Yu-Ang Cheng¹, Yuka Sasaki¹, Thomas Serre¹, Takeo Watanabe¹; ¹Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA

Visual perceptual learning (VPL) refers to long-term performance improvements following visual experience. It is controversial whether VPL arises from plasticity at the level of neural encoding processes or downstream readout processes (Watanabe & Sasaki, 2015, Ann. Rev. Psych.; Dosher & Lu, Perceptual Learning, MIT Press, 2020). To address this issue, we compared how training on an orientation detection task alters performance in humans and in a well-established neural network model of the early visual cortex, which includes broad excitation, untuned inhibition, and readout components. We focused on performance changes in untrained orientations, which have been discussed only relative to the trained orientation. In the psychophysical experiment with humans, as predicted, the greatest performance enhancement was observed at the trained orientation. Enhancements tapered off as orientations deviated further from the trained one, until around 90 degrees, where no performance increase was observed. Performance at orientations approximately 90 degrees from the trained orientation remained unchanged. In the neural network model simulation, plasticity in the readout components always led to unexpected performance decreases at untrained orientations. However, the introduction of plasticity in untuned inhibition during neural encoding processes, leading to a divisive normalization effect, mitigated the performance decreases at untrained orientations. These findings suggest that divisive normalization plasticity may resolve the discrepancy between the results from human psychophysics and the initial computational model. Our results further suggest the involvement of neural encoding processes in VPL.

Acknowledgements: NIH R01EY019466, R01EY027841, R01EY031705, NSF-BSF BCS2241417, ONR (N00014-24-1-2026), ONR (N00014-19-1-2029), DARPA (D19AC00015), NIH/NINDS (R21 NS 112743), the ANR-3IA Artificial and Natural Intelligence Toulouse Institute (ANR-19-PI3A-0004)