The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
The Dynamics of Perceptual Learning: An Incremental Reweighting Model.
Abstract
The mechanisms of perceptual learning are analyzed theoretically, probed in an orientation-discrimination experiment involving a novel nonstationary context manipulation, and instantiated in a detailed computational model. Two hypotheses are examined: modification of early cortical representations versus task-specific selective reweighting. Representation modification seems neither functionally necessary nor implied by the available psychophysical and physiological evidence. Computer simulations and mathematical analyses demonstrate the functional and empirical adequacy of selective reweighting as a perceptual learning mechanism. The stimulus images are processed by standard orientation- and frequency-tuned representational units, divisively normalized. Learning occurs only in the "read-out" connections to a decision unit; the stimulus representations never change. An incremental Hebbian rule tracks the task-dependent predictive value of each unit, thereby improving the signal-to-noise ratio of their weighted combination. Each abrupt change in the environmental statistics induces a switch cost in the learning curves as the system temporarily works with suboptimal weights.
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Cites
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Cited by
Circuit Mechanisms of Sensorimotor Learning
Makino, H., Hwang, E. J., Hedrick, N. G., & Komiyama, T. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.029 · Cited by 228
“These theories propose that perceptual learning involves appropriate routing and weighting of the most informative inputs from the sensory processing stage to the decision stage, while neural properties in early sensory areas are unaltered (Law and Gold, 2009; Petrov et al., 2005).”
Mesolimbic confidence signals guide perceptual learning in the absence of external feedback
Guggenmos, M., Wilbertz, G., Hebart, M. N., & Sterzer, P. (2016) · eLife · doi:10.7554/elife.13388 · Cited by 164
Multisensory perceptual learning and sensory substitution
Proulx, M. J., Brown, D. J., Pasqualotto, A., & Meijer, P. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.11.017 · Cited by 148
Beyeler, M., Rokem, A., Boynton, G. M., & Fine, I. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa795e · Cited by 102
Deep Neural Networks for Modeling Visual Perceptual Learning
Wenliang, L. K., & Seitz, A. R. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1620-17.2018 · Cited by 83
Metadata
- Authors
- Petrov, A. A., Dosher, B. A., & Lu, Z.-L.
- Venue
- Psychological Review
- Published
- 2005
- Volume
- 112
- Issue
- 4
- Pages
- 715-743
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2115256808
- Cited by
- 313
- References
- 126
- FWCI
- 3.89
- Type
- journal article
- Language
- en
- Open access
- closed
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