How repeated training or practice leads to long-term performance improvements is one of the fundamental questions in skill acquisition. Although long-term benefits of perceptual learning have been observed in a wide range of perceptual tasks, a few studies also documented some very interesting short-term phenomena during the learning process. However, most existing studies have focused on the average performance at the session-by-session time scale that typically involves hundreds of trials and may have missed some important short-term processes that only manifest at a finer time scale.

Dr. YANG Jia and her colleagues, under the guidance of Professor HUANG Chang-Bing in the Institute of Psychology at the Chinese Academy of Sciences and Professor LU Zhong-Lin at New York University Shanghai and New York University, developed a multicomponent theoretical framework (Fig. 1) to model and identify contributions of multiple long and short-term processes in perceptual learning.

The team proposed three hypotheses. The first one is that improved perceptual-task performance through training reflects cumulative effects of both long- and short-term processes. The second hypothesis is that finer grained analysis on block-by-block (e.g., tens of trials) learning curves may reveal short-term effects that have been obscured in the traditional coarse session-by-session analysis. Last but not least, different training tasks may engage different long- and short-term processes.

To examine these hypotheses, the team applied the multicomponent model to analyze the fine-grained block-by-block learning curves from a multiple-task perceptual learning experiment, in which 49 participants were trained in seven perceptual tasks in 35 daily sessions. In addition to the ubiquitous long-term general learning in all tasks, the multicomponent model identified a number of short-term processes: within-session relearning in all tasks; 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 fine-grained analysis of the learning curves with the multicomponent model provided a more comprehensive characterization of the time course of perceptual learning. The model provides not only a new framework for identifying component processes in perceptual learning but may also be used to optimize learning outcomes in normal and clinical populations.

This work entitled “Identifying Long- and Short-Term processes in Perceptual Learning” was published online on April 28, 2022 in Psychological Science.

Fig.1 Multicomponent model. Image by Prof. HUANG Chang-Bing.

CONTACT
Ms. LIU Chen
Institute of Psychology Chinese Academy of Sciences
Beijing 100101, China.