Video gaming and competitive eSports have garnered increased empirical attention, although a knowledge gap persists concerning the cognitive underpinnings of performance in individual gaming genres. Like traditional sports players, eSports players require genre-specific skillsets that depend upon distinct cognitive constructs to perform well in their genre. We examined the relations between high-level expertise in different eSports and performance on several cognitive assessments to isolate the processes that most directly support top-level competitive gaming performance. Top-level experts of Fighting and Rhythm game genres outperformed Non-video-game-players on measures of reaction time, paced motor timing, and sustained attention. Fighting Game Experts performed particularly well with sustained attention, and Rhythm Game Experts performed particularly well at paced motor timing. Expert groups did not dramatically differ from one another, suggesting relations with more general gaming expertise rather than game-specific expertise. This research provides preliminary explanation for how genre-specific video game expertise interacts with cognition.

Mathematics, by its very nature, is rife with patterns. However, students frequently treat mathematics as a series of isolated and unlinked exercises. The current study examined whether experience with extending mathematical patterns affected adults’ ability to solve equations that involved patterns and/or to reason about mathematical relationships in new contexts. Participants who were given 13 trials of pattern extension experience then went on to demonstrate both more efficient problem-solving (i.e., faster response times) and more accurate problem-solving at posttest, relative to individuals who were given an equivalent amount of explicit instruction related to solving the equations. However, there was no difference between the groups in the ability to abstract the structure of the underlying mathematical relationships. These findings suggest that patterning tasks like those used in this study may be useful in supporting math performance.

All agents must constantly learn from dynamic environments to optimize their behaviors. For instance, it is necessary in new environments to learn how to distribute attention – i.e., which stimuli are relevant, and thus should be selected for greater processing, and which are irrelevant, and should be suppressed. Despite this, many experiments implicitly assume that attentional control is a static process (by averaging performance over large blocks of trials). By developing and utilizing new statistical tools, here we demonstrate that the effect of flanking items on response times to a central item (often utilized as an index of attentional control) is systematically and continuously influenced through time by the statistics of the flanking items. We discuss the implications of this finding from the perspective of examining individual differences – where traditional data analysis approaches may confound the rate at which attentional filtering changes through time with the asymptotic ability to filter.

In some contexts, human learning greatly exceeds what the sparsity of the available data seems to allow, while in others, it can fall short, despite vast amounts of data. This apparent contradiction has led to separate explanations of humans being equipped either with background knowledge that enhances their learning or with suboptimal mechanisms that hinder it. Here, we reconcile these findings by recognising learners can be uncertain about two structural properties of environments: 1) is there only one generative model or are there multiple ones switching across time; 2) how stochastic are the generative models. We show that optimal learning under these conditions of uncertainty results in learning trade-offs: e.g., a prior for determinism fosters fast initial learning but renders learners susceptible to low asymptotic performance, when faced with high model-stochasticity. Our results reveal the existence of optimal-paths-to-not-learning and reconcile within a coherent framework, phenomena previously considered disparate.

Interleaving, as opposed to blocking, information improves learning of categories, such as artists' painting styles. The current study examined whether presentation schedules also impact memory for specific items. 179 participants studied paintings from 12 different artists on either a blocked or interleaved schedule. In Study 1 (N = 84), participants were then asked to either identify the artists of a series of paintings (style recognition task) or determine whether they had previously seen a specific painting (item recognition task). In Study 2 (N = 93), participants completed both tasks. Results showed that the interleaved schedule led to better learning of the painting styles, but did not impact item memory. However, when participants had to recognize the style and the painting for an artist on the interleaved schedule, they incorrectly thought that they had previously seen the painting. This finding illustrates the dynamic relationship between item memory and category learning.

One challenge that children face when learning from others isthat social agents can behave in unpredictable ways. Socialagents may acquire—or fail to acquire—new information thatinfluences how they interact with the learner. Little is knownabout children’s sensitivity to these changes or howeffectively children update their own behavior in response.Participants (N = 129) searched for rewards while receivingsuggestions from a social agent. The suggestions changed inlevel of reliability over time. All children updated howheavily they weighted the cues after the change. However,younger children were more influenced by their initialexperience with the suggestions, indicating that youngerchildren may have more difficulty disengaging from socialinformation in uncertain learning environments.

Perceptual learning is the process by which experience alters how incoming sensory information is processed by the brain to give rise to behavior—it is critical for how humans educate children, train experts, treat diseases, and promote health and well-being throughout the lifespan. Knowledge of perceptual learning requires basic and applied research in humans and nonhuman animal models, which informs strategic targets for advancing applications. Commercial products to induce perceptual learning are proliferating rapidly with limited regulation (e.g., for rehabilitation), while at the same time basic science is increasingly restricted by changing regulations (such as new granting-agency definitions of clinical trials). Realizing the full potential of perceptual learning requires balancing basic and translational science to advance new knowledge, while serving and protecting consumers. Reforms can promote open, accessible, and representative research, and the translation of this research to applications across different sectors of society.