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 Forty-nine subjects (22 males, 23.4 ± 2.5 y) participated in seven performance score in each task and training session was first training tasks in 35 sessions, with five consecutive daily sessions divided by her/his performance score in the first training session per task in a counterbalanced order across subjects (Fig. 1; see of the corresponding task and transformed with a log10 opera- Materials and Methods). The tasks included five visual perceptual tion, resulting in 343 (49 subjects × 7 tasks) learning curves (SI learning tasks (contrast detection, Vernier acuity, global motion Appendix, Fig. S1), each consisting of five data points. As shown direction discrimination, visual shape search, and face view dis- in Fig. 2A,it’s difficult to see any general patterns from the crimination), an auditory frequency discrimination task, and an learning curves; they varied greatly across subjects and tasks. We audiovisual N-back working memory task (28). A three-down/one- present a number of quantitative analyses below. up staircase method (29) was used to measure perceptual threshold We began by exploring the relationship between learning rates in the contrast detection, Vernier acuity, face view, and auditory and a number of potential factors. A linear regression model was tasks, and percent correct was monitored throughout training in the fit to each of the 343 log10 normalized learning curves. This

motion direction discrimination and visual shape search tasks. For resulted in 343 learning rates, one for each subject in each PSYCHOLOGICAL AND COGNITIVE SCIENCES the audiovisual N-back task, each session consisted of 30 blocks of training task. Previous research in perceptual learning found that 20 + N trials, with the N-back level adjusted adaptively across initial task performance, which may reflect subject-specific

A B Fixation Interval 1 Blank Fixation Interval 2 Response Interval 1 Interval 2 Interval 3 Response

K H R

100 ms 100 ms 500 ms 100 ms 100 ms 25 ms 150 ms 25 ms C D Interval 1 Blank Interval 2 Response Interval Response

500 ms 200 ms 500 ms 800 ms E F Interval 1 Blank Interval 2 Response Tone 1 Blank Tone 2 Response

100 ms 1400 ms 100 ms 100 ms 500 ms 100 ms

G ISI=2500 ms Visual target Visual target Visual target 500 ms 500 ms

+ + + + + + + + + + + + + C T H T L Q R Q S K L K

500 ms 500 ms Auditory target Auditory target Auditory target 2 back dual task H Training Motion Vernier Face Contrast Shape Auditory N back

Fig. 1. Illustrations of the experimental procedures of the training tasks. (A) Contrast detection, (B) Vernier acuity, (C) motion direction discrimination, (D) visual shape search, (E) face view discrimination, (F) auditory frequency discrimination, and (G) audiovisual N-back working memory task. An example training sequence is shown in H.

Yang et al. PNAS | August 11, 2020 | vol. 117 | no. 32 | 19093 Downloaded by guest on September 25, 2021 Fig. 2. The 343 learning curves and effects of initial performance, task, and subject on learning rates. (A) A heatmap of the log10 normalized performance scores for all subjects in the seven tasks across training sessions. The x axis is organized by subject; the y axis is organized by task (left y axis) and five training sessions for each task (right y axis). The magnitudes of the performance scores are indicated by color. The brighter the color, the better the performance. (B) Scatter plots of the negation correlation between learning rate and initial performance of all seven tasks. The solid line shows the linear regressionof learning rate vs. initial performance. Each symbol indicates one subject in one task, and different symbols are used for the seven tasks. (C) Average learning rate for each task across all subjects , with significantly different learning rates across the seven tasks (P < 0.001, η2 = 0.19, one-way ANOVA). (D) Average learning rate for each subject across all training tasks. Error bars indicate SE. (E) Distribution of the average learning rates in D.

neural underpinning in performing a particular task, was nega- given that false discovery rate (FDR) was 0 (33)] (SI Appendix, tively correlated with the magnitude of learning (23, 24). To Table S2). The results suggest that, in addition to task differences, explore such a relationship in our data, we computed the z score there might be individual differences in learning rates. of each subject’s performance in the first session of each task To evaluate individual differences, we computed the mean based on the mean and SD of the performance scores of all 49 learning rate across all seven tasks for each subject (Fig. 2D). The subjects, and then calculated the correlation between the 343 z 49 mean learning rates (Fig. 2E) were normally distributed (Sha- scores and the 343 learning rates (equivalent to magnitudes of piro−Wilk Test, W [49] = 0.97, P = 0.20). Some have suggested that learning). A significant negative correlation was found this pattern may indicate the existence of a general learning ability (r = −0.33, P < 0.001; Fig. 2B). The results were essentially the across different tasks (34). same when we computed the correlation of the performance These preliminary analyses showed that learning rates correlated scores in the first two blocks with the learning rate across all of with initial performance, task, and subject. To better quantify the the blocks in each task. In other words, consistent with the lit- contributions of all three factors simultaneously, we applied a erature, we found that the worse the initial performance was, the multivariate regression model with 64 parameters to fit all of the greater the learning rate or the magnitude of learning (SI Ap- 343 learning curves: 7 coefficients on initial performance, 7 coeffi- pendix, Table S1). cients on task, 49 coefficients on subject, and an intercept (Materials We then evaluated how learning rates varied across different and Methods Eq. 3). The model accounted for 53.06% of the total tasks. A one-way ANOVA showed that learning rates differed variance of 1,715 (343 × 5) data points (F [63, 1,651] = 13.02, P < 2 significantly across tasks (F [6, 336] = 11.14, P < 0.001, η2 = 0.19), 0.001, Cohen’s f = 0.50; comparing to the reduced model only with with the contrast detection task exhibiting the slowest average an intercept; see Materials and Methods and SI Appendix, Supple- learning rate (mean ± SE: 0.17 ± 0.02; Fig. 2C), and the visual mentary Data Analysis). Reduced models with only initial perfor- shape search task exhibiting the fastest average learning rate mance, task, or subject alone accounted for 36.86%, 38.64%, and (mean ± SE: 0.48 ± 0.02; Fig. 2C). In addition, none of the pair- 36.80% of the variance, respectively, and were inferior to the full wise correlations between the learning rates of any two tasks was model with all three factors (F [56, 1,650] = 10.17, P < 0.001, 2 2 significant [all P > 0.09; uncorrected for the multiple comparisons Cohen’s f = 0.35; F [56, 1,650] = 9.06, P < 0.001, Cohen’s f = 0.31;

19094 | www.pnas.org/cgi/doi/10.1073/pnas.2002903117 Yang et al. Downloaded by guest on September 25, 2021 Contrast 10-fold cross-validation procedure was used to identify the con- Vernier tributions of individual personality traits to individual differences AB Motion Shape l r=0.66, p<0.001*** in perceptual learning. With 1 norm regularization, LASSO re- 1.5 Face 0.4 Auditory gression has been used to identify significant predictors, avoid N back 1 overfitting, and improve the generalization of models (36) A SI Appendix Supplementary Data Analysis 0.2 (Fig. 4 ; see also , ). Our 0.5 Initial model consisted of seven task-, seven initial performance-, and Performance 0 Coefficient seven personality trait-related predictors, with the log10 nor- Predicted Rate 0 Subject (N=49) Task malized performance scores as the response variables. Data from -0.5 42 subjects were used as the training dataset, and those from the -0.5 0 0.5 1 1.5 -0.2 Rate of linear fit remaining seven were used as the test dataset. The best model derived from the training dataset was used to predict the test Fig. 3. Results from the multivariate regression model. (A) A scatter plot of subjects’ learning curves in seven tasks. model-predicted learning rates against those from direct linear fits to the The LASSO regression procedure was repeated 1,000 times. individual learning curves. (B) Coefficients of subject, task, and initial per- formance factors from the 64-parameter regression model. The 49 purple Almost all task- and initial performance-related predictors bars on the left represent the value of the 49 subject factors, the seven bars were selected with 100% probability (only the probability of in the middle represent the seven task factors, and the seven bars on the initial performance for the face view task was 99.99%; see SI right represent the seven coefficients of initial performance in the Appendix,TableS5), and all personality trait predictors were seven tasks. selected with over 90% probability (Fig. 4D;seeSI Appendix, Table S5). The average number of selected predictors was 20.80 ± 0.48 (mean ± SD). The average estimated weights of F = P < ’ f2 = [14, 1,650] 40.83, 0.001, Cohen s 0.35) but significantly selected task, initial performance, and personality trait pre- F = P < superior to the intercept model ( [7, 1,706] 27.29, 0.001, dictors are shown in Fig. 4 E–G.Onaverage,themodel ’ f2 = F = P < ’ f2 = Cohen s 0.11; [7, 1,706] 35.11, 0.001, Cohen s 0.14; explained 47.57% ± 1.40% of the variance in the training data F = P < ’ f2 = [49, 1,664] 3.77, 0.001, Cohen s 0.11), confirming the (Fig. 4B) and 40.66% ± 9.71% of the variance in the test necessity of each single factor. Reduced models with two factors, dataset (Fig. 4C). initial performance and task, initial performance and subject, and To further quantify the significance of each predictor, we also task and subject, accounted for 45.70%, 44.22%, and 45.64% of the conducted a general linear regression with 20 predictors PSYCHOLOGICAL AND COGNITIVE SCIENCES variance, respectively, and were also inferior to the full model (i.e., dropping one predictor) in each iteration. We next com- F = P < ’ f2 = F = ( [49, 1,650] 5.28, 0.001, Cohen s 0.16; [7, 1,650] pared the full model (21 predictors) and the reduced models (20 P < ’ f2 = F = P < 39.37, 0.001, Cohen s 0.19; [7, 1,650] 37.26, 0.001, predictors) using a nested F test and calculated the change of ’ f2 = Cohen s 0.16). In addition, the predicted learning rates from Akaike Information Criterion (AIC) after dropping each pre- the full model were highly correlated with those obtained from dictor (Eq. 5; also see SI Appendix, Supplementary Data Analysis r = P < −10 direct fits to the individual learning curves ( 0.66, 10 ; and Table S5). If the full model were significantly superior to a A Fig. 3 ). The estimated coefficients of task and subject factors reduced model and AIC increased after dropping the predictor, B (Fig. 3 ) were also highly correlated with the average learning rate the dropped predictor would have been necessary. We found r = P < r = P < (with task: 0.99, 0.001; with subject: 0.83, 0.001; that dropping IQ, neuroticism, and agreeableness signifi- SI Appendix ,Fig.S2). We performed Pearson correlation analysis cantly reduced the goodness of fit (R2)(P = 0.002, P = 0.022, between subject factors and scores of personality traits. We found and P = 0.026, respectively) and increased AIC by 7.99 ± significant correlations between IQ, neuroticism, and extraversion 4.85, 3.33 ± 3.75, and 3.12 ± 4.71, respectively (mean ± SD), r = P < r = − P < r = P < ( 0.25, 0.05; 0.28, 0.05; 0.26, 0.05, re- dropping extraversion and conscientiousness marginally re- SI Appendix spectively; ,TableS3) and the subject factor without duced model performance (P = 0.064 and P = 0.062, re- multicomparison correction. Although they became only marginally spectively) and increased AIC by 1.49 ± 2.83 and 1.54 ± 3.19, q = significant ( 0.09, 0.09, and 0.09) after multicomparison respectively, but dropping openness and motivation of correction based on FDR (33, 35), the correlations suggested achievement (MA) did not significantly affect model per- that there might be a tendency for IQ, neuroticism, and ex- formance (P = 0.088 and P = 0.195, respectively) and in- traversion to contribute to individual differences in learning creased AIC by 0.96 ± 2.88 and 0.28 ± 2.01, respectively (SI rates. The results motivated us to perform a more direct Appendix,Fig.S4). These results provided quantitative evidence analysis below. We also used the fixed effect model function in for the contributions of learners’ personality traits to perceptual fitlm Matlab, , to independently verify the results of the origi- learning. High IQ, extraversion, and openness scores were linked nal multivariate regression analysis, treating these factors as to better learning ability, while high scores in neuroticism, fixed effects and considering interactions between factors. The agreeableness, and conscientiousness posed negative effects on SI Appendix Supplementary results were essentially the same ( , perceptual learning. Interestingly, motivation seemed to be least Data Analysis and Table S4). important (SI Appendix,TableS5).

Identifying Personality Traits Underlying Individual Differences. The Discussion results from the multivariate regression analysis suggested that With a huge amount of data from a large sample of subjects in a individual differences contribute significantly to the learning wide range of visual, auditory, and working memory training rates in multitask perceptual learning. The marginally significant paradigms, we were able to evaluate contributions of initial per- correlations between three personality traits (IQ, neuroticism, formance, task, and subject to the learning rates in multiple and extraversion) and the subject factor from the multivariate perceptual learning tasks. We found that all these factors regression analysis motivated us to perform a direct analysis of contributed significantly to the learning rates. Moreover, a the contributions of personality traits to the rates of perceptual number of personality traits, including IQ, neuroticism, and learning, replacing the subject factor in the multiregression agreeableness, made significant contributions to individual model with personality traits and directly modeling the log10 differences. The results of this study suggest that individual normalized performance scores. LASSO regression with a differences in perceptual learning are not “noise”;rather,they

Yang et al. PNAS | August 11, 2020 | vol. 117 | no. 32 | 19095 Downloaded by guest on September 25, 2021 Fig. 4. Methods and results from LASSO regression. (A) A flowchart of the model selection and prediction procedure. Briefly, we separated the 49 subjects into a training dataset (n = 42) and a testing dataset (n = 7). LASSO finds the optimal model by minimizing the sum of SSE and the absolute value of β weights based on the training datasets (for details, see Materials and Methods and Eq. 4). We adopted a 10-fold cross-validation procedure to determine the value of λ, which controls the degree of regularization. Next, given λ, we estimated the weights (β) of the predictors corresponding to the maximal goodness of fit (R2) in the training dataset. To evaluate the prediction ability of the LASSO model, we applied the model to the remaining seven subjects and calculated the R2 as the index of prediction accuracy (see also Materials and Methods and SI Appendix, Supplementary Data Analysis). (B) Distribution of the goodness of fit (R2) on the training dataset (n = 42; 1,000 iterations). (C) Distribution of prediction accuracy (R2) of the selected model applied to the test dataset (n = 7; 1,000 iterations). (D) Selection probability of each predictor in 1,000 iterations. (E–G) The average weights (β) of the selected task, initial performance, and per- sonality trait factors. Error bars represent 1 SD.

reflect the variability of learning ability across individuals. extracting statistical regularities or the capacity to find an ap- These results could have important implications for theories propriate template, which has been demonstrated in action video and applications of perceptual learning across multiple tasks game players (27, 40), or a preferred strategy for updating con- and the development of perceptual expertise and/or remedi- nection weights between sensory representation and decision (1), ation of visual conditions. we would expect to observe a similar pattern of learning for each Individual difference is a universal phenomenon in almost all individual learner across different types of learning. On the other learning domains, including perceptual learning (6, 10, 37, 38), hand, there might be different types of learning ability in dif- motor learning (21, 39), cognitive learning (28), and even gaming ferent learning domains. If that were the case, we would expect a training (17, 40), and across all species (23, 25, 34, 41). Learners consistent pattern of individual differences only within each can exhibit diverse learning rates even in basic visual and audi- learning domain. tory tasks (4, 23, 37, 38). Such individual differences have con- Our results revealed the multifaceted nature of perceptual ventionally been treated as random variations in perceptual learning. With a wide range of training tasks, we were able to learning studies with a single training task (18, 19). In this study, extract a task-specific but subject-invariant component of learning, we observed a consistent pattern of individual differences across consistent with the well-documented phenomenon of task speci- multiple training tasks. The results suggest that there may exist a general perceptual learning ability for each individual that could ficity in perceptual learning (2, 3, 5, 37, 43) and the identification prevail in many perceptual tasks. of multiple sites of brain plasticity across different perceptual We focused on perceptual learning and found that a general learning tasks in physiology and imaging studies (5, 8, 11, 43, 44). perceptual learning ability could account for individual differ- Although there are individual differences, the structure, connec- ences across a range of visual, auditory, and working memory tivity, and functions of the human brain also have a high degree of tasks. Considering the similarities between different types of similarity across individuals, as reflected in, for example, the learning (42), whether the general ability can be extended to postulation of “atypicalobserver” in visual performance (45) and other domains such as skill acquisition, motor learning, and the “standard” topographic sensory maps (46). Such similarity language learning remains an open and interesting question. If across individuals may underlie the task-specific but subject- the general learning ability represents a subject’s efficiency in invariant property of perceptual learning.

19096 | www.pnas.org/cgi/doi/10.1073/pnas.2002903117 Yang et al. Downloaded by guest on September 25, 2021 On the other hand, the subject-specific but task-invariant per- Materials and Methods ceptual learning ability may reflect individual differences in brain Subjects. Forty-nine paid healthy subjects with normal or corrected-to-normal structures, connectivity, and functions as well as perceptual ex- vision (23.4 ± 2.5, 20 y to 30 y old, 22 males) participated in the experiment. All periences. Based on the LASSO regression analysis, we identified of them were right-handed, naïve to psychophysical experiment, and wore their corrective glasses, if necessary, during the whole experiment. None had a number of personality traits (IQ, neuroticism, and agreeable- any psychiatric or neurological disorder. Informed consent was obtained from ness) that contributed significantly to the general perceptual all subjects prior to the study. The study was approved by the Institute’sEthical learning ability. Plomin (18) has argued that general intelligence Committee of Institute of Psychology, Chinese Academy of Sciences. underlies individual differences in diverse cognitive processes. Charles Spearman (47) proposed that a shared intellectual com- Apparatus. Two SONY G220 color monitors with a 1,600 × 1,200 pixel reso- ponent, the well-known “g” component, underlies multiple cog- lution, a refresh rate of 85 Hz, and a 36 cd/m2 background luminance were nitive processes. However, whereas “g” has been widely accepted used in the visual tasks. A special circuit was used to combine two 8-bit (28, 48) and is the basis for many standardized tests such as output channels of the graphics card and enable 14-bit gray-level resolution (55). A third DELL E1912Hc LCD monitor was used to collect responses in the Scholastic Aptitude Test and American College Testing Assess- auditory frequency discrimination task. The experiment was programmed in ment, it has been largely neglected in studies of perceptual func- Matlab (Mathworks) with PsychToolbox extensions (56, 57). tions and associated learning in the past few decades. It has

regained some interest only recently in the examination of inter- Experimental Design. The experiment consisted of seven tasks, including individual variability in human behavior and brain structures, for contrast detection, Vernier offset discrimination, global motion discrimina- example, gray and white matter anatomy (49). A general learning tion, visual shape search, face view discrimination, auditory frequency dis- factor has been extracted from learning a battery of tasks in rodent crimination, and audiovisual N-back working memory. These tasks were studies (34). Considering human behavior as a manifestation of trained sequentially in counterbalanced orders across subjects. Each task was the complicated interplay of environmental and genetic influences trained in five consecutive daily sessions lasting about 40 min to 1 h. For the Vernier offset discrimination, global motion discrimination, face view dis- (50, 51), we suggest that the subject-specific component observed crimination, and auditory frequency discrimination tasks, each session in- in this study is a learner signature that reflects the influence of cluded seven blocks of 100 trials. For the contrast detection and visual shape both genetic and environmental factors on her/his brain to cope search tasks, each session included seven blocks of 96 trials. For the audio- with the changing world. The general regulatory factor could serve visual N-back working memory task, each session included 30 blocks of 20 + as an adaptive information processing module, which might be N trials. In total, each subject received about 23,720 trials of perceptual training (Fig. 1H). We also measured nonverbal IQ, five-factor personality, PSYCHOLOGICAL AND COGNITIVE SCIENCES linked to brain structures (25, 49), connectivity (50), and functions and MA for all subjects using the SPM (30), NEO-FFI (31), and AMS (32), re- (11), and might be genetically controlled (18, 20). Future studies spectively. In the beginning of the first session for each task, subjects re- with a wider range of cognitive and personality trait measures ceived a dozen of instruction examples (10 to 20 trials) to ensure that they might provide a more comprehensive and more detailed analysis understood the task. of the general learning ability. Whereas most of the literature has focused on perceptual Tasks. learning of a single task (6, 8, 9, 37, 38, 44), there is a recent Contrast detection. Subjects viewed the display binocularly at 2.76 m. The surge of investigations on perceptual learning in multiple tasks target stimuli were windowed vertical sinusoidal gratings of 2° in diameter with a half-Gaussian ramp (σ = 0.25°). In each trial, a target stimulus was (52), with a special emphasis on the development of perceptual presented with equal probability in only one of two 100-ms temporal in- expertise and/or remediation of visual conditions (1). The find- tervals separated by 500 ms (Fig. 1A). Subjects were required to judge ing that individual differences in perceptual learning were not whether the target appeared in the first or the second interval by pressing a random fluctuations but reflect variability of a general learning key. The contrast of the grating was controlled by a three-down/one-up ability across individuals also provides some important insights staircase that converges to 79.4% correct (29). The contrast of the target grating was reduced by 10% after three consecutive correct responses and on experimental design and power analysis. More sophisticated increased by 10% after each incorrect response. The initial contrast was set models, such as the linear mixed-effects model or related hier- at 0.6, and the spatial frequency was kept constant at 24 cycles per degree archical Bayesian models (53), are necessary to model the co- for all subjects. Auditory feedback was given on correct responses during variance of learning performance across multiple tasks within training. each individual as well as individual differences across subjects in Vernier offset discrimination. Subjects viewed the display binocularly at 1.38 m. Each trial started with a fixation point lasting 400 ms, followed by a Vernier power analysis of experimental design and in data analysis. In stimulus (contrast = 0.45, spatial frequency = three cycles per degree, and addition, quantifying the general perceptual learning ability of σ = 0.29°) at 5° retinal eccentricity (with a slight position jitter of 0° to 0.25° individuals could have significant implications in selecting po- from trial to trial) in the upper left visual quadrant for 200 ms, and a se- tential trainees in occupations that require perceptual expertise quence of nine small black letters (1.56° × 1.56°) at fixation (Fig. 1B). Subjects and designing better training protocols to improve the efficiency were required to make two judgments after stimuli presentation: first, to of clinical rehabilitations. Training perceptual expertise is usually report the foveal letter (H or N) for fixation control and, then, to report the offset direction of the Vernier stimulus—whether the lower Gabor was to costly and time consuming (1). Selecting individuals who can the left or right of the upper Gabor. The offset threshold was controlled by a more likely or more efficiently acquire the required perceptual three-down/one-up staircase that converges to 79.4% correct (29). The offset expertise could potentially increase the success rate of training of the two Gabors was reduced by 10% after three consecutive correct re- programs. Our previous research on amblyopia (54) indicates sponses and increased by 10% after each incorrect response. The initial that perceptual learning in adults with amblyopia transferred offset was 12.5 arcmin for all subjects. Auditory feedback was given on correct responses during training. more to other stimulus conditions than that of people with Global motion discrimination. Subjects viewed the display binocularly at 0.6 m. In normal vision. The current study suggests that we should inves- each trial (Fig. 1C), subjects were presented with two consecutive stimuli. tigate the general perceptual abilities of patients going through Each stimulus consisted of 400 dots (0.18° × 0.18° each) moving along a perceptual learning treatments. It is possible that patients may single direction at 10°/s within a circular aperture of 8° in diameter. The two possess better abilities to learn in general, and there could be stimuli either moved in the same direction (both 0°) or in different directions (0° and 2.5° or 0° and −2.5°) with the same probability. Each stimulus was important individual differences among the patient pop- presented for 500 ms, with a 200-ms interstimulus interval between them. A ulation(s). Knowing this would allow us to improve rehabilitation small dark fixation point (0.15°) was always present in the center of the protocols. display. Subjects were asked to judge whether the dots in the two intervals

Yang et al. PNAS | August 11, 2020 | vol. 117 | no. 32 | 19097 Downloaded by guest on September 25, 2021 moved in the same direction or not. Auditory feedback was given on correct days. In each day, subjects received training in two minisessions, separated responses during training. Percent correct was tracked during the whole by a 5-min resting period. Each mini session included 15 blocks and lasted experiment. about 25 min. In the first three minisessions, N started from 1; all other Visual shape search. Subjects viewed the display binocularly at 1.5 m. The minisessions started with N = 2. stimuli consisted of a central fixation spot and 24 triangles of four possible Questionnaires for personality traits. The SPM was developed by J. C. Raven to orientations (right, left, up, and down) in the remaining locations on a 5 × 5 directly measure two components: 1) educative ability, the ability to make grid (3.42° × 3.42°) (Fig. 1D). In each trial, the stimulus was presented for 800 meaning out of confusion, the ability to generate high-level, usually non- ms, and subjects were required to judge whether the target (with a down- verbal, schemata which makes it easy to handle complexity; and 2) repro- ward orientation) was present or absent. The target occurred in 75% of the ductive ability, the ability to absorb, recall, and reproduce information that trials with equal probability at all of the 24 locations. The remaining 25% has been made explicit and communicated from one person to another (30). catch trials contained no target. We tracked percent of correct responses The NEO-FFI, developed from the Five-Factor Model, or “Big Five,” assesses and calculated the corresponding d′ scores. A short beep sounded after re- five personality factors: Neuroticism (N), Extraversion (E), Openness to ex- sponse regardless of the correctness of response. perience (O), Agreeableness (A), and Conscientiousness (C) (31). Individuals Face view discrimination. Subjects viewed the display binocularly at 4 m. A three- with high neuroticism scores are more worried, temperamental, and prone dimensional (3D) face model with no hair was produced by FaceGen Modeler to sadness. Extraverts, those got high scores in the dimension of extraver- 3.1 (www.facegen.com/). Face view stimuli were generated by projecting the sion, are adventurous, assertive, frank, sociable, and talkative, while subjects 3D stimulus model with a range of in-depth rotation angles onto the monitor on the other side, so-called introverts, are quiet, reserved, shy, and unso- ± plane with the front view (0°) as the initial position. In each trial, 30° and 30° ciable. People with a high openness trait have broader interests, are liberal, θ ° face views were each presented for 100 ms and separated by a 1,400-ms and embrace novelty. The agreeableness trait is linked to altruism, nurtur- blank interval (Fig. 1E). Their temporal order was randomized, and spatial ance, caring, and emotional support. A person with a high level of consci- × positions were randomly distributed within a 1.43° 1.43° area with its center entiousness tends to focus on a couple of goals and strives hard to achieve coincident with the fixation point. Subjects were asked to make a two- them, while a flexible person is more impulsive and easier to persuade from alternative forced choice judgment of the orientation of the second face rel- one task to another. The AMS measures two factors: the motive to achieve ative to the first face. The orientation of the face view was controlled by a success (Ms) and the motive to avoid failure (Mf) (32). The score of MA is the three-down/one-up staircase that converges to 79.4% correct (29). The dif- difference between Ms and Mf, with higher scores indicating stronger ference between two face views was reduced by 10% after three consecutive striving for achievement. correct responses and increased by 10% after each incorrect response. The initial difference of view was 8° for all subjects. Auditory feedback was given on correct responses during training. Data Analysis. Auditory frequency discrimination. The stimuli were pairs of 100-ms tone bursts Learning performance normalization. We first transformed estimated thresholds (incorporating 10-ms rise−falls) modulated by a raised cosine function in the contrast detection, Vernier acuity, face view discrimination, and au- (Fig. 1F). The interstimulus interval was 500 ms. The signal waveforms were ditory frequency discrimination tasks into perceptual sensitivity (i.e., the computed in each presentation with a sample rate of 44.1 kHz and were reciprocal of threshold), converted performance in the motion direction generated with a 16-bit digital-to-analog converter. Stimuli were presented discrimination and visual search tasks into d′, and calculated the average binaurally with Sennheiser HD600 headphones. The listeners were tested N-back level in the audiovisual N-back working memory task in each training individually in a quiet room. On each trial, the subject was presented with session. We then generated normalized performance scores: Each subject’s two successive tones: one with a fixed frequency of 1,000 Hz and the other performance score in each task and training session was divided by her/his with a frequency of 1,000+Δ Hz. Subjects were required to judge whether performance score in the first training session of the corresponding task,

the higher tone (1,000+Δ) was present in the first or second interval. Dif- that is, y(i, j. t) = yraw(i, j. t)=yraw(i, j,1), where i denotes the ith task, j denotes the ference limen for frequency were estimated using an adaptive two-interval jth subject, and t denotes the tth training session of the task. Normalized forced choice paradigm. The initial frequency difference was 30 Hz, and a performance was applied in all following modeling analysis. three-down/one-up staircase was used to estimate the frequency difference The linear regression model. The following equation was used to fit the learning corresponding to 79.4% correct on the psychometric function (29). The curves and estimate the learning rate: frequency difference between two tones was reduced by 10% after three consecutive correct responses and increased by 10% after each incorrect (^ ) = ( ) ( ( )) log10 y(i, j, t) learning rate i, j *log10 session t , [1] response. Feedback was visually presented for 100 ms on the screen after each response. where i denotes the ith task, j denotes the jth subject, t denotes the tth Audiovisual N-back working memory. Subjects viewed the display binocularly at ^ training session of the task, and y(i, j, t) denotes the predicted performance 1.14 m. Auditory stimuli were presented binaurally with Sennheiser HD600 for each subject in each task in each session. A nonlinear least-square headphones. A sequential N-back paradigm (28) was used in the experiment method, implemented in MATLAB, was used to minimize the sum of (Fig. 1G). Each block consisted of 20 + N trials, in which one visuospatial squared differences between model predictions and observed values, stimulus and one auditory stimulus were presented simultaneously. Each = ∑( ( )− (^ ))2 trial included a 500-ms stimulus interval and a 2,500-ms interstimulus in- SSE log10 y(i, j, t) log10 y(i, j, t) , where SSE is the sum of squared error, terval. The visuospatial stimuli consisted of eight blue squares (2° × 2°) and the goodness of fit was gauged by R2, appearing in eight different loci on the computer screen. The auditory 2 material consisted of eight English consonants (C, H, K, L, Q, R, S, and T) ∑( ( ) − (^ )) log10 y(i,j,t) log10 y(i, j, t) spoken in a male voice and selected by their distinctiveness. Subjects had to 2 = − R 1 2 , [2] ∑( ( ) − ( )) process both modalities independently and simultaneously. The task was to log10 y(i,j,t) log10 y(i, j, t) decide whether the current stimulus matched the one that was presented N trials back in the stimulus stream. Before each block, subjects were informed ^ where y(i, j, t) and y(i, j, t) represent the observed and predicted performance, of the level of N for the current block. Each block included six targets per respectively, and y(i, j, t) is the mean of all observed values. modality, with targets occurring in random positions of the stimulus stream The multivariate linear regression model. The multivariate regression model while holding the number of interfering distractors constant. The first N consisted of 64 parameters, trials in each block were not scored (e.g., in a three-back block, the first three trials were not scored because no target appeared before the fourth (^ ) = ( ( ) + ( ) + ( ) z(i, j) + c) ( ( )) trial). The targets could occur either in only one modality or in both mo- log10 y(i, j, t) task i sub j Initial i * ini * log10 session t , dalities at the same time. Subjects made responses manually by pressing [3] letter “A” of a standard keyboard with their left index finger for visual targets, and letter “L” with their right index for auditory targets. No re- where i denotes the ith task, j denotes the jth subject, and t denotes the tth

sponse was required in nontarget trials. After each block, the subjects’ session, c is the intercept, and z(i,j)ini is the z score of initial performance. An performance was analyzed, and, in the following block, the level of N was ordinary least-square regression method was used to minimize the SSE in fitting adapted accordingly: If the subject made fewer than three mistakes per the model. modality, the level of N was increased by 1; if more than five mistakes were Multiple comparison correction. We used the “p.adjust” function in R and cal- made, N was decreased by 1; in all other cases, N remained unchanged. The culated q values with the Benjamini−Hochberg method to control FDR dual N-back task consisted of 10 minisessions allocated in five consecutive (33, 35).

19098 | www.pnas.org/cgi/doi/10.1073/pnas.2002903117 Yang et al. Downloaded by guest on September 25, 2021 The LASSO regression model. We included 21 predictors, seven task related, nested 10-fold cross-validation procedure, implemented in R, was used seven initial performance related, and seven personality trait related, to to select the optimal λ to minimize the mean squared error (MSE) in the predict subject performance in the LASSO regression model. Data from 42 test fold (CV-MSE). After deciding the optimal λ, we then found subjects were used as the training dataset, and those from the remaining β = {β β β } task , ini , trait that minimized the loss function. In each iteration, we seven were used as the test dataset. LASSO regression solves the l1-penalized also estimated the goodness of fit R2 (Eq. 2) and calculated AIC, β = {β β β } regression problems of finding task, ini , trait to minimize the loss function, AIC =−2ln(L) + 2*K, [5]

∑( ( ( )) − {[β ( ) ( )] + [β ( ) ( ) ] where L denotes the likelihood and K represents the number of predictors in log10 y i, j, t task i *task i ini i *z i, j ini i, j, t the model. + [β (p) z(p, j) ]} log (t))2 [4] trait⃒ * trait * 10 ⃒ + λ∑⃒β ( ) + β ( ) + β ( )⃒ Availability of Data and Code. Anonymized data and code to reproduce the task i ini i trait p , i, p results presented here are available at https://osf.io/dgqxv/ (58).

where y is the observed normalized performance score of the jth subject (i,j,t) ACKNOWLEDGMENTS. The research was supported by National Key Re- β β β at the ith task, the tth session, task, ini,and trait denote unknown regres- search and Development Program of China (Program2020YFC2003800), the sion coefficient for task, initial performance, and personality trait predictors, Scientific Instrument Developing Project of the Chinese Academy of Sciences λ controls the degree of regularization, z(i,j)ini and z(p,j)trait are the z scores (Program ZDKYYQ20200005), Natural Science Foundation of China Grant of initial performance and personality traits (the jth subject’s pth personality NSFC31470983 to C.-B.H., the Scientific Foundation of Institute of Psychol- trait, P = 1, 2, ...,7 represents IQ, neuroticism, extraversion, openness, ogy, Chinese Academy of Sciences Grant Y7CX332008, China Postdoctoral agreeableness, conscientiousness, and MA, respectively). LASSO drives some Science Foundation Grant 2018M641514 to F.-F.Y., and National Eye Institute predictor coefficients to 0 and simplifies the resulting model. A stratified, Grant EY017491 to Z.-L.L.

1. B. Dosher, Z.-L. Lu, Visual perceptual learning and models. Annu. Rev. Vis. Sci. 3, 27. V. R. Bejjanki et al., Action video game play facilitates the development of 343–363 (2017). better perceptual templates. Proc. Natl. Acad. Sci. U.S.A. 111, 16961–16966 2. D. Sagi, Perceptual learning in vision research. Vision Res. 51, 1552–1566 (2011). (2014). 3. T. Watanabe, Y. Sasaki, Perceptual learning: Toward a comprehensive theory. Annu. 28. S. M. Jaeggi, M. Buschkuehl, J. Jonides, W. J. Perrig, Improving fluid intelligence Rev. Psychol. 66, 197–221 (2015). with training on working memory. Proc.Natl.Acad.Sci.U.S.A.105, 6829–6833 4. M. Ahissar, S. Hochstein, Attentional control of early perceptual learning. Proc. Natl. (2008). Acad. Sci. U.S.A. 90, 5718–5722 (1993). 29. H. Levitt, Transformed up-down methods in psychoacoustics. J. Acoust. Soc. Am. 49 5. R. E. Crist, W. Li, C. D. Gilbert, Learning to see: Experience and attention in primary (suppl. 2), 467 (1971). PSYCHOLOGICAL AND COGNITIVE SCIENCES visual cortex. Nat. Neurosci. 4, 519–525 (2001). 30. J. Raven, The Raven’s progressive matrices: Change and stability over culture and 6. Y. Adini, D. Sagi, M. Tsodyks, Context-enabled learning in the human visual system. time. Cognit. Psychol. 41,1–48 (2000). Nature 415, 790–793 (2002). 31. P. T. Costa, R. R. McCrae, “Psychological assessment resources” in NEO PI/FFI Manual 7. A. R. Seitz, D. Kim, T. Watanabe, Rewards evoke learning of unconsciously processed Supplement for Use with the NEO Personality Inventory and the NEO Five-Factor visual stimuli in adult humans. Neuron 61, 700–707 (2009). Inventory, (Psychological Assessment Resources, 1989). 8. N. Chen, P. Cai, T. Zhou, B. Thompson, F. Fang, Perceptual learning modifies the 32. R. Nygård, T. Gjesme, Assessment of achievement motives: Comments and sugges- – functional specializations of visual cortical areas. Proc. Natl. Acad. Sci. U.S.A. 113, tions. Scand. J. Educ. Res. 17,39 46 (1973). 5724–5729 (2016). 33. Y. Benjamini, D. Yekutieli, The control of the false discovery rate in multiple testing – 9. K. Shibata et al., Overlearning hyperstabilizes a skill by rapidly making neurochemical under dependency. Ann. Stat. 29, 1165 1188 (2001). 34. L. D. Matzel et al., Individual differences in the expression of a “general” learning processing inhibitory-dominant. Nat. Neurosci. 20, 470–475 (2017). – 10. M. Sigman, C. D. Gilbert, Learning to find a shape. Nat. Neurosci. 3, 264–269 (2000). ability in mice. J. Neurosci. 23, 6423 6433 (2003). 11. T. Bi, J. Chen, T. Zhou, Y. He, F. Fang, Function and structure of human left fusiform 35. Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995). cortex are closely associated with perceptual learning of faces. Curr. Biol. 24, 222–227 36. R. Tibshirani, Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 58, (2014). 267–288 (1996). 12. H. P. Op de Beeck, C. I. Baker, The neural basis of visual object learning. Trends Cogn. 37. A. Karni, D. Sagi, Where practice makes perfect in texture discrimination: Evidence Sci. 14,22–30 (2010). for primary visual cortex plasticity. Proc. Natl. Acad. Sci. U.S.A. 88, 4966–4970 13. I. Fine, R. A. Jacobs, Comparing perceptual learning across tasks: A review. J. Vis. 2, 5 (2002). (1991). 14. L.-Q. Xiao et al., Complete transfer of perceptual learning across retinal locations 38. D. J. C. Hawkey, S. Amitay, D. R. Moore, Early and rapid perceptual learning. Nature 7, enabled by double training. Curr. Biol. 18, 1922–1926 (2008). 1055–1056 (2004). 15. P. E. Jeter, B. A. Dosher, S. H. Liu, Z. L. Lu, Specificity of perceptual learning increases 39. G. Cantarero, B. Tang, R. O’Malley, R. Salas, P. Celnik, Motor learning interfer- with increased training. Vision Res. 50, 1928–1940 (2010). ence is proportional to occlusion of LTP-like plasticity. J. Neurosci. 33,4634–4641 16. P. Zhang et al., High reward enhances perceptual learning. J. Vis. 18, 11 (2018). (2013). 17. C. S. Green, D. Bavelier, Action video game modifies visual selective attention. Nature 40. V. R. Bejjanki, J. M. Beck, Z.-L. Lu, A. Pouget, Perceptual learning as improved prob- 423, 534–537 (2003). abilistic inference in early sensory areas. Nat. Neurosci. 14, 642–648 (2011). 18. R. Plomin, The genetics of g in human and mouse. Nat. Rev. Neurosci. 2, 136–141 41. A. M. Ni, D. A. Ruff, J. J. Alberts, J. Symmonds, M. R. Cohen, Learning and attention (2001). reveal a general relationship between population activity and behavior. Science 359, 19. J. D. Mollon, J. M. Bosten, D. H. Peterzell, M. A. Webster, Individual differences in 463–465 (2018). visual science: What can be learned and what is good experimental practice? Vision 42. N. Censor, D. Sagi, L. G. Cohen, Common mechanisms of human perceptual and motor – Res. 141,4 15 (2017). learning. Nat. Rev. Neurosci. 13, 658–664 (2012). 20. J. W. Koten Jr. et al., Genetic contribution to variation in cognitive function: An FMRI 43. A. Schoups, R. Vogels, N. Qian, G. Orban, Practising orientation identification im- – study in twins. Science 323, 1737 1740 (2009). proves orientation coding in V1 neurons. Nature 412, 549–553 (2001). 21. P. W. Fox, S. L. Hershberger, T. J. Bouchard Jr., Genetic and environmental contri- 44. Q. Yu, P. Zhang, J. Qiu, F. Fang, Perceptual learning of contrast detection in the – butions to the acquisition of a motor skill. Nature 384, 356 358 (1996). human lateral geniculate nucleus. Curr. Biol. 26, 3176–3182 (2016). 22. E. K. Vogel, M. G. Machizawa, Neural activity predicts individual differences in visual 45. E. R. Kupers, M. Carrasco, J. Winawer, Modeling visual performance differences working memory capacity. Nature 428, 748–751 (2004). “around” the visual field: A computational observer approach. PLoS Comput. Biol. 15, 23. M. Fahle, S. Henke-Fahle, Interobserver variance in perceptual performance and e1007063 (2019). learning. Invest. Ophthalmol. Vis. Sci. 37, 869–877 (1996). 46. L. Wang, R. E. B. Mruczek, M. J. Arcaro, S. Kastner, Probabilistic maps of visual to- 24. A. T. Astle, R. W. Li, B. S. Webb, D. M. Levi, P. V. McGraw, A Weber-like law for pography in human cortex. Cereb. Cortex 25, 3911–3931 (2015). perceptual learning. Sci. Rep. 3, 1158 (2013). 47. C. Spearman, “General intelligence,” objectively determined and measured. Am. 25. S. M. Frank, E. A. Reavis, M. W. Greenlee, P. U. Tse, Pretraining cortical thickness J. Psychol. 15, 201–292 (1904). predicts subsequent perceptual learning rate in a visual search task. Cereb. Cortex 26, 48. Y. Y. Choi et al., Multiple bases of human intelligence revealed by cortical thickness 1211–1220 (2016). and neural activation. J. Neurosci. 28, 10323–10329 (2008). 26. D. Bavelier, C. S. Green, A. Pouget, P. Schrater, Brain plasticity through the life span: 49. R. Kanai, G. Rees, The structural basis of inter-individual differences in human be- Learning to learn and action video games. Annu. Rev. Neurosci. 35, 391–416 (2012). haviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).

Yang et al. PNAS | August 11, 2020 | vol. 117 | no. 32 | 19099 Downloaded by guest on September 25, 2021 50. Z. Yang et al., Genetic and environmental contributions to functional connectivity 54. C.-B. Huang, Y. Zhou, Z.-L. Lu, Broad bandwidth of perceptual learning in the visual architecture of the human brain. Cereb. Cortex 26, 2341–2352 (2016). system of adults with anisometropic amblyopia. Proc. Natl. Acad. Sci. U.S.A. 105, 51. W. Johnson, Genetic and environmental influences on behavior: Capturing all the 4068–4073 (2008). interplay. Psychol. Rev. 114, 423–440 (2007). 55. X. Li, Z.-L. Lu, P. Xu, J. Jin, Y. Zhou, Generating high gray-level resolution mono- 52. M. Maniglia, A. R. Seitz, Towards a whole brain model of Perceptual Learning. Curr. chrome displays with conventional computer graphics cards and color monitors. – Opin. Behav. Sci. 20,47–55 (2018). J. Neurosci. Methods 130,9 18 (2003). 56. D. G. Pelli, The VideoToolbox software for visual psychophysics: Transforming num- 53. A. Bartlema, M. Lee, R. Wetzels, W. Vanpaemel, A Bayesian hierarchical mixture bers into movies. Spat. Vis. 10, 437–442 (1997). approach to individual differences: Case studies in selective attention and represen- 57. D. H. Brainard, Psychophysics software for use with MATLAB. Spat. Vis. 10, 433–436 (1997). tation in category learning. J. Math. Psychol. 59, 132–150 (2014). 58. J. Yang et al, General learning ability in perceptual learning. Open Science Frame- work. https://doi.org/10.17605/OSF.IO/DGQXV. Deposited 21 May 2020.

19100 | www.pnas.org/cgi/doi/10.1073/pnas.2002903117 Yang et al. Downloaded by guest on September 25, 2021