Identifying Long- and Short-Term Processes in Perceptual Learning

Abstract

Practice makes perfect in almost all perceptual tasks, but how perceptual improvements accumulate remains unknown. Here, we developed a multicomponent theoretical framework to model contributions of both long- and short-term processes in perceptual learning. Applications of the framework to the block-by-block learning curves of 49 adult participants in seven perceptual tasks identified ubiquitous long-term general learning and within-session relearning in most tasks. More importantly, we also found between-session forgetting in the vernier-offset discrimination, face-view discrimination, and auditory-frequency discrimination tasks; between-session off-line gain in the visual shape search task; and within-session adaptation and both between-session forgetting and off-line gain in the contrast detection task. The main results of the vernier-offset discrimination and visual shape search tasks were replicated in a new experiment. The multicomponent model provides a theoretical framework to identify component processes in perceptual learning and a potential tool to optimize learning in normal and clinical populations.

The time course of stimulus-specific perceptual learning

“…In both experiments, the strongest predictor of performance in session 2 was the log-transformed trial number ( t 2 effect size: 1.24 and 1.23), followed by individual differences at baseline ( effect size: 0.50 and 0.62). Average accuracy increased approximately linearly against log trial for all groups, consistent with a single exponential function for perceptual learning as shown for various tasks ( Dosher & Lu, 2007 ; Hussain et al., 2009b ; Zhang, Zhao, Dosher, & Lu, 2019 ; Cochrane & Green, 2021 ; Yang et al, 2022 ). Stimulus novelty significantly affected the intercept but not the slope of the accuracy-vs.-trial function ( Figures 3 and 5 ), but the effect size for novelty ( N = 0.3 and 0.14) was smaller than the effects of t 2 and .…”

“…This result, combined with the finding that the N × T 1 did not differ between experiments, suggests that the longer 1-week interval between sessions constrained the amount of learning (or the retention of learning) by similar amounts in the same and novel conditions. Between-session forgetting is characteristic of some types of perceptual learning ( Yang et al., 2022 ), but it is not clear how it depends on the training-test interval. Based on the current results, we predict that increasing the training-test interval beyond 1 week would further diminish the benefits of large amounts of practice, but would not affect the stimulus-specificity of learning.…”

Two opposing yet complementary ocular dominance plasticity: thalamus strengthens the weak channel while higher cortex listens to the strong signal

“…Consistent with this hypothesis, the short-term effect of perceptual learning can occur even within training session in a block-by-block basis, especially for high-level visual processing (Yang et al, 2022). To directly test this hypothesis in our data, we performed a dynamic causal modeling (DCM) analysis to investigate how MCD influenced the effective connectivity between DVA and V1 (Zeidman, Jafarian, Corbin, et al, 2019).…”

The asymmetric transfers of visual perceptual learning determined by the stability of geometrical invariants

“…Due to the employment of short-term perceptual learning paradigms in both psychophysical experiments in our study, it has been challenging to accurately track the temporal processes of learning (Yang et al, 2022 ).It is also possible that the lack of observed differences in learning effects between tasks could be due to insufficient learning in some tasks, considering the differences in learning speed among the three tasks, as well as the possibility that different training tasks may involve different short-term and long-term learning processes (Aberg et al, 2009 ; Mascetti et al, 2013 ).…”