ArticleNeural networks : the official journal of the International Neural Network Society2017
Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.
Article in Neural networks : the official journal of the International Neural Network Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Category learning in a recurrent neural network with reinforcement learning.Frontiers in psychiatry · 2022Article
- Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model.Computational brain & behavior · 2021Article
- Introduction to Special Issue on Perceptual Learning.Vision research · 2018Article
- On the Role of Cortex-Basal Ganglia Interactions for Category Learning: A Neurocomputational Approach.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
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Authors and funding
4 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.