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General Learning Ability in Perceptual Learning General learning ability in perceptual learning Jia Yanga,b, Fang-Fang Yana,b, Lijun Chena,b, Jie Xia,b, Shuhan Fana,b, Pan Zhangc, Zhong-Lin Luc,d,e,1, and Chang-Bing Huanga,b,1 aKey Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 100101 Beijing, China; bDepartment of Psychology, University of Chinese Academy of Sciences, 100049 Beijing, China; cCenter for Neural Science and Department of Psychology, New York University, New York, NY 10003; dDivision of Arts and Sciences, New York University Shanghai, 200122 Shanghai, China; and eNYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, 200062 Shanghai, China Edited by Takeo Watanabe, Brown University, Providence, RI, and accepted by Editorial Board Member Charles D. Gilbert June 24, 2020 (received for review February 23, 2020) Developing expertise in any field usually requires acquisition of a suggesting the existence of a general ability to learn that can be wide range of skills. Most current studies on perceptual learning improved through action video game plays. Although these have focused on a single task and concluded that learning is quite studies in perceptual learning and other domains of cognitive specific to the trained task, and the ubiquitous individual differ- science suggest that there might be a more systematic account of ences reflect random fluctuations across subjects. Whether there individual differences in perceptual learning, it remains largely exists a general learning ability that determines individual learning unclear whether individual differences in perceptual learning performance across multiple tasks remains largely unknown. In a reflect variabilities of learning abilities of individuals that are large-scale perceptual learning study with a wide range of training nonetheless consistent across multiple perceptual learning tasks tasks, we found that initial performance, task, and individual differ- for each individual. ences all contributed significantly to the learning rates across the In the current study, we hypothesized that 1) there’s a general tasks. Most importantly, we were able to extract both a task-specific ability for each individual in learning multiple perceptual tasks but subject-invariant component of learning, that accounted for and 2) individual differences in perceptual learning reflect the 38.6% of the variance, and a subject-specific but task-invariant per- variability of such ability across individuals. If these hypotheses ceptual learning ability, that accounted for 36.8% of the variance. were true, we would observe a systematic pattern of individual The existence of a general perceptual learning ability across multiple differences across multiple perceptual learning tasks that de- tasks suggests that individual differences in perceptual learning are pends only on specific individuals but is invariant across tasks. To not “noise”; rather, they reflect the variability of learning ability test these hypotheses, we trained a large number of subjects in a across individuals. These results could have important implications wide range of perceptual tasks. The resulting dataset consisted of for selecting potential trainees in occupations that require percep- seven different learning tasks covering a wide range of percep- tual expertise and designing better training protocols to improve tual domains, 35 consecutive training sessions, 49 subjects, and the efficiency of clinical rehabilitation. ∼23,720 trials/subject. We found that learning rate varied tre- mendously across tasks and subjects, and negatively correlated perceptual learning | multitask continual learning | general learning with initial performance. A multivariate model successfully accounted ability | individual difference Significance he remarkable sensitivity of the human visual system is achieved Tthrough millions of years of evolution, years of development, Developing expertise usually requires acquisition of a variety of and life-long perceptual learning. The past 30 y of research on skills, and some can become experts and some can’t. The ubiq- perceptual learning has documented effects of perceptual learning uitous individual differences in perceptual learning led us to in almost every visual task in adult humans (1–3), from detection or hypothesize that each individual has a general perceptual discrimination of single stimulus features (4–9) to identification of learning “ability,” and individual differences reflect the vari- complex objects and natural scenes (10–12). A ubiquitous obser- ability of such ability across individuals. By collecting and ana- vation is the widespread individual differences that have been re- lyzing data from a large sample of subjects in seven visual, lated to the trained task (13), training procedure (14), feedback auditory, and working memory training tasks, we successfully (15), reward (7, 16), and trainees’ gaming experiences (17). extracted both a task-specific but subject-invariant component Although the common practice in perceptual learning treats of learning and a subject-specific but task-invariant perceptual individual differences as random fluctuations or noise and makes learning ability. The existence of a general learning ability could inferences based on aggregated data from multiple subjects (18, have important implications for theories and applications of 19), individual differences have been investigated in relation to perceptual learning across multiple tasks and the development genetic and/or environmental influence on human behavior and of perceptual expertise and/or remediation of visual conditions. can even be predicted from brain structure and neural activity in a wide range of cognitive and motor tasks (20–22). There is also Author contributions: C.-B.H. designed research; J.Y. performed research; F.-F.Y., L.C., J.X., accumulating evidence that individual differences in perceptual S.F., P.Z., and C.-B.H. contributed new reagents/analytic tools; J.Y., Z.-L.L., and C.-B.H. analyzed data; J.Y., Z.-L.L., and C.-B.H. wrote the paper; and F.-F.Y. and C.-B.H. provided learning may not be merely caused by random fluctuations across equipment and advice on study design. individuals but reflect systematic differences across individuals. The authors declare no competing interest. For example, large variability of the magnitude of perceptual This article is a PNAS Direct Submission. T.W. is a guest editor invited by the learning among subjects was found to be negatively correlated Editorial Board. with initial task performance in perceptual learning of Vernier Published under the PNAS license. and stereoscopic depth discrimination tasks (23, 24). In addition, Data deposition: Anonymized (.mat; .csv) data have been deposited at Open Science cortical thickness of MT+ and the left fusiform cortex was found Framework (https://osf.io/dgqxv/). to be a good predictor of the learning rate in a motion dis- 1To whom correspondence may be addressed. Email: [email protected] or huangcb@ crimination task (25) and the magnitude of learning in a face psych.ac.cn. view discrimination task (11), respectively. Studies on action This article contains supporting information online at https://www.pnas.org/lookup/suppl/ video game training (26, 27) have concluded that improved doi:10.1073/pnas.2002903117/-/DCSupplemental. perceptual learning of gamers implicates “learning to learn,” First published July 23, 2020. 19092–19100 | PNAS | August 11, 2020 | vol. 117 | no. 32 www.pnas.org/cgi/doi/10.1073/pnas.2002903117 Downloaded by guest on September 25, 2021 for most of the variance across 343 learning curves, and extracted the blocks (28). Each subject completed a total of about 23,720 trials. In contributions of task, subject, and initial performance to the learning addition, a number of personality trait measures (nonverbal IQ, big- rates. These results provide strong evidence for the existence of a five personality, and achievement of motivation) were assessed us- consistent pattern of individual differences across multiple training ing the standard progressive matrices (SPM) (30), neuroticism tasks. An additional least absolute shrinkage and selection operator -extraversion-openness five-factor inventory (NEO-FFI) (31), and (LASSO) regression analysis revealed that a number of personality achievement motivation scale (AMS) (32). traits, including IQ, extraversion, and neuroticism, made significant To compare human performance across different tasks and contributions to individual differences. Our results reveal the multi- measures, we first reorganized the data from the seven tasks. faceted nature of perceptual learning and the existence of a general Specifically, we transformed estimated thresholds in the contrast ability in perceptual learning, with strong implications for the devel- detection, Vernier acuity, face view discrimination, and auditory opment and applications of test batteries to select potential trainees frequency discrimination tasks into perceptual sensitivity for perceptual expertise and customization of more effective and (i.e., the reciprocal of threshold), converted performance in the efficient training protocols in clinical applications. motion direction discrimination and visual shape search tasks into d′, and obtained the average N-back level in the audiovisual Results N-back working memory task in each training session. We then Effects of Initial Performance, Task, and Subject on Learning Rates. generated log10 normalized performance scores: Each subject’s
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