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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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“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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Mesencephalic dopaminergic unit activity in the behaviorally conditioned rat
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11. The effects of positive affect and arousal on working memory and executive attention
Ashby, F. G., Valentin, V. V., & Turken, A. U. (2002) · Advances in consciousness research · doi:10.1075/aicr.44.11ash · Cited by 160
Self-citationTask-Dependent Selectivity of Movement-Related Neuronal Activity in the Primate Prefrontal Cortex
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Muscarinic modulation of a transient K+ conductance in rat neostriatal neurons
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Maddox, W. T., Ashby, F. G., Ing, A. D., & Pickering, A. D. (2004) · Memory & Cognition · doi:10.3758/bf03195849 · Cited by 152
Self-citationDopamine-Dependent Synaptic Plasticity in the Striatal Cholinergic Interneurons
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Striatal dopamine in motor activation and reward-mediated learning: steps towards a unifying model
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FROST: A Distributed Neurocomputational Model of Working Memory Maintenance
Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005) · Journal of Cognitive Neuroscience · doi:10.1162/089892905774589271 · Cited by 132
Self-citationA Stochastic Version of General Recognition Theory
Ashby, F. G. (2000) · Journal of Mathematical Psychology · doi:10.1006/jmps.1998.1249 · Cited by 130
Self-citationResponses of striatal neurons in the behaving monkey. 2. Visual processing in the caudal neostriatum
Caan, W., Perrett, D. I., & Rolls, E. T. (1984) · Brain Research · doi:10.1016/0006-8993(84)90735-2 · Cited by 127
Global model analysis by parameter space partitioning.
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Evidence for a procedural-learning-based system in perceptual category learning
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Extended habit training reduces dopamine mediation of appetitive response expression
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The role of the basal ganglia in category learning
Ashby, F. G., & Ennis, J. M. (2006) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(06)46001-1 · Cited by 120
Self-citationMonchi, O., Taylor, J. G., & Dagher, A. (2000) · Neural Networks · doi:10.1016/s0893-6080(00)00058-7 · Cited by 120
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Schematic Influences on Category Learning and Recognition Memory.
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Information-Integration Category Learning in Patients With Striatal Dysfunction.
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MULTIPLE SYSTEMS OF PERCEPTUAL CATEGORY LEARNING: THEORY AND COGNITIVE TESTS
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Self-citationDopaminergic regulation of protein phosphorylation in the striatum: DARPP-32
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Human emotion recognition from motion using a radial basis function network architecture
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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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Quantitative modeling of category learning in amnesic patients
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Radial basis function networks for speaker recognition
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Striatal activity in concept learning
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Encoding of Categories by Noncategory-Specific Neurons in the Inferior Temporal Cortex
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Metadata
- 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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