@inproceedings{
liu2024to,
title={To Learn or Not to Learn, That is the Question {\textemdash} A Feature-Task Dual Learning Model of Perceptual Learning},
author={Xiao Liu and Muyang Lyu and Cong Yu and Si Wu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=g3MbZOw0qO}
}
Perceptual learning refers to the practices through which participants learn to improve their performance in perceiving sensory stimuli. Two seemingly conflicting phenomena of specificity and transfer have been widely observed in perceptual learning. 

Here, we propose a dual-learning model to reconcile these two phenomena. The model consists of two learning processes. One is task-based learning, which is fast and enables the brain to adapt to a task rapidly by using existing feature representations. The other is feature-based learning, which is slow and enables the brain to improve feature representations to match the statistical change of the environment. Associated with different training paradigms, the interactions between these two learning processes induce the rich phenomena of perceptual learning. Specifically, in the training paradigm where the same stimulus condition is presented excessively, feature-based learning is triggered, which incurs specificity, while in the paradigm where the stimulus condition varies during the training, task-based learning dominates to induce the transfer effect. As the number of training sessions under the same stimulus condition increases, a transition from transfer to specificity occurs. 

We demonstrate that the dual-learning model can account for both the specificity and transfer phenomena observed in classical psychophysical experiments. We hope that this study gives us insight into understanding how the brain balances the accomplishment of a new task and the consumption of learning effort.
To Learn or Not to Learn, That is the Question — A Feature-Task Dual Learning Model of Perceptual Learning
Xiao Liu Muyang Lyu Cong Yu Si Wu