08.003 · Cited by 56 · see citing works
A tutorial on computational cognitive neuroscience: Modeling the neurodynamics of cognition
Ashby, F. G., & Hélie, S. (2011) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2011.04.003 · Cited by 57 · see citing works
Self-citationTriggering prepared actions by sudden sounds: reassessing the evidence for a single mechanism
Marinovic, W., & Tresilian, J. R. (2015) · Acta Physiologica · doi:10.1111/apha.12627 · Cited by 56 · see citing works
Role of the striatum in incidental learning of sound categories
Lim, S.-J., Fiez, J. A., & Holt, L. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811992116 · Cited by 55 · see citing works
Striatum in stimulus–response learning via feedback and in decision making
Hiebert, N. M., Vo, A., Hampshire, A., Owen, A. M., Seergobin, K. N., & MacDonald, P. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.013 · Cited by 54 · see citing works
On the normative advantages of dopamine and striatal opponency for learning and choice
Jaskir, A., & Frank, M. J. (2023) · eLife · doi:10.7554/elife.85107 · Cited by 53 · see citing works
Curriculum learning for human compositional generalization
Dekker, R., Otto, F., & Summerfield, C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205582119 · Cited by 56 · see citing works
Prefrontal contributions to rule-based and information-integration category learning
Schnyer, D. M., Maddox, W. T., Ell, S. W., Davis, S., Pacheco, J., & Verfaellie, M. H. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.07.011 · Cited by 51 · see citing works
Rasmussen, D., & Eliasmith, C. (2013) · Intelligence · doi:10.1016/j.intell.2013.10.003 · Cited by 49 · see citing works
Selection of cortical dynamics for motor behaviour by the basal ganglia
Mannella, F., & Baldassarre, G. (2015) · Biological Cybernetics · doi:10.1007/s00422-015-0662-6 · Cited by 48 · see citing works
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511921322.004 · Cited by 47 · see citing works
Self-citationFrontoparietal networks involved in categorization and item working memory
Braunlich, K., Gomez‐Lavin, J., & Seger, C. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.051 · Cited by 47 · see citing works
Response processes in information–integration category learning
Spiering, B. J., & Ashby, F. G. (2008) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2008.04.015 · Cited by 48 · see citing works
Self-citationStocco, A., Sibert, C., Steine-Hanson, Z., Koh, N., Laird, J. E., Lebiere, C. J., & Rosenbloom, P. S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118035 · Cited by 45 · see citing works
Tikhonova, M. A., Ho, S.-C., Akopyan, A. A., Kolosova, N. G., Weng, J., Meng, W.-Y., Lin, C., Amstislavskaya, T. G., & Ho, Y. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.05.002 · Cited by 42 · see citing works
Lévy-like diffusion in eye movements during spoken-language comprehension
Kelty‐Stephen, D. G., Mirman, D., Magnuson, J. S., & Dixon, J. A. (2009) · Physical Review E · doi:10.1103/physreve.79.056114 · Cited by 41 · see citing works
Moutsopoulou, K., Yang, Q., Desantis, A., & Waszak, F. (2014) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2014.984232 · Cited by 41 · see citing works
The effects of concurrent verbal and visual tasks on category learning.
Miles, S. J., & Minda, J. P. (2011) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0022309 · Cited by 40 · see citing works
Impaired and Spared Auditory Category Learning in Developmental Dyslexia
Gabay, Y., Roark, C. L., & Holt, L. L. (2023) · Psychological Science · doi:10.1177/09567976231151581 · Cited by 38 · see citing works
Realigning Models of Habitual and Goal-Directed Decision-Making
Miller, K. J., Ludvig, E. A., Pezzulo, G., & Shenhav, A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812098-9.00018-8 · Cited by 41 · see citing works
Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory
Cantwell, G. T., Crossley, M. J., & Ashby, F. G. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0827-2 · Cited by 38 · see citing works
Self-citationImplicit and explicit category learning by capuchin monkeys (Cebus apella).
Smith, J. D., Crossley, M. J., Boomer, J. B., Church, B. A., Beran, M. J., & Ashby, F. G. (2011) · Journal of comparative psychology · doi:10.1037/a0026031 · Cited by 37 · see citing works
Self-citationHsieh, M.-H., Meng, W.-Y., Liao, W.-C., Weng, J.-C., Li, H.-H., Su, H.-L., Lin, C.-L., Hung, C.-S., & Ho, Y.-J. (2017) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2017.05.015 · Cited by 35 · see citing works
Moutsopoulou, K., & Waszak, F. (2011) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0025858 · Cited by 36 · see citing works
The Involvement of Corticostriatal Loops in Learning Across Tasks, Species, and Methodologies
Seger, C. A. (2009) · Advances in behavioral biology · doi:10.1007/978-1-4419-0340-2_2 · Cited by 36 · see citing works
A neurocomputational account of cognitive deficits in Parkinson's disease
Hélie, S., Paul, E. J., & Ashby, F. G. (2012) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2012.05.033 · Cited by 34 · see citing works
Self-citationA Biologically Inspired Computational Model of Basal Ganglia in Action Selection
Baston, C., & Ursino, M. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/187417 · Cited by 33 · see citing works
Wenger, M. J., Murray‐Kolb, L. E., Nevins, J. E. H., Venkatramanan, S., Reinhart, G. A., Wesley, A. S., & Haas, J. D. (2017) · Journal of Nutrition · doi:10.3945/jn.117.251587 · Cited by 33 · see citing works
Time course of visual object categorization: Fastest does not necessarily mean first
Mack, M. L., Wong, A. C. ‐., Gauthier, L., Tanaka, J. W., & Palmeri, T. J. (2009) · Vision Research · doi:10.1016/j.visres.2009.05.005 · Cited by 32 · see citing works
Immink, M. A., Verwey, W. B., & Wright, D. L. (2020) · Cognitive science and technology · doi:10.1007/978-3-030-34784-0_12 · Cited by 30 · see citing works
Hélie, S., Paul, E. J., & Ashby, F. G. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.033 · Cited by 32 · see citing works
Self-citationBasal Ganglia dysfunctions in movement disorders: What can be learned from computational simulations
Schroll, H., & Hamker, F. H. (2016) · Movement Disorders · doi:10.1002/mds.26719 · Cited by 30 · see citing works
Stocco, A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00018 · Cited by 30 · see citing works
Acquired equivalence and related memory processes in migraine without aura
Őze, A., Nagy, A., Benedek, G., Bodosi, B., Kéri, S., Pálinkás, É., Bihari, K., & Braunitzer, G. (2016) · Cephalalgia · doi:10.1177/0333102416651286 · Cited by 30 · see citing works
- Authors
- Ashby, F. G., Ennis, J. M., & Spiering, B. J.
- Affiliations
- University of California, Santa Barbara (US)
- ORCID
- Ashby, F. Gregory
- Venue
- Psychological Review
- Published
- 2007
- Volume
- 114
- Issue
- 3
- Pages
- 632-656
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- 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.
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