Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.
The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning.
University of California, Santa Barbara, United States. Electronic address: gcant@umich.edu.
George
G
Cantwell
Georgetown University, United States. Electronic address: Max.Riesenhuber@georgetown.edu.
Maximilian
M
Riesenhuber
University of California, Santa Barbara, United States. Electronic address: jessicalroeder@gmail.com.
Jessica L
JL
Roeder
University of California, Santa Barbara, United States. Electronic address: ashby@psych.ucsb.edu.
F Gregory
FG
Ashby
D001479:Basal Ganglia / Q000502:physiology; D000066494:Cognitive Neuroscience / Q000379:methods* / Q000639:trends; D003198:Computer Simulation / Q000639:trends; D006801:Humans; D007858:Learning / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods; D011897:Random Allocation; D014793:Visual Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology
Basal ganglia; COVIS; Categorization; Computational cognitive neuroscience; HMAX; Visual neuroscience