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12 - Computational Cognitive Neuroscience Models of Categorization

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Categorization is the process of assigning an object or event to a behaviorally relevant group. Before the 1990s, almost nothing was known about the neural networks and processes that mediate human categorization. As a result, theories of categorization were dominated by purely cognitive descriptions. The cognitive neuroscience revolution dramatically increased our understanding of the neural bases of human categorization. As a result, models grounded in neuroscience are becoming increasingly popular. Virtually all of these models assume that different neural systems mediate learning in different types of categorization tasks. Collectively, these models have already made profound contributions to our understanding of human categorization, by widening the empirical domain of categorization research, and by motivating experiments that might not otherwise have been run. Furthermore, this trend should increase in the future, as methods for studying the functioning human brain improve and the database of human brain function during categorization grows.

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Publisher: Cambridge University Press
Print publication year: 2023

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References

Aizenstein, H. J., MacDonald, A. W., Stenger, V. A., et al. (2000). Complementary category learning systems identified using event-related functional MRI. Journal of Cognitive Neuroscience, 12(6), 977987.Google Scholar
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9(1), 357381.CrossRefGoogle ScholarPubMed
Amos, A. (2000). A computational model of information processing in the frontal cortex and basal ganglia. Journal of Cognitive Neuroscience, 12(3), 505519.CrossRefGoogle ScholarPubMed
Apicella, P., Legallet, E., & Trouche, E. (1997). Responses of tonically discharging neurons in the monkey striatum to primary rewards delivered during different behavioral states. Experimental Brain Research, 116(3), 456466.CrossRefGoogle ScholarPubMed
Arbuthnott, G., Ingham, C., & Wickens, J. (2000). Dopamine and synaptic plasticity in the neostriatum. Journal of Anatomy, 196(4), 587596.CrossRefGoogle ScholarPubMed
Ashby, F. G. (2018). Computational cognitive neuroscience. In Batchelder, W., Colonius, H., Dzhafarov, E., & Myung, J. (Eds.), New Handbook of Mathematical Psychology (vol. 2, pp. 223270). New York, NY: Cambridge University Press.Google Scholar
Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105(3), 442481.Google Scholar
Ashby, F. G., & Casale, M. B. (2003). The cognitive neuroscience of implicit category learning. In Jiménez, L. (Ed.), Attention and Implicit Learning, (vol. 48, pp. 109142). New York, NY: John Benjamins Publishing Company.Google Scholar
Ashby, F. G., & Crossley, M. J. (2011). A computational model of how cholinergic interneurons protect striatal-dependent learning. Journal of Cognitive Neuroscience, 23(6), 15491566.CrossRefGoogle ScholarPubMed
Ashby, F. G., & Crossley, M. J. (2012). Automaticity and multiple memory systems. Wiley Interdisciplinary Reviews Cognitive Science, 3(3), 363376.Google Scholar
Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005). FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17(11), 17281743.Google Scholar
Ashby, F. G., & Ennis, J. M. (2006). The role of the basal ganglia in category learning. Psychology of Learning and Motivation, 46, 136.Google Scholar
Ashby, F. G., Ennis, J. M., & Spiering, B. J. (2007). A neurobiological theory of automaticity in perceptual categorization. Psychological Review, 114(3), 632656.Google Scholar
Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 3353.Google ScholarPubMed
Ashby, F. G., Isen, A. M., & Turken, A. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106(3), 529550.Google Scholar
Ashby, F. G., & O’Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 2, 8389.Google Scholar
Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). COVIS. In Pothos, E. M. & Wills, A. (Eds.), Formal Approaches in Categorization (pp. 6587). New York, NY: Cambridge University Press.Google Scholar
Ashby, F. G., & Rosedahl, L. (2017). A neural interpretation of exemplar theory. Psychological Review, 124(4), 472482.Google Scholar
Ashby, F. G., & Valentin, V. V. (2017). Multiple systems of perceptual category learning: theory and cognitive tests. In Cohen, H. & Lefebvre, C. (Eds.), Handbook of Categorization in Cognitive Science, 2nd ed. (pp. 157188). Amsterdam: Elsevier.CrossRefGoogle Scholar
Ashby, F. G., & Valentin, V. V. (2018). The categorization experiment: experimental design and data analysis. In Wagenmakers, E. J. & Wixted, J. T. (Eds.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 4th ed., vol. 5: Methodology (pp. 307347). New York, NY: Wiley.Google Scholar
Ashby, F. G., & Waldron, E. M. (1999). On the nature of implicit categorization. Psychonomic Bulletin & Review, 6(3), 363378.Google Scholar
Asmus, F., Huber, H., Gasser, T., & Schöls, L. (2008). Kick and rush paradoxical kinesia in parkinson disease. Neurology, 71(9), 695.CrossRefGoogle ScholarPubMed
Bennett, B. D., & Wilson, C. J. (2000). Synaptology and physiology of neostriatal neurones. In Miller, R. & Wickens, J. R. (Eds.), Brain Dynamics and the Striatal Complex (pp. 111140). Amsterdam: Harwood Academic Publishers.Google Scholar
Braver, T. S., Cohen, J. D., Nystrom, L. E., Jonides, J., Smith, E. E., & Noll, D. C. (1997). A parametric study of prefrontal cortex involvement in human working memory. Neuroimage, 5(1), 4962.Google Scholar
Bunge, S. A. (2004). How we use rules to select actions: a review of evidence from cognitive neuroscience. Cognitive, Affective, & Behavioral Neuroscience, 4(4), 564579.Google Scholar
Calabresi, P., Pisani, A., Mercuri, N. B., & Bernardi, G. (1996). The corticostriatal projection: from synaptic plasticity to dysfunctions of the basal ganglia. Trends in Neurosciences, 19(1), 1924.CrossRefGoogle ScholarPubMed
Cantwell, G., Crossley, M. J., & Ashby, F. G. (2015). Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory. Psychonomic Bulletin & Review, 22(6), 15981613.Google Scholar
Cantwell, G., Riesenhuber, M., Roeder, J. L., & Ashby, F. G. (2017). Perceptual category learning and visual processing: an exercise in computational cognitive neuroscience. Neural Networks, 89, 3138.Google Scholar
Casale, M. B., & Ashby, F. G. (2008). A role for the perceptual representation memory system in category learning. Perception & Psychophysics, 70(6), 983999.Google Scholar
Chersi, F., Mirolli, M., Pezzulo, G., & Baldassarre, G. (2013). A spiking neuron model of the cortico-basal ganglia circuits for goal-directed and habitual action learning. Neural Networks, 41, 212224.Google Scholar
Cools, R. (2006). Dopaminergic modulation of cognitive function-implications for l-dopa treatment in Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 30(1), 123.CrossRefGoogle ScholarPubMed
Cools, R., Lewis, S. J., Clark, L., Barker, R. A., & Robbins, T. W. (2007). L-dopa disrupts activity in the nucleus accumbens during reversal learning in Parkinson’s disease. Neuropsychopharmacology, 32(1), 180189.CrossRefGoogle ScholarPubMed
Crossley, M. J., Ashby, F. G., & Maddox, W. T. (2013). Erasing the engram: the unlearning of procedural skills. Journal of Experimental Psychology: General, 142(3), 710741.CrossRefGoogle ScholarPubMed
Crossley, M. J., Ashby, F. G., & Maddox, W. T. (2014). Context-dependent savings in procedural category learning. Brain & Cognition, 92, 110.Google Scholar
Crossley, M. J., Horvitz, J. C., Balsam, P. D., & Ashby, F. G. (2016). Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning. Journal of Neurophysiology, 115, 240254.Google Scholar
Crossley, M. J., Madsen, N. R., & Ashby, F. G. (2012). Procedural learning of unstructured categories. Psychonomic Bulletin & Review, 19(6), 12021209.Google Scholar
Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7(9), 415423.Google Scholar
Davis, T., Love, B. C., & Preston, A. R. (2011). Learning the exception to the rule: model-based fMRI reveals specialized representations for surprising category members. Cerebral Cortex, 22(2), 260273.Google Scholar
Desmurget, M., & Turner, R. S. (2010). Motor sequences and the basal ganglia: kinematics, not habits. The Journal of Neuroscience, 30(22), 76857690.Google Scholar
Dunn, J. C., Newell, B. R., & Kalish, M. L. (2012). The effect of feedback delay and feedback type on perceptual category learning: the limits of multiple systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(4), 840859.Google ScholarPubMed
Eichenbaum, H., & Cohen, N. J. (2001). From Conditioning to Conscious Recollection: Memory Systems of the Brain. Oxford: Oxford University Press.Google Scholar
Engel, T. A., Chaisangmongkon, W., Freedman, D. J., & Wang, X.-J. (2015). Choice-correlated activity fluctuations underlie learning of neuronal category representation. Nature Communications, 6, 6454.Google Scholar
Estes, W. K. (1986). Array models for category learning. Cognitive Psychology, 18(4), 500549.Google Scholar
Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review of Neuroscience, 32, 3355.CrossRefGoogle ScholarPubMed
Filoteo, J. V., Maddox, W. T., Salmon, D. P., & Song, D. D. (2005). Information-integration category learning in patients with striatal dysfunction. Neuropsychology, 19(2), 212222.Google Scholar
Filoteo, J. V., Paul, E. J., Ashby, F. G., et al. (2014). Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa. Neuropsychology, 28(5), 741751.CrossRefGoogle ScholarPubMed
Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120(3), 497517.Google Scholar
Heaton, R. K. (1981). Wisconsin Card Sorting Test Manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Hélie, S., Paul, E. J., & Ashby, F. G. (2012a). A neurocomputational account of cognitive deficits in Parkinson’s disease. Neuropsychologia, 50(9), 22902302.Google Scholar
Hélie, S., Paul, E. J., & Ashby, F. G. (2012b). Simulating the effects of dopamine imbalance on cognition: from positive affect to Parkinson’s disease. Neural Networks, 32, 7485.Google Scholar
Helie, S., Roeder, J. L., Vucovich, L., Rünger, D., & Ashby, F. G. (2015). A neurocomputational model of automatic sequence production. Journal of Cognitive Neuroscience, 27(7), 14561469.Google Scholar
Hélie, S., Waldschmidt, J. G., & Ashby, F. G. (2010). Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics, 72(4), 10131031.Google Scholar
Hopkins, R. O., Myers, C. E., Shohamy, D., Grossman, S., & Gluck, M. (2004). Impaired probabilistic category learning in hypoxic subjects with hippocampal damage. Neuropsychologia, 42(4), 524535.Google Scholar
Houk, J. C., Adams, J. L., & Barto, A. G. (1995). A model of how the basal ganglia generate and use neural signals that predict reinforcement. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 249270). Cambridge, MA: MIT Press.Google Scholar
Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 15691572.Google Scholar
Janowsky, J. S., Shimamura, A. P., Kritchevsky, M., & Squire, L. R. (1989). Cognitive impairment following frontal lobe damage and its relevance to human amnesia. Behavioral Neuroscience, 103(3), 548560.CrossRefGoogle ScholarPubMed
Kehagia, A. A., Cools, R., Barker, R. A., & Robbins, T. W. (2009). Switching between abstract rules reflects disease severity but not dopaminergic status in Parkinson’s disease. Neuropsychologia, 47(4), 11171127.Google Scholar
Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996). A neostriatal habit learning system in humans. Science, 273(5280), 13991402.Google Scholar
Kovacs, P., Hélie, S., Tran, A. N., & Ashby, F. G. (2021). A neurocomputational theory of how rule-guided behaviors become automatic. Psychological Review, 128(3), 488508.Google Scholar
Kruschke, J. K. (1996). Dimensional relevance shifts in category learning. Connection Science, 8(2), 225247.Google Scholar
Leng, N. R., & Parkin, A. J. (1988). Double dissociation of frontal dysfunction in organic amnesia. British Journal of Clinical Psychology, 27(4), 359362.Google Scholar
Lisman, J., Schulman, H., & Cline, H. (2002). The molecular basis of CaMKII function in synaptic and behavioural memory. Nature Reviews Neuroscience, 3(3), 175190.Google Scholar
Logothetis, N. K., & Sheinberg, D. L. (1996). Visual object recognition. Annual Review of Neuroscience, 19(1), 577621.CrossRefGoogle ScholarPubMed
Lopez-Paniagua, D., & Seger, C. A. (2011). Interactions within and between corticostriatal loops during component processes of category learning. Journal of Cognitive Neuroscience, 23(10), 30683083.Google Scholar
Love, B. C., & Gureckis, T. M. (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience, 7(2), 90108.Google Scholar
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111(2), 309332.Google Scholar
Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 650662.Google Scholar
Maddox, W. T., Filoteo, J. V., Hejl, K. D., et al. (2004). Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 227235.Google Scholar
Maddox, W. T., Glass, B. D., O’Brien, J. B., Filoteo, J. V., & Ashby, F. G. (2010). Category label and response location shifts in category learning. Psychological Research, 74(2), 219236.Google Scholar
Maddox, W. T., & Ing, A. D. (2005). Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), 100107.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: W. H. Freeman.Google Scholar
Matsumoto, N., Minamimoto, T., Graybiel, A. M., & Kimura, M. (2001). Neurons in the thalamic CM-Pf complex supply striatal neurons with information about behaviorally significant sensory events. Journal of Neurophysiology, 85(2), 960976.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207238.Google Scholar
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167202.Google Scholar
Mishkin, M., Malamut, B., & Bachevalier, J. (1984). Memories and habits: two neural systems. In Lynch, G., McGaugh, J. L., & Weinberger, N. M. (Eds.), Neurobiology of Human Learning and Memory (pp. 6577). New York, NY: Guilford Press.Google Scholar
Monchi, O., Petrides, M., Doyon, J., Postuma, R. B., Worsley, K., & Dagher, A. (2004). Neural bases of set-shifting deficits in Parkinson’s disease. The Journal of Neuroscience, 24(3), 702710.Google Scholar
Monchi, O., Petrides, M., Petre, V., Worsley, K., & Dagher, A. (2001). Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. The Journal of Neuroscience, 21(19), 77337741.Google Scholar
Monchi, O., Taylor, J. G., & Dagher, A. (2000). A neural model of working memory processes in normal subjects, Parkinson’s disease and schizophrenia for fMRI design and predictions. Neural Networks, 13(8–9), 953973.CrossRefGoogle ScholarPubMed
Moustafa, A. A., & Gluck, M. A. (2011). A neurocomputational model of dopamine and prefrontal–striatal interactions during multicue category learning by Parkinson patients. Journal of Cognitive Neuroscience, 23(1), 151167.Google Scholar
Nomura, E., Maddox, W., Filoteo, J., et al. (2007). Neural correlates of rule-based and information-integration visual category learning. Cerebral Cortex, 17(1), 3743.Google Scholar
Norman, K. A., & O’Reilly, R. C. (2003). Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychological Review, 110(4), 611646.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115, 3957.Google Scholar
O’Reilly, R. C., Hazy, T. E., & Herd, S. A. (2016). The Leabra cognitive architecture: how to play 20 principles with nature. The Oxford Handbook of Cognitive Science, 91, 91116.Google Scholar
O’Reilly, R. C., Munakata, Y., Frank, M., Hazy, T., et al. (2012). Computational Cognitive Neuroscience. Mainz: PediaPress.Google Scholar
O’Reilly, R. C., Noelle, D. C., Braver, T. S., & Cohen, J. D. (2002). Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. Cerebral Cortex, 12(3), 246257.Google Scholar
O’Reilly, R. C., Wyatte, D., Herd, S., Mingus, B., & Jilk, D. J. (2013). Recurrent processing during object recognition. Frontiers in Psychology, 4, 124.Google Scholar
Pakhotin, P., & Bracci, E. (2007). Cholinergic interneurons control the excitatory input to the striatum. The Journal of Neuroscience, 27(2), 391400.CrossRefGoogle ScholarPubMed
Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353363.Google Scholar
Price, A., Filoteo, J. V., & Maddox, W. T. (2009). Rule-based category learning in patients with Parkinson’s disease. Neuropsychologia, 47(5), 12131226.Google Scholar
Rall, W. (1967). Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. Journal of Neurophysiology, 30(5), 11381168.Google Scholar
Reber, P. J., & Squire, L. R. (1999). Intact learning of artificial grammars and intact category learning by patients with Parkinson’s disease. Behavioral Neuroscience, 113(2), 235242.Google Scholar
Reber, P. J., Stark, C. E., & Squire, L. R. (1998). Contrasting cortical activity associated with category memory and recognition memory. Learning & Memory, 5(6), 420428.CrossRefGoogle ScholarPubMed
Reynolds, J. N., & Wickens, J. R. (2002). Dopamine-dependent plasticity of corticostriatal synapses. Neural Networks, 15(4), 507521.Google Scholar
Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 10191025.Google Scholar
Riesenhuber, M., & Poggio, T. (2002). Neural mechanisms of object recognition. Current Opinion in Neurobiology, 12(2), 162168.Google Scholar
Rougier, N. P., & O’Reilly, R. C. (2002). Learning representations in a gated prefrontal cortex model of dynamic task switching. Cognitive Science, 26(4), 503520.Google Scholar
Rudy, J. W. (2014). The Neurobiology of Learning and Memory. Sunderland, MA: Sinauer.Google Scholar
Sanders, B. (1971). Factors affecting reversal and nonreversal shifts in rats and children. Journal of Comparative and Physiological Psychology, 74, 192202.Google Scholar
Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84(1), 166.Google Scholar
Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74(1), 158.Google Scholar
Seger, C. A., & Cincotta, C. M. (2005). The roles of the caudate nucleus in human classification learning. The Journal of Neuroscience, 25(11), 29412951.Google Scholar
Seger, C. A., & Miller, E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203219.Google Scholar
Seger, C. A., Peterson, E. J., Cincotta, C. M., Lopez-Paniagua, D., & Anderson, C. W. (2010). Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling. NeuroImage, 50(2), 644656.Google Scholar
Serre, T., Oliva, A., & Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences, 104(15), 64246429.Google Scholar
Smith, Y., Raju, D. V., Pare, J.-F., & Sidibe, M. (2004). The thalamostriatal system: a highly specific network of the basal ganglia circuitry. Trends in Neurosciences, 27(9), 520527.Google Scholar
Sreenivasan, K. K., Curtis, C. E., & D’Esposito, M. (2014). Revisiting the role of persistent neural activity during working memory. Trends in Cognitive Sciences, 18(2), 8289.Google Scholar
Tachibana, K., Suzuki, K., Mori, E., et al. (2009). Neural activity in the human brain signals logical rule identification. Journal of Neurophysiology, 102(3), 15261537.Google Scholar
Valentin, V. V., Maddox, W. T., & Ashby, F. G. (2014). A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing. Frontiers in Psychology, 5(643). https://doi.org/10.3389/fpsyg.2014.00643Google Scholar
Varrone, A., & Halldin, C. (2014). Human brain imaging of dopamine transporters. In Seeman, P. & Madras, B. (Eds.), Imaging of the Human Brain in Health and Disease (pp. 203240). Amsterdam: Elsevier.Google Scholar
Waldron, E. M., & Ashby, F. G. (2001). The effects of concurrent task interference on category learning: evidence for multiple category learning systems. Psychonomic Bulletin & Review, 8(1), 168176.CrossRefGoogle ScholarPubMed
Wallis, J. D., & Miller, E. K. (2003). From rule to response: neuronal processes in the premotor and prefrontal cortex. Journal of Neurophysiology, 90(3), 17901806.Google Scholar
Wickens, J. (1993). A Theory of the Striatum. Oxford: Pergamon Press.Google Scholar
Willingham, D. B. (1998). A neuropsychological theory of motor skill learning. Psychological Review, 105, 558584.Google Scholar
Willingham, D. B., Nissen, M. J., & Bullemer, P. (1989). On the development of procedural knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 10471060.Google Scholar
Wills, A., Noury, M., Moberly, N. J., & Newport, M. (2006). Formation of category representations. Memory & Cognition, 34(1), 1727.Google Scholar
Wilson, C. J. (1995). The contribution of cortical neurons to the firing pattern of striatal spiny neurons. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 2950). Cambridge, MA: MIT Press.Google Scholar
Worthy, D. A., Markman, A. B., & Maddox, W. T. (2013). Feedback and stimulus-offset timing effects in perceptual category learning. Brain and Cognition, 81(2), 283293.Google Scholar
Wyatte, D., Herd, S., Mingus, B., & O’Reilly, R. (2012). The role of competitive inhibition and top-down feedback in binding during object recognition. Frontiers in Psychology, 3, 182.Google Scholar
Yagishita, S., Hayashi-Takagi, A., Ellis-Davies, G. C., Urakubo, H., Ishii, S., & Kasai, H. (2014). A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science, 345(6204), 16161620.Google Scholar
Zeithamova, D., & Maddox, W. T. (2006). Dual-task interference in perceptual category learning. Memory & Cognition, 34(2), 387398.Google Scholar

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