The Functional Form of Performance Improvements in Perceptual Learning

Transfer of visual perceptual learning over a task-irrelevant feature through feature-invariant representations: Behavioral experiments and model simulations

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Early auditory processing abnormalities alter individual learning trajectories and sensitivity to computerized cognitive training in schizophrenia

BACKGROUND

Auditory system plasticity is a promising target for neuromodulation, cognitive rehabilitation and therapeutic development in schizophrenia (SZ). Auditory-based targeted cognitive training (TCT) is a 'bottom up' intervention designed to enhance the speed and accuracy of auditory information processing, which has been shown to improve neurocognition in certain SZ patients. However, the dynamics of TCT learning as a function of training exercises and their impact on neurocognitive functioning and therapeutic outcomes are unknown.

METHODS

Forty subjects (SZ, n = 21; healthy subjects (HS), n = 19) underwent comprehensive clinical, cognitive, and auditory assessments, including measurements of auditory processing speed (APS) at baseline and after 1-h of TCT. SZ patients additionally completed 30-hours of TCT and repeated assessments ~10-12 weeks later.

RESULTS

SZ patients were deficient in APS at baseline (d = 0.96, p < 0.005) relative to HS. After 1-h of TCT, analyses revealed significant main effects of diagnosis (d = 1.75, p = 0.002) and time (d = 1.04, p < 0.001), and a diagnosis × time interaction (d = 0.85, p = 0.02) on APS. APS learning effects were robust after 1-h in SZ patients (d = 1.47, p < 0.001) and persisted throughout the 30-h of training. Baseline APS was associated with verbal learning gains after 30-h of TCT (r = 0.51, p = 0.02) in SZ.

CONCLUSIONS

TCT learning metrics may have prognostic utility and aid in the prospective identification of individuals likely to benefit from TCT. Future experimental medicine studies may advance predictive algorithms that enhance TCT-related clinical, cognitive and functional outcomes.

The time course of stimulus-specific perceptual learning

Practice on perceptual tasks can lead to long-lasting, stimulus-specific improvements. Rapid stimulus-specific learning, assessed 24 hours after practice, has been found with just 105 practice trials in a face identification task. However, a much longer time course for stimulus-specific learning has been found in other tasks. Here, we examined 1) whether rapid stimulus-specific learning occurs for unfamiliar, non-face stimuli in a texture identification task; 2) the effects of varying practice across a range from just 21 trials up to 840 trials; and 3) if rapid, stimulus-specific learning persists over a 1-week, as well as a 1-day, interval. Observers performed a texture identification task in two sessions separated by one day (Experiment 1) or 1 week (Experiment 2). Observers received varying amounts of practice (21, 63, 105, or 840 training trials) in session 1 and completed 840 trials in session 2. In session 2, one-half of the observers in each group performed the task with the same textures as in session 1, and one-half switched to novel textures (same vs. novel conditions). In both experiments we found that stimulus-specific learning - defined as the difference in response accuracy in the same and novel conditions - increased as a linear function of the log number of session 1 training trials and was statistically significant after approximately 100 training trials. The effects of stimulus novelty did not differ across experiments. These results support the idea that stimulus-specific learning in our task arises gradually and continuously through practice, perhaps concurrently with general learning.

A novel task and methods to evaluate inter-individual variation in audio-visual associative learning

Learning audio-visual associations is foundational to a number of real-world skills, such as reading acquisition or social communication. Characterizing individual differences in such learning has therefore been of interest to researchers in the field. Here, we present a novel audio-visual associative learning task designed to efficiently capture inter-individual differences in learning, with the added feature of using non-linguistic stimuli, so as to unconfound language and reading proficiency of the learner from their more domain-general learning capability. By fitting trial-by-trial performance in our novel learning task using simple-to-use statistical tools, we demonstrate the expected inter-individual variability in learning rate as well as high precision in its estimation. We further demonstrate that such measured learning rate is linked to working memory performance in Italian-speaking (N = 58) and French-speaking (N = 51) adults. Finally, we investigate the extent to which learning rate in our task, which measures cross-modal audio-visual associations while mitigating familiarity confounds, predicts reading ability across participants with different linguistic backgrounds. The present work thus introduces a novel non-linguistic audio-visual associative learning task that can be used across languages. In doing so, it brings a new tool to researchers in the various domains that rely on multi-sensory integration from reading to social cognition or socio-emotional learning.

Informational feedback accelerates learning in multi-alternative perceptual judgements of orientation

Experience or training can substantially improve perceptual performance through perceptual learning, and the extent and rate of these improvements may be affected by feedback. In this paper, we first developed a neural network model based on the integrated reweighting theory (Dosher et al., 2013) to account for perceptual learning and performance in n-alternative identification tasks and the dependence of learning on different forms of feedback. We then report an experiment comparing the effectiveness of response feedback (RF) versus accuracy feedback (AF) or no feedback (NF) (full versus partial versus no supervision) in learning a challenging eight-alternative visual orientation identification (8AFC) task. Although learning sometimes occurred in the absence of feedback (NF), RF had a clear advantage above AF or NF in this task. Using hybrid supervision learning rules, a new n-alternative identification integrated reweighting theory (I-IRT) explained both the differences in learning curves given different feedback and the dynamic changes in identification confusion data. This study shows that training with more informational feedback (RF) is more effective, though not necessary, in these challenging n-alternative tasks, a result that has implications for developing training paradigms in realistic tasks.

Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling

The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in Zhao et al. (submitted): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a Bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical Bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., 2010, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest half width of 68.2% credible interval of the learning curves compared to the BIP and HBMv. The parametric HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.

Multiple timescales of learning indicated by changes in evidence-accumulation processes during perceptual decision-making

Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants' threshold of when enough evidence is present to act.

Learning spatial frequency identification through reweighted decoding

Perceptual learning, the improvement of perceptual judgments with practice, occurs in many visual tasks. There are, however, relatively fewer studies examining perceptual learning in spatial frequency judgments. In addition, perceptual learning has generally been studied in two-alternative tasks, occasionally in n-alternative tasks, and infrequently in identification. Recently, perceptual learning was found in an orientation identification task (eight-alternatives) and was well accounted for by a new identification integrated reweighting theory (I-IRT) (Liu et al., submitted). Here, we examined perceptual learning in a similar eight-alternative spatial frequency absolute identification task in two different training protocols, finding learning in the majority but not all observers. We fit the I-IRT to the spatial frequency learning data and discuss possible model explanations for variations in learning.

Dissociable effects of transcranial direct current stimulation (tDCS) on early and later stages of visual motion perceptual learning

Transcranial direct current stimulation (tDCS) has the potential to benefit visual perceptual learning (VPL). However, previous studies investigated the effect of tDCS on VPL within early sessions, and the influence of tDCS on learning effects at later stages (plateau level) is unclear. Here, participants completed 9 days of training on coherent motion direction identification to reach a plateau (stage 1) and then continued training for 3 days (stage 2). The coherent thresholds were measured before training, after stage 1 and after stage 2. In the first group, anodal tDCS was applied when participants trained over a period of 12 days (stage 1+ stage 2). In the second group, participants completed a 9-day training period without any stimulation to reach a plateau (stage 1); after that, participants completed a 3-day training period while anodal tDCS was administered (stage 2). The third group was treated the same as the second group except that anodal tDCS was replaced by sham tDCS. The results showed that anodal tDCS did not improve posttest performance after the plateau was reached. The comparison of learning curves between the first and third groups showed that anodal tDCS decreased the threshold at the early stage, but it did not improve the plateau level. For the second and third groups, anodal tDCS did not further enhance the plateau level after a continued 3-day training period. These results suggest that anodal tDCS boosts VLP during the early period of training sessions, but it fails to facilitate later learning effects. This study contributed to a deep understanding of the dissociable tDCS effects at distinct temporal stages, which may be due to the dynamic change in brain regions during the time course of VPL.

Repetition learning is neither a continuous nor an implicit process

Learning advances through repetition. A classic paradigm for studying this process is the Hebb repetition effect: Immediate serial recall performance improves for lists presented repeatedly as compared to nonrepeated lists. Learning in the Hebb paradigm has been described as a slow but continuous accumulation of long-term memory traces over repetitions [e.g., Page & Norris, Phil. Trans. R. Soc. B 364, 3737-3753 (2009)]. Furthermore, it has been argued that Hebb repetition learning requires no awareness of the repetition, thereby being an instance of implicit learning [e.g., Guérard et al., Mem. Cogn. 39, 1012-1022 (2011); McKelvie,  J. Gen. Psychol. 114, 75-88 (1987)]. While these assumptions match the data from a group-level perspective, another picture emerges when analyzing data on the individual level. We used a Bayesian hierarchical mixture modeling approach to describe individual learning curves. In two preregistered experiments, using a visual and a verbal Hebb repetition task, we demonstrate that 1) individual learning curves show an abrupt onset followed by rapid growth, with a variable time for the onset of learning across individuals, and that 2) learning onset was preceded by, or coincided with, participants becoming aware of the repetition. These results imply that repetition learning is not implicit and that the appearance of a slow and gradual accumulation of knowledge is an artifact of averaging over individual learning curves.

Efficient neural codes naturally emerge through gradient descent learning

Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural networks trained to recognize objects also have patterns of sensitivity that match the statistics of features in images. To interpret these findings, we show mathematically that learning with gradient descent in neural networks preferentially creates representations that are more sensitive to common features, a hallmark of efficient coding. This effect occurs in systems with otherwise unconstrained coding resources, and additionally when learning towards both supervised and unsupervised objectives. This result demonstrates that efficient codes can naturally emerge from gradient-like learning.

Identifying Long- and Short-Term Processes in Perceptual Learning

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.

Perceptual learning is robust to manipulations of valence and arousal in childhood and adulthood

Despite clear links between affective processes in many areas of cognition and perception, the influence of affective valence and arousal on low-level perceptual learning have remained largely unexplored. Such influences could have the potential to disrupt or enhance learning that would have long-term consequences for young learners. The current study manipulated 8- to 11-year-old children's and young adults' mood using video clips (to induce a positive mood) or a psychosocial stressor (to induce a negative mood). Each participant then completed one session of a low-level visual learning task (visual texture paradigm). Using novel computational methods, we did not observe evidence for the modulation of visual perceptual learning by manipulations of emotional arousal or valence in either children or adults. The majority of results supported a model of perceptual learning that is overwhelmingly constrained to the task itself and independent from external factors such as variations in learners' affect.

Assessing the functions underlying learning using by-trial and by-participant models: Evidence from two visual perceptual learning paradigms

Inferred mechanisms of learning, such as those involved in improvements resulting from perceptual training, are reliant on (and reflect) the functional forms that models of learning take. However, previous investigations of the functional forms of perceptual learning have been limited in ways that are incompatible with the known mechanisms of learning. For instance, previous work has overwhelmingly aggregated learning data across learning participants, learning trials, or both. Here we approach the study of the functional form of perceptual learning on the by-person and by-trial levels at which the mechanisms of learning are expected to act. Each participant completed one of two visual perceptual learning tasks over the course of two days, with the first 75% of task performance using a single reference stimulus (i.e., "training") and the last 25% using an orthogonal reference stimulus (to test generalization). Five learning functions, coming from either the exponential or the power family, were fit to each participant's data. The exponential family was uniformly supported by Bayesian Information Criteria (BIC) model comparisons. The simplest exponential function was the best fit to learning on a texture oddball detection task, while a Weibull (augmented exponential) function tended to be the best fit to learning on a dot-motion discrimination task. The support for the exponential family corroborated previous by-person investigations of the functional form of learning, while the novel evidence supporting the Weibull learning model has implications for both the analysis and the mechanistic bases of the learning.

Effectiveness of Computer-Based Auditory Training for Adult Cochlear Implant Users: A Randomized Crossover Study

The aim of this study was to assess whether a computer-based speech-in-noise auditory training (AT) program would lead to short- and long-term changes in trained and untrained measures of listening, cognition, and quality of life. A secondary aim was to assess whether directly training the underlying cognitive abilities required for speech perception in noise, using a computer-based visual training (VT) program without the auditory component, would elicit comparable outcomes as the AT program. A randomized crossover study with repeated measures was conducted with 26 adult cochlear implant users. Participants completed either 6 weeks of speech perception in noise training followed by 6 weeks of masked text recognition training, or vice versa. Outcome measures were administered twice before each training program, as well as twice after the completion of each program. The test battery was designed to evaluate whether training led to improvements in listening abilities, cognitive abilities, or quality of life. Mixed-effects models were conducted to analyze whether changes occurred on the trained tasks and on untrained outcome measures after training. Statistically significant improvements were shown for verbal recognition performance during both training programs, in particular for consonants in words, and during the first 2 weeks of training. This on-task learning, however, did not lead to clear improvements in outcomes measured beyond the training programs. This suggests that experienced cochlear implant users may not show transfer of on-task learning to untrained tasks after computer-based auditory and visual training programs such as the ones used in this study.

Initial performance predicts improvements in computerized cognitive training: Evidence from a selective attention task

Computerized cognitive training (CCT) has been found to improve a range of skills such as attention, working memory, inhibition control, and decision making. However, the relationship between the initial performance, amount of improvement, time constant, and asymptotic performance level in CCT is still unclear. In the current study, we performed selective attention training on college students and addressed this issue by mathematically modeling the learning curve with an exponential function. Twenty-nine students completed approximately 10 days of CCT. Presentation time served as the dependent variable and was measured by three-down/one-up adaptive algorithms. We fitted an exponential function to the estimated block thresholds during CCT and obtained three learning parameters (amount of improvement, time constant, and asymptotic performance level) for all subjects. The initial performance was defined by the sum of the amount of improvement and the asymptotic performance level. Pearson correlation analyses were conducted between the initial performance and the three leaning parameters. The initial performance was positively correlated with the amount of improvement and asymptotic performance level, but was negatively correlated with the time constant. The time constant was negatively correlated with the amount of improvement and asymptotic performance level. Poorer initial performance was linked to a larger amount of improvement, shorter time constant, and higher asymptotic threshold, which supported the compensation account. Our results may help improve the present understanding of the nature of the CCT process and demonstrate the advantages of using a customized training protocol to enhance the efficiency of cognitive training in practical applications.

An expert advantage in detecting unfamiliar visual signals in noise

Diagnostic radiologists are experts in discriminating and classifying medical images for clinically significant anomalies. Does their perceptual expertise confer an advantage in unfamiliar visual tasks? Here, this issue was investigated by comparing the performance of 10 radiologists and 2 groups of novices on the ability to detect novel visual signals: band-limited textures in noise. Observers performed a yes/no detection task in which texture spatial frequency and external noise levels were varied. The task was performed on two consecutive days. Contrast thresholds and response bias were measured. Contrast thresholds of radiologists were superior to the control groups in all stimulus conditions on both days. Performance improved by an equivalent amount for all groups across days. Response bias differed consistently across stimulus conditions and days but not across groups. The difference in thresholds between the radiologists and control groups suggests that experience in diagnostic medical imaging produces perceptual skills that that transfer beyond the trained domain.

Roving: The causes of interference and re-enabled learning in multi-task visual training

People routinely perform multiple visual judgments in the real world, yet, intermixing tasks or task variants during training can damage or even prevent learning. This paper explores why. We challenged theories of visual perceptual learning focused on plastic retuning of low-level retinotopic cortical representations by placing different task variants in different retinal locations, and tested theories of perceptual learning through reweighting (changes in readout) by varying task similarity. Discriminating different (but equivalent) and similar orientations in separate retinal locations interfered with learning, whereas training either with identical orientations or sufficiently different ones in different locations released rapid learning. This location crosstalk during learning renders it unlikely that the primary substrate of learning is retuning in early retinotopic visual areas; instead, learning likely involves reweighting from location-independent representations to a decision. We developed an Integrated Reweighting Theory (IRT), which has both V1-like location-specific representations and higher level (V4/IT or higher) location-invariant representations, and learns via reweighting the readout to decision, to predict the order of learning rates in different conditions. This model with suitable parameters successfully fit the behavioral data, as well as some microstructure of learning performance in a new trial-by-trial analysis.

Training peripheral vision to read: Reducing crowding through an adaptive training method

Reading is slow and difficult for people with central vision loss who must rely on their peripheral vision. It has been shown that practicing on a letter-recognition task can increase peripheral reading speed, and that the training-related improvement is attributable mainly to reduced crowding. Since there is a high degree of variability in the vision conditions across people with central vision loss, a one-size-fits-all training protocol may not be adequate or appropriate for these patients. In this study, we target two aspects of training-training task and individual customization, and propose a training paradigm that focuses on reducing crowding and tailors training for each individual using an adaptive method. Seven normally-sighted adults were trained with four daily sessions of identifying crowded letters presented at various positions 10° below fixation in a pre/post design. During the training, a dynamic cue (jitter motion) was applied to target letters to modulate crowding. Amplitude of motion was varied on a block by block basis according to individual performance to maintain task difficulty near a pre-defined level (80% accuracy in letter recognition). We found that motion amplitude gradually reduced as training progressed, indicating a reduction in crowding. Following training, reading speed (measured using RSVP method) showed a significant improvement in both the trained (49%) and untrained (50%) visual fields. Despite showing similar improvement as observed in the previous training studies, our adaptive training method demands less effort and, most importantly, offers customization for each individual trainee.

Evaluating the performance of the staircase and quick Change Detection methods in measuring perceptual learning

The staircase method has been widely used in measuring perceptual learning. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed the quick Change Detection (qCD) method and applied it to measure the trial-by-trial time course of dark adaptation. In the current study, we conducted two simulations to evaluate the performance of the 3-down/1-up staircase and qCD methods in measuring perceptual learning in a two-alternative forced-choice task. In Study 1, three observers with different time constants (40, 80, and 160 trials) of an exponential learning curve were simulated. Each simulated observer completed staircases with six step sizes (1%, 5%, 10%, 20%, 30%, and 60%) and a qCD procedure, each starting at five levels (+50%, +25%, 0, −25%, and −50% different from the true threshold in the first trial). We found the following results: Staircases with 1% and 5% step sizes failed to generate more than five reversals half of the time; and the bias and standard deviations of thresholds estimated from the post hoc segment-by-segment qCD analysis were much smaller than those from the staircase method with the other four step sizes. In Study 2, we simulated thresholds in the transfer phases with the same time constants and 50% transfer for each observer in Study 1. We found that the estimated transfer indexes from qCD showed smaller biases and standard deviations than those from the staircase method. In addition, rescoring the simulated data from the staircase method using the Bayesian estimation component of the qCD method resulted in much-improved estimates. We conclude that the qCD method characterizes the time course of perceptual learning and transfer more accurately, precisely, and efficiently than the staircase method, even with the optimal 10% step size.

Change deafness can be reduced, but not eliminated, using brief training interventions

Research on change deafness indicates there are substantial limitations to listeners' perception of which objects are present in complex auditory scenes, an ability that is important for many everyday situations. Experiment 1 examined the extent to which change deafness could be reduced by training with performance feedback compared to no training. Experiment 2 compared the efficacy of training with detailed feedback that identified the change and provided performance feedback on each trial, training without feedback, and no training. We further examined the timescale over which improvement unfolded by examining performance using an immediate post-test and a second post-test 12 h later. We were able to reduce, but not eliminate, change deafness for all groups, and determined that the practice content strongly impacted bias and response strategy. Training with simple performance feedback reduced change deafness but increased bias and false alarm rates, while providing a more detailed feedback improved change detection without affecting bias. Together, these findings suggest that change deafness can be reduced if a relatively small amount of practice is completed. When bias did not impede performance during the first post-test, the majority of the learning following training occurred immediately, suggesting that fast within-session learning primarily supported improvement on the task.

Assessing the detailed time course of perceptual sensitivity change in perceptual learning

The learning curve in perceptual learning is typically sampled in blocks of trials, which could result in imprecise and possibly biased estimates, especially when learning is rapid. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed a Bayesian adaptive quick Change Detection (qCD) method to accurately, precisely, and efficiently assess the time course of perceptual sensitivity change. In this study, we implemented and tested the qCD method in assessing the learning curve in a four-alternative forced-choice global motion direction identification task in both simulations and a psychophysical experiment. The stimulus intensity in each trial was determined by the qCD, staircase or random stimulus selection (RSS) methods. Simulations showed that the accuracy (bias) and precision (standard deviation or confidence bounds) of the estimated learning curves from the qCD were much better than those obtained by the staircase and RSS method; this is true for both trial-by-trial and post hoc segment-by-segment qCD analyses. In the psychophysical experiment, the average half widths of the 68.2% credible interval of the estimated thresholds from the trial-by-trial and post hoc segment-by-segment qCD analyses were both quite small. Additionally, the overall estimates from the qCD and staircase methods matched extremely well in this task where the behavioral rate of learning is relatively slow. Our results suggest that the qCD method can precisely and accurately assess the trial-by-trial time course of perceptual learning.

"Approximate number system" training: A perceptual learning approach

Recent research suggests that humans perceive quantity using a non-symbolic "number sense." This sense is then thought to provide a foundation for understanding symbolic numbers in formal education. Given this link, there has been interest in the extent to which the approximate number system (ANS) can be improved via dedicated training, as this could provide a route to improving performance in symbolic mathematics. However, current evidence regarding the trainability of the ANS comes largely from studies that have used short training durations, leaving open the question of whether improvements occur over a longer time span. To address this limitation, we utilized a perceptual learning approach to investigate the extent to which long-term (8,000+ trials) training modifies the ANS. Consistent with the general methodological approach common in the domain of perceptual learning (where learning specificity is commonly observed), we also examined whether ANS training generalizes to: (a) untrained locations in the visual field; (b) an enumeration task; (c) a higher-level ratio comparison task; and (d) arithmetic ability. In contrast to previous short-term training studies showing that ANS learning quickly asymptotes, our long-term training approach revealed that performance continued to improve even after thousands of trials. We further found that the training generalized to untrained visual locations. At post-test there was non-significant evidence for generalization to a low-level enumeration task, but not to our high-level tasks, including ratio comparison, multi-object tracking, and arithmetic performance. These results demonstrate the potential utility of long-term psychophysical training, but also suggest that ANS training alone (even long-duration training) may be insufficient to modify higher-level math skills.

Efficient assessment of the time course of perceptual sensitivity change

Perceptual sensitivity is usually estimated over trials and time intervals, which results in imprecise and biased estimates when it changes rapidly over time. We develop a novel procedure, the quick Change-Detection (qCD) method, to accurately, precisely, and efficiently assess the trial-by-trial time course of perceptual sensitivity change. Based on Bayesian adaptive testing, qCD selects the optimal stimulus, and updates, trial by trial, a joint probability distribution of the parameters that quantify perceptual sensitivity change over time. We demonstrate the utility of the method in measuring the time course of dark adaptation. Simulations showed that the accuracy and precision of the estimated dark adaptation curve after one qCD run (root mean squared error (RMSE): 0.002; the half width of the 68.2% credible interval (HWCI): 0.016; standard deviation (SD): 0.020; all in log₁₀ units) was higher than those obtained by ten runs of the quick Forced-Choice (qFC) procedure (RMSE: 0.020; HWCI: 0.032; SD: 0.031) and ten runs of a weighted up-down staircase procedure (RMSE: 0.026; SD: 0.031). Further, the dark adaptation curve obtained from one qCD run in a psychophysics experiment was highly consistent with the average of four qFC runs (RMSE = 0.076 log₁₀ units). Overall, qCD provides a procedure to characterize the detailed time course of perceptual sensitivity change in both basic research and clinical applications.

GABA, not BOLD, reveals dissociable learning-dependent plasticity mechanisms in the human brain

Experience and training have been shown to facilitate our ability to extract and discriminate meaningful patterns from cluttered environments. Yet, the human brain mechanisms that mediate our ability to learn by suppressing noisy and irrelevant signals remain largely unknown. To test the role of suppression in perceptual learning, we combine fMRI with MR Spectroscopy measurements of GABA, as fMRI alone does not allow us to discern inhibitory vs. excitatory mechanisms. Our results demonstrate that task-dependent GABAergic inhibition relates to functional brain plasticity and behavioral improvement. Specifically, GABAergic inhibition in the occipito-temporal cortex relates to dissociable learning mechanisms: decreased GABA for noise filtering, while increased GABA for feature template retuning. Perturbing cortical excitability during training with tDCs alters performance in a task-specific manner, providing evidence for a direct link between suppression and behavioral improvement. Our findings propose dissociable GABAergic mechanisms that optimize our ability to make perceptual decisions through training.

When searching for a friend in the crowd or telling identical twins apart, your visual system must solve a complex puzzle. It must ignore all irrelevant information (e.g., unfamiliar faces in the crowd) and focus on key features (e.g., your friend’s familiar face) that will allow you to make a decision. We become better at solving complex visual discriminations with practice. But exactly how the brain achieves this improved performance is unclear.

To answer this question, Frangou et al. trained healthy volunteers on two such visual tasks. The first (target detection task) involved locating a target (e.g. circular shape made of dots among randomly distributed dots in the background), a task similar to identifying a friend in the crowd. The second (feature discrimination task) involved assigning highly alike shapes in two different categories, similar to telling apart identical twins. To solve this problem, volunteers had to identify distinct features that allowed them to distinguishthese shapes. During training on this task, they updated and refined the representation of these distinct features in their brain. This enabled them to make finer discriminations and assign each image correctly to one of the two categories.

While the volunteers trained on the tasks, Frangou et al. measured levels of a chemical called GABA in brain areas that process visual information. GABA is the brain's main inhibitory molecule and controls the activity of neurons. As the volunteers learned the two tasks, their brains showed opposite changes in GABA levels. In the first, target detection task, individuals did better if their GABA decreased during training. In the second, feature discrimination task, they achieved more if their GABA increased during training. To confirm these findings, Frangou et al. used a second technique to activate or suppress processing in visual areas of the brain. Activating visual areas enhanced performance on the target detection task. Suppressing them enhanced performance on the fine discrimination task. These changes are thus consistent with those seen in GABA levels.

As well as revealing how we learn to make decisions based on the information from our eyes, these findings suggest that adjusting brain activity could help patients regain skills lost as a result of eye-related or neurological conditions. Understanding the role of GABA in brain plasticity is also relevant to conditions like autism and psychosis, which have been shown to relate to changes in brain inhibition.

Impact of transcranial direct current stimulation on structural plasticity of the somatosensory system

While there is a growing body of evidence regarding the behavioral and neurofunctional changes in response to the longitudinal delivery of transcranial direct current stimulation (tDCS), there is limited evidence regarding its structural effects. Therefore, the present study was intended to investigate the effect of repeatedly applied anodal tDCS over the primary somatosensory cortex on the gray matter (GM) and white matter (WM) compartment of the brain. Structural tDCS effects were, moreover, related to effects evidenced by functional imaging and behavioral assessment. tDCS was applied over the course of 5 days in 25 subjects with concomitant assessment of tactile acuity of the right and left index finger as well as imaging at baseline, after the last delivery of tDCS and at follow-up 4 weeks thereafter. Irrespective of the stimulation condition (anodal vs. sham), voxel-based morphometry revealed a behaviorally relevant decrease of GM in the precuneus co-localized with a functional change of its activity. Moreover, there was a decrease in GM of the bilateral lingual gyrus and the right cerebellum. Diffusion tensor imaging analysis showed an increase of fractional anisotropy exclusively in the tDCS_anodal condition in the left frontal cortex affecting the final stretch of a somatosensory decision making network comprising the middle and superior frontal gyrus as well as regions adjacent to the genu of the corpus callosum. Thus, this is the first study in humans to identify structural plasticity in the GM compartment and tDCS-specific changes in the WM compartment in response to somatosensory learning.

Learning to recognize letters in the periphery: Effects of repeated exposure, letter frequency, and letter complexity

Patients with central vision loss must rely on their peripheral vision for reading. Unfortunately, limitations of peripheral vision, such as crowding, pose significant challenges to letter recognition. As a result, there is a need for developing effective training methods for improving crowded letter recognition in the periphery. Several studies have shown that extensive practice with letter stimuli is beneficial to peripheral letter recognition. Here, we explore stimulus-related factors that might influence the effectiveness of peripheral letter recognition training. Specifically, we examined letter exposure (number of letter occurrences), frequency of letter use in English print, and letter complexity and evaluated their contributions to the amount of improvement observed in crowded letter recognition following training. We analyzed data collected across a range of training protocols. Using linear regression, we identified the best-fitting model and observed that all three stimulus-related factors contributed to improvement in peripheral letter recognition with letter exposure being the most important factor. As an important explanatory variable, pretest accuracy was included in the model as well to avoid estimate biases and was shown to have influence on the relationship between training improvement and letter exposure. When developing training protocols for peripheral letter recognition, it may be beneficial to not only consider the overall length of training, but also to tailor the number of stimulus occurrences for each letter according to its initial performance level, frequency, and complexity.

Generalization of multisensory perceptual learning

Life in a multisensory world requires the rapid and accurate integration of stimuli across the different senses. In this process, the temporal relationship between stimuli is critical in determining which stimuli share a common origin. Numerous studies have described a multisensory temporal binding window—the time window within which audiovisual stimuli are likely to be perceptually bound. In addition to characterizing this window’s size, recent work has shown it to be malleable, with the capacity for substantial narrowing following perceptual training. However, the generalization of these effects to other measures of perception is not known. This question was examined by characterizing the ability of training on a simultaneity judgment task to influence perception of the temporally-dependent sound-induced flash illusion (SIFI). Results do not demonstrate a change in performance on the SIFI itself following training. However, data do show an improved ability to discriminate rapidly-presented two-flash control conditions following training. Effects were specific to training and scaled with the degree of temporal window narrowing exhibited. Results do not support generalization of multisensory perceptual learning to other multisensory tasks. However, results do show that training results in improvements in visual temporal acuity, suggesting a generalization effect of multisensory training on unisensory abilities.

Associative Learning Through Acquired Salience

Most associative learning studies describe the salience of stimuli as a fixed learning-rate parameter. Presumptive saliency signals, however, have also been linked to motivational and attentional processes. An interesting possibility, therefore, is that discriminative stimuli could also acquire salience as they become powerful predictors of outcomes. To explore this idea, we first characterized and extracted the learning curves from mice trained with discriminative images offering varying degrees of structural similarity. Next, we fitted a linear model of associative learning coupled to a series of mathematical representations for stimulus salience. We found that the best prediction, from the set of tested models, was one in which the visual salience depended on stimulus similarity and a non-linear function of the associative strength. Therefore, these analytic results support the idea that the net salience of a stimulus depends both on the items' effective salience and the motivational state of the subject that learns about it. Moreover, this dual salience model can explain why learning about a stimulus not only depends on the effective salience during acquisition but also on the specific learning trajectory that was used to reach this state. Our mathematical description could be instrumental for understanding aberrant salience acquisition under stressful situations and in neuropsychiatric disorders like schizophrenia, obsessive-compulsive disorder, and addiction.

The Impacts of Video Games on Cognition (and How the Government Can Guide the Industry)

Video game play has become a pervasive part of American culture. The dramatic increase in the popularity of video games has resulted in significant interest in the effects that video gaming may have on the brain and behavior. The scientific research to date indicates that some, but not all, commercial video games do indeed have the potential to cause large-scale changes in a wide variety of aspects of human behavior, including the focus of this review—cognitive abilities. More recent years have seen the rise of a separate form of video games, the so-called “brain games,” or games designed with the explicit goal of enhancing cognitive abilities. Although research on such brain games is still in its infancy, and the results have definitely not been uniformly positive, there is nonetheless reason for continued optimism that custom games can be developed that make a lasting and positive impact on human cognitive skills. Here, we discuss the current state of the scientific literature surrounding video games and human cognition with an emphasis on points critically related to public policy.

Changes across the psychometric function following perceptual learning of an RSVP reading task

Several recent studies have shown that perceptual learning can result in improvements in reading speed for people with macular disease (e.g., Chung, 2011; Tarita-Nistor et al., 2014). The improvements were reported as an increase in reading speed defined by specific criteria; however, little is known about how other properties of the reading performance or the participants' perceptual responses change as a consequence of learning. In this paper, we performed detailed analyses of data following perceptual learning using an RSVP (rapid serial visual presentation) reading task, looking beyond the change in reading speed defined by the threshold at a given accuracy on a psychometric function relating response accuracy with word exposure duration. Specifically, we explored the statistical characteristics of the response data to address two specific questions: was there a change in the slope of the psychometric function and did the improvements in performance occur consistently across different word exposure durations? Our results show that there is a general steepening of the slope of the psychometric function, leading to non-uniform improvements across stimulus levels.

The effect of normal aging and age-related macular degeneration on perceptual learning

We investigated whether perceptual learning could be used to improve peripheral word identification speed. The relationship between the magnitude of learning and age was established in normal participants to determine whether perceptual learning effects are age invariant. We then investigated whether training could lead to improvements in patients with age-related macular degeneration (AMD). Twenty-eight participants with normal vision and five participants with AMD trained on a word identification task. They were required to identify three-letter words, presented 10° from fixation. To standardize crowding across each of the letters that made up the word, words were flanked laterally by randomly chosen letters. Word identification performance was measured psychophysically using a staircase procedure. Significant improvements in peripheral word identification speed were demonstrated following training (71% ± 18%). Initial task performance was correlated with age, with older participants having poorer performance. However, older adults learned more rapidly such that, following training, they reached the same level of performance as their younger counterparts. As a function of number of trials completed, patients with AMD learned at an equivalent rate as age-matched participants with normal vision. Improvements in word identification speed were maintained at least 6 months after training. We have demonstrated that temporal aspects of word recognition can be improved in peripheral vision with training across a range of ages and these learned improvements are relatively enduring. However, training targeted at other bottlenecks to peripheral reading ability, such as visual crowding, may need to be incorporated to optimize this approach.

The challenges of developing a contrast-based video game for treatment of amblyopia

Perceptual learning of visual tasks is emerging as a promising treatment for amblyopia, a developmental disorder of vision characterized by poor monocular visual acuity. The tasks tested thus far span the gamut from basic psychophysical discriminations to visually complex video games. One end of the spectrum offers precise control over stimulus parameters, whilst the other delivers the benefits of motivation and reward that sustain practice over long periods. Here, we combined the advantages of both approaches by developing a video game that trains contrast sensitivity, which in psychophysical experiments, is associated with significant improvements in visual acuity in amblyopia. Target contrast was varied adaptively in the game to derive a contrast threshold for each session. We tested the game on 20 amblyopic subjects (10 children and 10 adults), who played at home using their amblyopic eye for an average of 37 sessions (approximately 11 h). Contrast thresholds from the game improved reliably for adults but not for children. However, logMAR acuity improved for both groups (mean = 1.3 lines; range = 0–3.6 lines). We present the rationale leading to the development of the game and describe the challenges of incorporating psychophysical methods into game-like settings.

Enhanced attentional gain as a mechanism for generalized perceptual learning in human visual cortex

Learning to better discriminate a specific visual feature (i.e., a specific orientation in a specific region of space) has been associated with plasticity in early visual areas (sensory modulation) and with improvements in the transmission of sensory information from early visual areas to downstream sensorimotor and decision regions (enhanced readout). However, in many real-world scenarios that require perceptual expertise, observers need to efficiently process numerous exemplars from a broad stimulus class as opposed to just a single stimulus feature. Some previous data suggest that perceptual learning leads to highly specific neural modulations that support the discrimination of specific trained features. However, the extent to which perceptual learning acts to improve the discriminability of a broad class of stimuli via the modulation of sensory responses in human visual cortex remains largely unknown. Here, we used functional MRI and a multivariate analysis method to reconstruct orientation-selective response profiles based on activation patterns in the early visual cortex before and after subjects learned to discriminate small offsets in a set of grating stimuli that were rendered in one of nine possible orientations. Behavioral performance improved across 10 training sessions, and there was a training-related increase in the amplitude of orientation-selective response profiles in V1, V2, and V3 when orientation was task relevant compared with when it was task irrelevant. These results suggest that generalized perceptual learning can lead to modified responses in the early visual cortex in a manner that is suitable for supporting improved discriminability of stimuli drawn from a large set of exemplars.

Dissociable mechanisms of speed-accuracy tradeoff during visual perceptual learning are revealed by a hierarchical drift-diffusion model

Two phenomena are commonly observed in decision-making. First, there is a speed-accuracy tradeoff (SAT) such that decisions are slower and more accurate when instructions emphasize accuracy over speed, and vice versa. Second, decision performance improves with practice, as a task is learnt. The SAT and learning effects have been explained under a well-established evidence-accumulation framework for decision-making, which suggests that evidence supporting each choice is accumulated over time, and a decision is committed to when the accumulated evidence reaches a decision boundary. This framework suggests that changing the decision boundary creates the tradeoff between decision speed and accuracy, while increasing the rate of accumulation leads to more accurate and faster decisions after learning. However, recent studies challenged the view that SAT and learning are associated with changes in distinct, single decision parameters. Further, the influence of speed-accuracy instructions over the course of learning remains largely unknown. Here, we used a hierarchical drift-diffusion model to examine the SAT during learning of a coherent motion discrimination task across multiple training sessions, and a transfer test session. The influence of speed-accuracy instructions was robust over training and generalized across untrained stimulus features. Emphasizing decision accuracy rather than speed was associated with increased boundary separation, drift rate and non-decision time at the beginning of training. However, after training, an emphasis on decision accuracy was only associated with increased boundary separation. In addition, faster and more accurate decisions after learning were due to a gradual decrease in boundary separation and an increase in drift rate. The results suggest that speed-accuracy instructions and learning differentially shape decision-making processes at different time scales.

On methodological standards in training and transfer experiments

The past two decades have seen a tremendous surge in scientific interest in the extent to which certain types of training-be it aerobic, athletic, musical, video game, or brain trainer-can result in general enhancements in cognitive function. While there are certainly active debates regarding the results in these domains, what is perhaps more pressing is the fact that key aspects of methodology remain unsettled. Here we discuss a few of these areas including expectation effects, test-retest effects, the size of the cognitive test battery, the selection of control groups, group assignment methods, difficulties in comparing results across studies, and in interpreting null results. Specifically, our goal is to highlight points of contention as well as areas where the most commonly utilized methods could be improved upon. Furthermore, because each of the sub-areas above (aerobic training through brain training) share strong similarities in goal, theoretical framework, and experimental approach, we seek to discuss these issues from a general perspective that considers each as members of the same broad "training" domain.

The effect of sleep in perceptual learning with complex objects

Recognition of objects improves with training, but task performance also improves between sessions without further training. This offline learning seems to be influenced by post-training sleep, as is evidenced in perceptual learning studies with simple stimuli. In this study we aim to investigate the role of sleep in perceptual learning with complex natural and man-made objects. Participants were trained with a backward masking task during four sessions with 12h between each training session (morning-evening-morning-evening or evening-morning-evening-morning). A larger improvement on performance was found after a night's sleep, than when subjects performed the task without having slept between training sessions. This effect was not influenced by the participants' chronotype or non-verbal intelligence. In addition, we replicated some key characteristics of perceptual learning with complex objects. Participants were retested six days after the last training session with the previously trained stimulus and new stimuli. The performance gains were long-lasting and specific to the trained stimulus set.

An integrated reweighting theory of perceptual learning

Improvements in performance on visual tasks due to practice are often specific to a retinal position or stimulus feature. Many researchers suggest that specific perceptual learning alters selective retinotopic representations in early visual analysis. However, transfer is almost always practically advantageous, and it does occur. If perceptual learning alters location-specific representations, how does it transfer to new locations? An integrated reweighting theory explains transfer over retinal locations by incorporating higher level location-independent representations into a multilevel learning system. Location transfer is mediated through location-independent representations, whereas stimulus feature transfer is determined by stimulus similarity at both location-specific and location-independent levels. Transfer to new locations/positions differs fundamentally from transfer to new stimuli. After substantial initial training on an orientation discrimination task, switches to a new location or position are compared with switches to new orientations in the same position, or switches of both. Position switches led to the highest degree of transfer, whereas orientation switches led to the highest levels of specificity. A computational model of integrated reweighting is developed and tested that incorporates the details of the stimuli and the experiment. Transfer to an identical orientation task in a new position is mediated via more broadly tuned location-invariant representations, whereas changing orientation in the same position invokes interference or independent learning of the new orientations at both levels, reflecting stimulus dissimilarity. Consistent with single-cell recording studies, perceptual learning alters the weighting of both early and midlevel representations of the visual system.

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

Our brain is inexorably confronted with a dynamic environment in which it has to fine-tune spatiotemporal representations of incoming sensory stimuli and commit to a decision accordingly. Among those representations needing constant calibration is interval timing, which plays a pivotal role in various cognitive and motor tasks. To investigate how perceived time interval is adjusted by experience, we conducted a human psychophysical experiment using an implicit interval-timing task in which observers responded to an invisible bar drifting at a constant speed. We tracked daily changes in distributions of response times for a range of physical time intervals over multiple days of training with two major types of timing performance, mean accuracy and precision. We found a decoupled dynamics of mean accuracy and precision in terms of their time course and specificity of perceptual learning. Mean accuracy showed feedback-driven instantaneous calibration evidenced by a partial transfer around the time interval trained with feedback, while timing precision exhibited a long-term slow improvement with no evident specificity. We found that a Bayesian observer model, in which a subjective time interval is determined jointly by a prior and likelihood function for timing, captures the dissociative temporal dynamics of the two types of timing measures simultaneously. Finally, the model suggested that the width of the prior, not the likelihoods, gradually shrinks over sessions, substantiating the important role of prior knowledge in perceptual learning of interval timing.

Tactile perceptual learning: learning curves and transfer to the contralateral finger

Tactile perceptual learning has been shown to improve performance on tactile tasks, but there is no agreement about the extent of transfer to untrained skin locations. The lack of such transfer is often seen as a behavioral index of the contribution of early somatosensory brain regions. Moreover, the time course of improvements has never been described explicitly. Sixteen subjects were trained on the Ludvigh task (a tactile vernier task) on four subsequent days. On the fifth day, transfer of learning to the non-trained contralateral hand was tested. In five subjects, we explored to what extent training effects were retained approximately 1.5 years after the final training session, expecting to find long-term retention of learning effects after training. Results showed that tactile perceptual learning mainly occurred offline, between sessions. Training effects did not transfer initially, but became fully available to the untrained contralateral hand after a few additional training runs. After 1.5 years, training effects were not fully washed out and could be recuperated within a single training session. Interpreted in the light of theories of visual perceptual learning, these results suggest that tactile perceptual learning is not fundamentally different from visual perceptual learning, but might proceed at a slower pace due to procedural and task differences, thus explaining the apparent divergence in the amount of transfer and long-term retention.

The rapid emergence of stimulus specific perceptual learning

Is stimulus specific perceptual learning the result of extended practice or does it emerge early in the time course of learning? We examined this issue by manipulating the amount of practice given on a face identification task on Day 1, and altering the familiarity of stimuli on Day 2. We found that a small number of trials was sufficient to produce stimulus specific perceptual learning of faces: on Day 2, response accuracy decreased by the same amount for novel stimuli regardless of whether observers practiced 105 or 840 trials on Day 1. Current models of learning assume early procedural improvements followed by late stimulus specific gains. Our results show that stimulus specific and procedural improvements are distributed throughout the time course of learning.

Accounting for speed-accuracy tradeoff in perceptual learning

In the perceptual learning (PL) literature, researchers typically focus on improvements in accuracy, such as d'. In contrast, researchers who investigate the practice of cognitive skills focus on improvements in response times (RT). Here, we argue for the importance of accounting for both accuracy and RT in PL experiments, due to the phenomenon of speed-accuracy tradeoff (SAT): at a given level of discriminability, faster responses tend to produce more errors. A formal model of the decision process, such as the diffusion model, can explain the SAT. In this model, a parameter known as the drift rate represents the perceptual strength of the stimulus, where higher drift rates lead to more accurate and faster responses. We applied the diffusion model to analyze responses from a yes-no coherent motion detection task. The results indicate that observers do not use a fixed threshold for evidence accumulation, so changes in the observed accuracy may not provide the most appropriate estimate of learning. Instead, our results suggest that SAT can be accounted for by a modeling approach, and that drift rates offer a promising index of PL.

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning

Background

The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session.

Methodology/Principal Findings

We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased.

Conclusions/Significance

Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

Rule-based learning explains visual perceptual learning and its specificity and transfer

Visual perceptual learning models, as constrained by orientation and location specificities, propose that learning either reflects changes in V1 neuronal tuning or reweighting specific V1 inputs in either the visual cortex or higher areas. Here we demonstrate that, with a training-plus-exposure procedure, in which observers are trained at one orientation and either simultaneously or subsequently passively exposed to a second transfer orientation, perceptual learning can completely transfer to the second orientation in tasks known to be orientation-specific. However, transfer fails if exposure precedes the training. These results challenge the existing specific perceptual learning models by suggesting a more general perceptual learning process. We propose a rule-based learning model to explain perceptual learning and its specificity and transfer. In this model, a decision unit in high-level brain areas learns the rules of reweighting the V1 inputs through training. However, these rules cannot be applied to a new orientation/location because the decision unit cannot functionally connect to the new V1 inputs that are unattended or even suppressed after training at a different orientation/location, which leads to specificity. Repeated orientation exposure or location training reactivates these inputs to establish the functional connections and enable the transfer of learning.

Specificity of perceptual learning increases with increased training

Perceptual learning often shows substantial and long-lasting changes in the ability to classify relevant perceptual stimuli due to practice. Specificity to trained stimuli and tasks is a key characteristic of visual perceptual learning, but little is known about whether specificity depends upon the extent of initial training. Using an orientation discrimination task, we demonstrate that specificity follows after extensive training, while the earliest stages of perceptual learning exhibit substantial transfer to a new location and an opposite orientation. Brief training shows the best performance at the point of transfer. These results for orientation-location transfer have both theoretical and practical implications for understanding perceptual expertise.