Childhood unpredictability and the development of exploration.

Early in development, the process of exploration helps children gather new information that fosters learning about the world. Yet, it is unclear how childhood experiences may influence the way humans approach new learning. What influences decisions to exploit known, familiar options versus trying a novel alternative? We found that childhood unpredictability, characterized by unpredictable caregiving and unstable living environments, was associated with reduced exploratory behavior. This effect holds while controlling for individual differences, including anxiety and stress. Individuals who perceived their childhoods as unpredictable explored less and were instead more likely to repeat previous choices (habitual responding). They were also more sensitive to uncertainty than to potential rewards, even when the familiar options yielded lower rewards. We examined these effects across multiple task contexts and via both in-person (N = 78) and online replication (N = 84) studies among 10- to 13-y-olds. Results are discussed in terms of the potential cascading effects of unpredictable environments on the development of decision-making and the effects of early experience on subsequent learning.

Expectation effects in working memory training.

There is a growing body of research focused on developing and evaluating behavioral training paradigms meant to induce enhancements in cognitive function. It has recently been proposed that one mechanism through which such performance gains could be induced involves participants' expectations of improvement. However, no work to date has evaluated whether it is possible to cause changes in cognitive function in a long-term behavioral training study by manipulating expectations. In this study, positive or negative expectations about cognitive training were both explicitly and associatively induced before either a working memory training intervention or a control intervention. Consistent with previous work, a main effect of the training condition was found, with individuals trained on the working memory task showing larger gains in cognitive function than those trained on the control task. Interestingly, a main effect of expectation was also found, with individuals given positive expectations showing larger cognitive gains than those who were given negative expectations (regardless of training condition). No interaction effect between training and expectations was found. Exploratory analyses suggest that certain individual characteristics (e.g., personality, motivation) moderate the size of the expectation effect. These results highlight aspects of methodology that can inform future behavioral interventions and suggest that participant expectations could be capitalized on to maximize training outcomes.

The structure and development of explore-exploit decision making.

A critical component of human learning reflects the balance people must achieve between focusing on the utility of what they know versus openness to what they have yet to experience. How individuals decide whether to explore new options versus exploit known options has garnered growing interest in recent years. Yet, the component processes underlying decisions to explore and whether these processes change across development remain poorly understood. By contrasting a variety of tasks that measure exploration in slightly different ways, we found that decisions about whether to explore reflect (a) random exploration that is not explicitly goal-directed and (b) directed exploration to purposefully reduce uncertainty. While these components similarly characterized the decision-making of both youth and adults, younger participants made decisions that were less strategic, but more exploratory and flexible, than those of adults. These findings are discussed in terms of how people adapt to and learn from changing environments over time.Data has been made available in the Open Science Foundation platform (osf.io).

Going beyond video game consumption when considering Internet Gaming Disorder.

BACKGROUND: Recognizing the crucial importance of understanding the impact of video games on health in today's gaming-dominated world, our study aimed to investigate the relationship between gaming time and Internet Gaming Disorder (IGD). Despite the widespread assumption that a connection exists between both, previous studies have revealed highly variable associations, highlighting significant weaknesses in establishing a robust link. METHODS: To unravel this complex relationship, we recruited two independent samples of League of Legends players. We combined the collection of self-reported and actual gameplay data, together with assessments of mental health, personality traits, and cognitive abilities. RESULTS: Surprisingly, none of the gaming variables demonstrated a robust and stable association with IGD, regardless of whether players spent less than or more than 30 hours per week gaming-a threshold suggested by the American Psychiatric Association as a potential indicator of disordered gaming. Notably, mental health factors, such as anxiety, depression and ADHD, emerged as the most influential predictors of IGD. CONCLUSION: These findings, replicated across two independent samples, challenge the prevailing belief that limiting screen time alone effectively combats IGD. Instead, mental health factors play a crucial role in mitigating risks associated with gaming. Policies focusing solely on restricting screen time are insufficient in reducing the prevalence or symptoms of IGD. Rather, a comprehensive approach that considers mental health and key personality traits must be adopted to safeguard the well-being of individuals engaged in gaming.

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.

Brain plasticity through the life span: learning to learn and action video games.

The ability of the human brain to learn is exceptional. Yet, learning is typically quite specific to the exact task used during training, a limiting factor for practical applications such as rehabilitation, workforce training, or education. The possibility of identifying training regimens that have a broad enough impact to transfer to a variety of tasks is thus highly appealing. This work reviews how complex training environments such as action video game play may actually foster brain plasticity and learning. This enhanced learning capacity, termed learning to learn, is considered in light of its computational requirements and putative neural mechanisms.

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.

Probability Learning: Changes in Behavior Across Time and Development.

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience-within a learning session and over development-influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information.

Action video game play facilitates the development of better perceptual templates.

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.

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.

Brains on video games.

The popular press is replete with stories about the effects of video and computer games on the brain. Sensationalist headlines claiming that video games 'damage the brain' or 'boost brain power' do not do justice to the complexities and limitations of the studies involved, and create a confusing overall picture about the effects of gaming on the brain. Here, six experts in the field shed light on our current understanding of the positive and negative ways in which playing video games can affect cognition and behaviour, and explain how this knowledge can be harnessed for educational and rehabilitation purposes. As research in this area is still in its early days, the contributors of this Viewpoint also discuss several issues and challenges that should be addressed to move the field forward.

Task-specific response strategy selection on the basis of recent training experience.

The goal of training is to produce learning for a range of activities that are typically more general than the training task itself. Despite a century of research, predicting the scope of learning from the content of training has proven extremely difficult, with the same task producing narrowly focused learning strategies in some cases and broadly scoped learning strategies in others. Here we test the hypothesis that human subjects will prefer a decision strategy that maximizes performance and reduces uncertainty given the demands of the training task and that the strategy chosen will then predict the extent to which learning is transferable. To test this hypothesis, we trained subjects on a moving dot extrapolation task that makes distinct predictions for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping for training stimuli, and a general model-based strategy that utilizes humans' default predictive model for a class of trajectories. When the number of distinct training trajectories is low, we predict better performance for the mapping strategy, but as the number increases, a predictive model is increasingly favored. Consonant with predictions, subject extrapolations for test trajectories were consistent with using a mapping strategy when trained on a small number of training trajectories and a predictive model when trained on a larger number. The general framework developed here can thus be useful both in interpreting previous patterns of task-specific versus task-general learning, as well as in building future training paradigms with certain desired outcomes.

Differences in perceptual learning transfer as a function of training task.

A growing body of research--including results from behavioral psychology, human structural and functional imaging, single-cell recordings in nonhuman primates, and computational modeling--suggests that perceptual learning effects are best understood as a change in the ability of higher-level integration or association areas to read out sensory information in the service of particular decisions. Work in this vein has argued that, depending on the training experience, the "rules" for this read-out can either be applicable to new contexts (thus engendering learning generalization) or can apply only to the exact training context (thus resulting in learning specificity). Here we contrast learning tasks designed to promote either stimulus-specific or stimulus-general rules. Specifically, we compare learning transfer across visual orientation following training on three different tasks: an orientation categorization task (which permits an orientation-specific learning solution), an orientation estimation task (which requires an orientation-general learning solution), and an orientation categorization task in which the relevant category boundary shifts on every trial (which lies somewhere between the two tasks above). While the simple orientation-categorization training task resulted in orientation-specific learning, the estimation and moving categorization tasks resulted in significant orientation learning generalization. The general framework tested here--that task specificity or generality can be predicted via an examination of the optimal learning solution--may be useful in building future training paradigms with certain desired outcomes.

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.

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.

Realistic and complex visual chasing behaviors trigger the perception of intentionality.

We not only perceive the physical state of the environment, but also the causal structures underlying the physical state. Determining whether an object has intentionality is a key component of this process. Among all possible intentions, the intention that has arguably been studied the most is chasing-often via a reasonably simple and stereotyped computer algorithm ("heat-seeking"). The current study investigated the perception of multiple types of chasing approaches and thus whether it is the intention of chasing that triggers the perception of chasing, whether the chasing agent and the agent being chased play equally important roles, and whether the perception of chasing requires the presence of both agents. We implemented a well-studied wolf chasing a sheep paradigm where participants viewed recordings of a disc (the wolf) chasing another disc (the sheep) among other distracting discs. We manipulated the types of chasing algorithms, the density of the distractors, the target agent in the task, and the presence of the agent being chased. We found that the participants could successfully identify the chasing agent in all conditions where both agents were present, albeit with different levels of performance (e.g., participants were best at detecting the chasing agent when the chasing agent engaged in a direct chasing strategy and were worst at detecting a human-controlled chasing agent). This work therefore extends our understanding of the types of cues that are and are not utilized by the visual system to detect the chasing intention.

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.

Development of a Novel Method of Assessing Potential Loss of Control in Internet Gaming Disorder.

As video game play has increased in popularity, so too have reports that a subset of individuals play games in a way that causes negative consequences to their lives, which has resulted in the proposed inclusion of Internet Gaming Disorder (IGD) as a formal mental health diagnosis. Given the mass popularity of video games, it is critical that screening materials for this proposed disorder are sufficiently sensitive to ensure that individuals who suffer harm are identified, while those who do not are not mislabeled as such. Here we examined the extent to which participants' responses to a typical IGD questionnaire predicted academic behaviors that could be associated with harm. We recruited 42 college students and tracked their gaming and studying habits weekly over the course of one semester, taking particular note of weeks in which participants did or did not have exams. We predicted that college students overall would spend less time engaging with video games and more time with academics when they had exams, but such modulations would be smaller among individuals with more initial IGD symptoms (i.e., the "more IGD symptoms" group, as compared to the "fewer IGD symptoms" group, would show loss of control). We did not find that college students overall spent less time engaging with video games and more time with academics when they had exams, but post-hoc effect size analyses indicated that our study was underpowered. Implications of potential results using this methodology are discussed and estimates of powerful sample sizes are provided.

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.