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.

Robust within-session modulations of IAT scores may reveal novel dynamics of rapid change.

The Implicit Association Test (IAT) is employed in the domain of social psychology as a measure of implicit evaluation. Participants in this task complete blocks of trials where they are asked to respond to categories and attributes (e.g., types of faces and types of words). Reaction times in different blocks sharing certain response combinations are averaged and then subtracted from blocks with other response combinations and then normalized, the result of which is taken as a measure indicating implicit evaluation toward or away from the given categories. One assumption of this approach is stationarity of response time distributions, or at a minimum, that temporal dynamics in response times are not theoretically relevant. Here we test these assumptions, examine the extent to which response times change within the IAT blocks and, if so, how trajectories of change are meaningful in relation to external measures. Using multiple data sets we demonstrate within-session changes in IAT scores. Further, we demonstrate that dissociable components in the trajectories of IAT performance may be linked to theoretically distinct processes of cognitive biases as well as behaviors. The present work presents evidence that IAT performance changes within the task, while future work is needed to fully assess the implications of these temporal dynamics.

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.

Working memory is supported by learning to represent items as actions.

Working memory is typically described as a set of processes that allow for the maintenance and manipulation of information for proximal actions, yet the "action" portion of this construct is commonly overlooked. In contrast, neuroscience-informed theories of working memory have emphasized the hierarchical nature of memory representations, including both goals and sensory representations. These two representational domains are combined for the service of actions. Here, we tested whether, as it is commonly measured (i.e., with computer-based stimuli and button-based responses), working memory involved the planning of motor actions (i.e., specific button presses). Next, we examined the role of motor plan learning in successful working memory performance. Results showed that visual working memory performance was disrupted by unpredictable motor mappings, indicating a role for motor planning in working memory. Further, predictable motor mappings were in fact learned over the course of the experiment, thereby causing the measure of working memory to be partially a measure of participants' ability to learn arbitrary associations between visual stimuli and motor responses. Such learning was not highly specific to certain mappings; in sequences of short tasks, participants improved in their abilities to learn to represent items as actions in working memory. We discuss implications for working memory theories in light of hierarchical structure learning and ecological validity.

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.

Testimony bias lingers across development under uncertainty.

Children have a powerful ability to track probabilistic information, but there are also situations in which young learners simply follow what another person says or does at the cost of obtaining rewards. This latter phenomenon, sometimes termed bias to trust in testimony, has primarily been studied in children preschool-age and younger, presumably because reasoning capacities improve with age. Less attention has been paid to situations in which testimony bias lingers-one possibility is that children revert to a testimony bias under conditions of uncertainty. Here, participants (4 to 9 years old) searched for rewards and received testimony that varied in reliability. We find support for testimony bias beyond preschool-age, particularly for uncertain testimony. Children were sensitive to trial-by-trial uncertainty (Experiment 1: N = 102, 59 boys, 43 girls; the sample included nine Hispanic/Latinx, 93 non-Hispanic/Latinx participants, of whom six were Black/African American, seven were Asian American, eight were multiracial, 77 were White, and four indicated "other" or did not respond), and with uncertainty defined as a one-time, unexpected change in the testimony (Experiment 3: N = 129; 68 boys, 61 girls; the sample included 12 Hispanic/Latinx, 117 non-Hispanic/Latinx [10 Black/African American, four Asian American, nine multiracial, 103 White, and three "other"]). However, the impact of the testimony bias decreased with age. These effects were specific to the testimony coming from another person as opposed to resulting from a computer glitch (Experiment 2: N = 89, 52 boys, 37 girls; five Hispanic/Latinx, 80 non-Hispanic/Latinx, of whom one was Black/African American, three were Asian American, 15 were multiracial, 66 were White, and four did not report race). Taken together, these experiments provide evidence of a disproportionate influence of testimony, even in children with more advanced reasoning skills. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Trajectories of performance change indicate multiple dissociable links between working memory and fluid intelligence.

Many areas of psychology assume that performance on tasks of interest is stable through time. Here, using time-sensitive modeling of working memory task performance, we show not only was this assumption incorrect, but that certain components of the performance trajectory (e.g., final task performance; rate of change) were independently predictive of fluid intelligence. This fact has clear implications for theoretical frameworks linking working memory and fluid intelligence, and beyond.

Individual difference predictors of learning and generalization in perceptual learning.

Given appropriate training, human observers typically demonstrate clear improvements in performance on perceptual tasks. However, the benefits of training frequently fail to generalize to other tasks, even those that appear similar to the trained task. A great deal of research has focused on the training task characteristics that influence the extent to which learning generalizes. However, less is known about what might predict the considerable individual variations in performance. As such, we conducted an individual differences study to identify basic cognitive abilities and/or dispositional traits that predict an individual's ability to learn and/or generalize learning in tasks of perceptual learning. We first showed that the rate of learning and the asymptotic level of performance that is achieved in two different perceptual learning tasks (motion direction and odd-ball texture detection) are correlated across individuals, as is the degree of immediate generalization that is observed and the rate at which a generalization task is learned. This indicates that there are indeed consistent individual differences in perceptual learning abilities. We then showed that several basic cognitive abilities and dispositional traits are associated with an individual's ability to learn (e.g., simple reaction time; sensitivity to punishment) and/or generalize learning (e.g., cognitive flexibility; openness to experience) in perceptual learning tasks. We suggest that the observed individual difference relationships may provide possible targets for future intervention studies meant to increase perceptual learning and generalization.

Modulation of compatibility effects in response to experience: Two tests of initial and sequential learning.

Attentional control is a key component of goal-directed behavior. Modulation of this control in response to the statistics of the environment allows for flexible processing or suppression of relevant and irrelevant items in the environment. Modulation occurs robustly in compatibility-based attentional tasks, where incompatibility-related slowing is reduced when incompatible events are likely (i.e., the proportion compatibility effect; PCE). The PCE implicates dynamic changes in the measured compatibility effects that are central to fields of study such as attention, executive functions, and cognitive control. In these fields, stability in compatibility effects are generally assumed, which may be problematic if individual or group differences in measured compatibility effects may arise from differences in statistical learning speed or magnitude. Further, the sequential nature of many studies may lead the learning of certain statistics to be inadvertently applied to future behaviors. Here, we report tests of learning the PCE across conditions of task statistics and sequential blocks. We then test for the influence of feedback on the development of the PCE. We find clear evidence for the PCE, but no conclusive evidence for its slow development through experience. Initial experience with more incompatible trials selectively mitigated performance decreases in a subsequent block. Despite the lack of behavioral changes associated with patterns of learning, systematic within-task changes in compatibility effects remain an important possible source of variation in a wide range of attention research.

Perceptual Learning of Appendicitis Diagnosis in Radiological Images.

A sizeable body of work has demonstrated that participants have the capacity to show substantial increases in performance on perceptual tasks given appropriate practice. This has resulted in significant interest in the use of such perceptual learning techniques to positively impact performance in real-world domains where the extraction of perceptual information in the service of guiding decisions is at a premium. Radiological training is one clear example of such a domain. Here we examine a number of basic science questions related to the use of perceptual learning techniques in the context of a radiology-inspired task. On each trial of this task, participants were presented with a single axial slice from a CT image of the abdomen. They were then asked to indicate whether or not the image was consistent with appendicitis. We first demonstrate that, although the task differs in many ways from standard radiological practice, it nonetheless makes use of expert knowledge, as trained radiologists who underwent the task showed high (near ceiling) levels of performance. Then, in a series of four studies we show that (1) performance on this task does improve significantly over a reasonably short period of training (on the scale of a few hours); (2) the learning transfers to previously unseen images and to untrained image orientations; (3) purely correct/incorrect feedback produces weak learning compared to more informative feedback where the spatial position of the appendix is indicated in each image; and (4) there was little benefit seen from purposefully structuring the learning experience by starting with easier images and then moving on to more difficulty images (as compared to simply presenting all images in a random order). The implications for these various findings with respect to the use of perceptual learning techniques as part of radiological training are then discussed.

Load effects in attention: Comparing tasks and age groups.

Attention is limited in terms of both capacity (i.e., amount of information attended) and selectivity (i.e., the degree to which non-attended information is nonetheless processed). One of the seminal theories in the field, load theory, predicts that these two aspects of attention interact in systematic ways. Specifically, load theory predicts that when the amount of information to attend is less than the available capacity, spare attention will naturally leak out to unattended items. While load theory has found a great deal of empirical support, the robustness of the findings has recently been called into question, in particular with respect to the extent to which the predictions are borne out across different tasks and populations. Here we report tests of perceptual load effects in two different tasks (change detection and enumeration) and in two populations (adults and 7- to 8-year-old children). Adults' accuracies did not demonstrate the predicted interaction between the capacity and selection dimensions, whereas children's performance, in addition to being overall worse than adults, did show the interaction. The overall lower accuracy of children was seen to be the result of a larger performance decrement in response to capacity demands, distracting information, and their interaction. Interestingly, while these results were seen at the level of the two tasks, there was no within-participants correlation across tasks. Overall, these results suggest that maturation-related changes attenuate the magnitude of distractor effects in attention, which in turn limits the evidence for interactions between capacity and selection in high-functioning populations.

Fluid intelligence is related to capacity in memory as well as attention: Evidence from middle childhood and adulthood.

Human fluid intelligence emerges from the interactions of various cognitive processes. Although some classic models characterize intelligence as a unitary "general ability," many distinct lines of research have suggested that it is possible to at least partially decompose intelligence into a set of subsidiary cognitive functions. Much of this work has focused on the relationship between intelligence and working memory, and more specifically between intelligence and the capacity-loading aspects of working memory. These theories focus on domain-general processing capacity limitations, rather than limitations specifically linked to working memory tasks. Performance on other capacity-constrained tasks, even those that have typically been given the label of "attention tasks," may thus also be related to fluid intelligence. We tested a wide range of attention and working memory tasks in 7- to 9-year-old children and adults, and we used the results of these cognitive measures to predict intelligence scores. In a set of 13 measures we did not observe a single "positive manifold" that would indicate a general-ability understanding of intelligence. Instead, we found that a small number of measures were related to intelligence scores. More specifically, we found two tasks that are typically labeled as "attentional measures", Multiple Object Tracking and Enumeration, and two tasks that are typically labeled as "working memory" measures, N-back and Spatial Span, were reliably related to intelligence. However, the links between attention and intelligence scores were fully mediated by working memory measures. In contrast, attention scores did not mediate the relations between working memory and intelligence. Furthermore, these patterns were indistinguishable across age groups, indicating a hierarchical cognitive basis of intelligence that is stable from childhood into adulthood.

"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.

Trial-dependent psychometric functions accounting for perceptual learning in 2-AFC discrimination tasks.

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Perceptual Learning Generalization from Sequential Perceptual Training as a Change in Learning Rate.

With practice, humans tend to improve their performance on most tasks. But do such improvements then generalize to new tasks? Although early work documented primarily task-specific learning outcomes in the domain of perceptual learning [1-3], an emerging body of research has shown that significant learning generalization is possible under some training conditions [4-9]. Interestingly, however, research in this vein has focused nearly exclusively on just one possible manifestation of learning generalization, wherein training on one task produces an immediate boost to performance on the new task. For instance, it is this form of generalization that is most frequently referred to when discussing learning "transfer" [10, 11]. Essentially no work in this domain has focused on a second possible manifestation of generalization, wherein the knowledge or skills acquired via training, despite not being directly applicable to the new task, nonetheless allow the new task to be learned more efficiently [12-15]. Here, in both the visual category learning and visual perceptual learning domains, we demonstrate that sequentially training participants on tasks that share a common high-level task structure can produce faster learning of new tasks, even in cases where there is no immediate benefit to performance on the new tasks. We further show that methods commonly employed in the field may fail to detect or else conflate generalization that manifests as increased learning rate with generalization that manifests as immediate boosts to performance. These results thus lay the foundation for the various routes to learning generalization to be more thoroughly explored.