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
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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Cites
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
Shiffrin, R. M., & Schneider, W. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.127 · Cited by 7,335
Anderson, J. R. (2013) · doi:10.4324/9781315799438 · Cited by 6,761
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
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
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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
Self-citationCategory Learning in the Brain
Seger, C. A., & Miller, E. K. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135546 · Cited by 483
Basal ganglia contributions to motor control: a vigorous tutor
Turner, R. S., & Desmurget, M. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.08.022 · Cited by 441
Stocco, A., Lebiere, C. J., & Anderson, J. R. (2010) · Psychological Review · doi:10.1037/a0019077 · Cited by 383
Dual processes in decision making and developmental neuroscience: A fuzzy-trace model
Reyna, V. F., & Brainerd, C. J. (2011) · Developmental Review · doi:10.1016/j.dr.2011.07.004 · Cited by 393
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
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