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The field of psychometrics has undergone substantial evolution over the past several decades, both in terms of advances in methodology and improved software and hardware for deploying new methods. Despite these strides, many of these developments have not been integrated into the broader field of psychology, as highlighted by Embretson (2005) and Borsboom (2006). Understanding and incorporating these psychometric advances is crucial to enable cognitive scientists to address growing concerns about validity and reliability, as well as to develop robust theoretical frameworks for understanding cognition.

What impacts what we remember about objects we have just encountered? Influential theories of learning suggest that more active engagement leads to stronger memories than passive observation. However, it is not clear which aspects of interaction lead to stronger memories, nor what kinds of memories are supported by active engagement. Here we conduct several experiments to investigate the impact of assembling an object on subsequent recognition and recall performance. We found that reconstructing a block tower by copying it part-by-part could impair subsequent memory for that tower, compared to passively viewing that tower. By contrast, when participants initially encoded each tower by building it from working memory, their subsequent recall was enhanced relative to when they held the tower in working memory without building it. Together our results suggest a complex relationship between the nature of our interactions with objects and our subsequent memories of them.

Current models of cognitive control frame its allocation as a process of expected utility maximization. The benefits of a candidate action are weighed against the costs of that control allocation (e.g. opportunity costs). Recent theorizing has found that it is normative to account for the value of learning when determining control allocation. Here, we sought to test whether learning expectations could explain people's initial control allocation in a standard dot-motion perceptual task. We found that subjects' initial skill level and learning rate in a first block were able to predict their initial willingness to accumulate evidence in a second block, interpreted as a greater control allocation for the task. Our findings support the hypothesis that agents consider the learnability of a task when deciding how much cognitive control to allocate to that task.

Code Knowledge Tracing (CKT) aims to model students' programming proficiency from their coding activities. Existing approaches mainly rely on answer records and lack problem descriptions and knowledge concepts, which fail to capture the inherent information. To solve this problem, we propose ECKT, an Enhanced Code Knowledge Tracing framework using Large Language Models (LLMs), which simulate human cognitive process through chain-of-thought prompting and adapts quickly to new tasks with limited data using few-shot learning. Specifically, ECKT generates detailed problem descriptions and knowledge concepts from student code, enhancing the model's understanding of programming concepts and proficiency. Additionally, ECKT incorporates task difficulty information by correlating problems with difficulty levels based on student performance scores. This integration allows for a more accurate assessment of student proficiency across varying levels of difficulty. Also, ECKT can explicitly capture the essential information of code and learn a better representation of them. Experimental results demonstrate that ECKT effectively improves the performance of knowledge tracing in programming education. This advancement not only supports personalized learning but also contributes to a deeper understanding of coding activities.

Recent advancements in Artificial Intelligence (AI) have arguably enhanced human performance instead of supplanting it. Here we analyse 2.8 million decisions by elite chess players, a field emblematic of AI's application due to its complexity and objective measurability. We identify two AI milestones that correspond with substantial enhancements in top chess players' performance quality over the past four decades: the introduction of personal computers (PCs) and internet access in the late 1990s, and the advent of deep neural networks for chess in the late 2010s. The impact of these technologies, however, varied by age group; adult elite players derived considerable benefits from neural network-based chess computers, whereas younger top players were more influenced by the widespread availability of knowledge and PCs. Our findings underscore AI's potential to amplify human proficiency in complex tasks, highlighting the importance of tailored technological integration among elite performers.

We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance is significantly better. The same held true for meta-reinforcement learning agents that differed in whether or not they receive contextual information when performing the task. We evaluated their accuracy in predicting human performance in the task and analyzed their internal representations. The results indicate that contextual cues allow the formation of separate representations in space and time when using different visuomotor mappings, whereas the absence of them favors sharing one representation. While both strategies can allow learning of multiple visuomotor mappings, we showed contextual cues provide a computational advantage in terms of how many mappings can be learned.

Bipolar disorder (BD) is a mental health condition characterized by large fluctuations in goal-directed energy and mood. BD is defined by the presence of at least one lifetime episode of mania, a prolonged period of excessive goal-directed behavior, hyperactivity and elevated mood. Previous computational models of BD have primarily focused on explaining mood fluctuations in mania, placing less emphasis on goal-directed symptoms. In this work, we use reinforcement learning (RL), a principled model of goal-directed behavior and learning, to show how augmenting RL agents with \textit{dynamic self-efficacy beliefs} can give rise to goal-directed and mood symptoms characteristic of the mania phase of BD. Our simulations demonstrate that a model-free RL agent that dynamically updates its self-efficacy beliefs learns optimistic overgeneralized value representations. We suggest that these representations may underlie several behaviors associated with mania, such as increased motivational drive and faster initiation of approach behavior (i.e. impatience). We further show that agents with more sensitive self-efficacy beliefs display increased willingness to exert effort in order to achieve higher goals even in the face of costs, a characteristic that is observed in individuals at risk for BD. Finally, unrealistically high self-efficacy beliefs that emerged with learning were accompanied by behaviors such as distractibility and compulsive action selection that have clinical parallels to symptoms of mania.

Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on a melody task to study the implications of this trade-off, and show that constructing a shared program library across tasks provides global benefits. However, this comes at the cost of sensitivity to curricula, which is also characteristic of human learners. Finally, we use methods from partial information decomposition to generate training curricula that induce more effective libraries and better generalization.

In this paper, we developed three ACT-R cognitive models to simulate the learning process of abacus gestures. Abacus gestures are mid-air gestures, each representing a number between 0 and 99. Our models learn to predict the response time of making an abacus gesture. We found the accuracy of a model's predictions depends on the structure of its declarative memory. A model with 100 chunks cannot simulate human response, whereas models using fewer chunks can, as segmenting chunks increase both the frequency and recency of information retrieval. Furthermore, our findings suggest that the mind is more likely to represent abacus gestures by dividing attention between two hands rather than memorizing and outputting all gestures directly. These insights have important implications for future research in cognitive science and human-computer interaction, particularly in developing vision and motor modules for mental states in existing cognitive architectures and designing intuitive and efficient mid-air gesture interfaces.

Transcription with Incremental Presentation of the Stimulus (TIPS) is a novel approach relying on micro-behaviours proposed by Colarusso and colleagues (2023) to study users' cognition with data visualizations. The study in this paper has two primary objectives: (a) investigate whether TIPS can measure an individual's competence with data visualizations; and (b) explore the potential enhancement of TIPS measures by normalizing them with the individual's performance on tests of visuo-spatial abilities and memory capacity. We test 30 participants with different expertise and cognitive skills. Results reveal that TIPS provides some promise for individual competence assessment, but only when normalized with the individual's performance on a test of rigid transformation of mental images. Other tests measuring visuospatial abilities or memory capacity did not produce effective normalizations.

Latin American dancing relies heavily on Cuban Motion (CM) to captivate audiences with its powerful, enchanting, and beautiful expressions. We researched how skilled dancers' CM movements influence audience perceptions. We compared CM movement and evaluations between different skill levels. From previous research, we hypothesize that expert's CM is symmetrical and involves whole-body coordination. Result showed that the heel and other body parts were coordinated in the R-L direction and that the hip trajectory in the horizontal plane was highly circular. However, contrary to the hypothesis, the symmetrical feature of the CM's hip trajectories of experts was divided into two groups: symmetrical / asymmetrical expert. In the evaluation results, the symmetrical group was evaluated higher for factor of Aesthetics and Dynamism, while the asymmetrical group was lower.

We introduce the concept of high-performance cognition as a domain-general function of acquiring and performing cognitively demanding skills to a high level. We conduct a survey among academic experts to identify key attention categories of high-performance cognition: by independent consensus they highlight the importance of goal-directed attention. Selective, focused, and sustained attention are strongly associated at slightly less complete consensus. They qualify their ratings with free-text reflections. Our work offers a new framing for skilled performance and its underlying cognitive processes.

Various methods of dimensionality reduction have been used to apply a quantitative approach to the study of complex skill acquisition. This work builds upon past approaches, offering a comparative analysis of principal component analysis, logistic regression, and linear discriminant analysis to quantify expertise in the domain of competitive video gaming, or “eSports.” We present a novel, robust dataset of expert and non-expert gameplay data from professional and amateur players of the Super Smash Bros. Melee competitive fighting game. We assess each quantitative model via the metrics of providing accurate expertise classification, predictive utility, and a pragmatic window into the features of complex skill performance that hold the most weight in overall performance outcomes, thereby also providing insights for direction of future training. We posit that linear discriminant analysis provides the best performance for all relevant metrics. The nuances are discussed here, and suggestions for the field are offered for future study of other complex skill domains.

The way working memory, attention, and long-term memory interact is an important question given the role these cognitive systems play in many tasks. In this paper, we present a study examining prior counterintuitive results that show that prioritization of some stimuli aids learning but hurts performance at a delayed test. In this study, we use eye tracking to measure attention consistency, to examine the effect of prioritization and working memory load on recall accuracy. The goal was to assess two possible explanations of the negative effect of prioritization on a delayed test. Our results indicate that prioritization reduces response time and increases accuracy during learning of associations. However, the negative effect of prioritization on a delayed test is replicated with participants showing higher accuracy for non-prioritized items during testing. Measures of attention shifting and consistency impact learning performance but do not explain the negative prioritization effect at test.

Studies on interactions between procedural and declarative learning have focused on largely on competition during encoding, consolidation, or use (retrieval). Less attention has been paid to interactions between the representations created by each system. In a behavioral study, we demonstrated that information from both declarative and procedural learning can contribute to response selection. Participants were instructed to use a completely diagnostic, verbalizable, shape-based rule to categorize exemplars and received feedback after each trial. However, the categories also differed probabilistically in their color distributions. Participants used both color (learned procedurally) and shape (learned declaratively) to categorize exemplars, making faster responses when both sources indicated the same category judgement, and slower when they conflicted. Debriefing confirmed that most participants were unaware of the color distributions (aware participants were analyzed separately). This result suggests that both the color (procedural) and shape (declarative) information contributed to response selection.

In this paper we introduce a novel way of quantifying typing expertise according to the ability to type without visual guidance from the keyboard (i.e., in a blind typing task). We present results of two experiments showing that performance in blind typing allows dissociating two profiles of typists, touch and non-touch typists. In Experiment 1, analyzing more than 100 typists, we show that performance in blind typing correlates with faster typing speed of lexical and non-lexical material, but not with low-level motoric skills. In Experiment 2, we show that touch and non-touch typists present differences in both written and spoken language production, but not language perception. Our results demonstrate that the characterization of “everyday touch typists” not only discriminates typing skills but may also capture distinct cognitive abilities. Spanning the fields of sensorimotor and linguistic processing, this study stresses the importance of considering language processing to understand typing skills.

While it is difficult to find and persuade research participants to invest the famous 10,000 hours of practice necessary to develop expertise in any given task, one can more easily commit oneself to such a devoted undertaking. Through autoethnographic observation, the author, a retired semi-professional eSports competitor with no experience or knowledge of the new competitive eSport game Street Fighter 6, documented and livestreamed months of gameplay sessions as he acquired expertise and rose through the ranks of the game's competitive online mode, striving to reach the game's highest ranking of “Master.” The author critically examines the strategies and practices most useful for optimizing learning and performance – illustrating the contributions of reflexivity and reflection that are often overlooked in laboratory experimentation. Overall, this work demonstrates how autoethnographic insights developed “in the streets,” when combined with empirical research in the lab, contribute to a fuller picture of learning and expertise.

This research studies how 24 experts in sport, music, and business were able to make successful and repeated career transitions to senior levels in their field. It examined – among other aspects – the roles of cognitive flexibility, personal intelligence, generative thinking, motivation, and contextual intelligence in career transitions. It also examined how identity changes and adapts during a career transition and how self concept evolves over the course of a career.In-depth interviews were analysed both qualitatively and quantitatively and served as the basis for evaluating several theories of expertise, cognition, motivation, and intelligence. Key findings include: deliberate practice was rarely mentioned as a contributor to transitions; the early development of expertise in multiple domains contributed to its generalizability; transition expertise evolved over the course of a career; and self concept did not unfold in a linear progression of sequential stages as predicted by many theories in the field.

Decades of work exploring the development of children's analogical reasoning illustrates that 3- and 4-year-old children struggle with reasoning by analogy (i.e. glove:hand::sock:___), almost always preferring superficially related “object matches” (:shoe) over “relational matches” (:foot). However, one recent study demonstrated preschoolers' ability to choose relational matches when a traditional relational-match-to-sample task is embedded in causal scaffolding, framing the target abstract relation as one between beginning and ending states of a causal transformation. Current work aims to discover which factors of causal framing facilitate this boost in early abstract reasoning. In Study 1, we replicate this effect while adapting the transformation to involve two objects, showing that preservation of identity is not necessary for analogical reasoning in a causal context. In Study 2, we explore the replicated effect in a case of non-agentive causation, finding that the causal boost, while still present, is significantly weaker when scaffolding involves a machine vs. an agent. These findings demonstrate that causal framing can be a powerful tool in bolstering children's early abstract reasoning capabilities and show that this enhancing effect is even stronger when an agent holds causal power.

The interplay between cognition and gaming, notably through educational games enhancing cognitive skills, has garnered significant attention in recent years. This research introduces the CogSimulator, a novel algorithm for simulating user cognition in small-group settings with minimal data, as the educational game Wordle exemplifies. The CogSimulator employs Wasserstein-1 distance and coordinates search optimization for hyperparameter tuning, enabling precise few-shot predictions in new game scenarios. Comparative experiments with the Wordle dataset illustrate that our model surpasses most conventional machine learning models in mean Wasserstein-1 distance, mean squared error, and mean accuracy, showcasing its efficacy in cognitive enhancement through tailored game design.

Sustained attention is a fundamental cognitive ability that influences various aspects of human functioning. Studies of the neural correlates of attention commonly treat sustained attention as an isolated construct, however in any ecological context, sustained attention interacts with other executive functions such as inhibition of interference and processing of complex hierarchical stimuli. We have thus constructed a protocol to probe the interplay between these cognitive processes during visual attention task. We contrast putative typical vs atypical attention by comparing 18 healthy participants with 53 adults with Attention-Deficit/Hyperactivity Disorder, for whom difficulties with sustained attention are a core symptom and thus constitute a natural experiment condition. Our behavioural and brain-imaging analyses demonstrate distinct neural patterns in bottom-up visual processing and attention allocation mechanisms in ADHD and Control groups, highlighting different cognitive strategies utilised by adults with ADHD and healthy participants in tasks requiring sustained attention.

In personalized-schedule learning, previous research has shown the benefit of initial attempted retrieval of study-items on short-term retention and later test performance. As a way of prior-knowledge identification, initial attempted retrieval may help to optimize learning and long-term performance further, through the removal (or ‘drop') of items from the learning set that are answered correctly on the first attempt. This study sought to support this hypothesis through a real-world, within-subjects experiment, comparing vocabulary test performance of Dutch middle school students after the use of a drop- and non-drop adaptive learning algorithm. The results show that short- and long-term item retention was higher for material studied using the drop-algorithm, while dropping items did not lead to worse retention compared to items that were kept upon initial correct responses. This suggests that initially-known items are correctly identified as ‘mastered', and that their removal from the learning material allows students to focus their efforts on unknown items, leading to increased learning gains.

Humans are born with an intuitive representation of the physics world. How accurate is intuitive physics? Researchers from education focus on the failures, students' errors and misconceptions while cognitive psychologists argue humans anticipate and manipulate physical environments in ways betraying veridical knowledge of classical mechanics. One solution is to hypothesize there are distinct systems of “cognitive physics” with different limitations and deployment in the tasks favored by the two literatures. The goal of current study is to gather evidence from psychometric studies by estimating how many distinct factors explain performance on intuitive physics assessments. We build an augmented concept inventory including several previously-validated concept inventories, around 120 items. The pilot study indicated that participants recruited online from Prolific displayed expected accuracy on the tasks. We are now collecting around 1,000 participants and applying multidimensional item response theory (MIRT) analyzes to identify the latent structure.

Previous research has effectively employed the fast-paced action puzzle video-game Tetris for understanding the acquisition of extreme expertise in complex, dynamic environments. A common approach when contrasting expert to novice performance has been the dissection of their interactions with the environment into disjoint sub-tasks – such as Reaction Time (RT), measured by the input latency to new events on screen. The crucial, underlying assumption to this paradigm is task consistency at all levels of expertise. Using data collected from participants of the Tetris World Championship 2019 and from novices in our lab, we show that this assumption does not hold. While for novices the RT task type remains the same across all conditions, for experts - depending on environmental parameters - the nature of the RT task undergoes a shift and under specific conditions does not represent a RT task anymore. Thus, expert vs. novice sub-task comparison may not be a valid paradigm.

It has been theorized that people, as decision-makers with limited cognitive resources, may represent value and probability in an adaptive way, thus exhibiting context-dependent decision biases. However, most theoretical models and empirical tests only focused on contextual effects within single dimensions, such as the influence of the value context on the utility function or that of the probability context on the probability distortion function. Here we investigated potential cross-dimensional as well as within-dimensional contextual effects in a series of experiments of decision under risk. Each participant was tested on gambles with factorially-designed values and probabilities so that we could simultaneously estimate their utility and probability distortion functions. Across participants we manipulated the mean and variance of the distribution of the gamble values. We found not only within-dimensional effects of the value context on the utility function, but also cross-dimensional effects of the value context on the probability distortion function.

Through training using distorted vision, the perception of the trained limb position is shifted based on visual information. We investigated whether and how such proprioceptive recalibration transfers to untrained limbs. The results of experiments using human-like virtual limbs confirmed that transfer to some limbs occurred. The manner in which the transfer occurred varied according to the participants' body posture and the type of trained limb. When the participants sat, the recalibration transferred from one arm to another arm symmetrically around the body midline. Conversely, in the case of a sitting leg and standing arm, it was directly copied to another leg or arm.

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.

Efficient representation of our motor memories is essential for developing and deploying the plethora of human motor skills we use every day, yet there is little cohesion in understanding how these representations are organized and develop over time. In this study, we developed a novel behavioral paradigm aimed at probing experience-dependent representational change of a motor skill with training. Across several experiments, participants trained to coordinate novel finger configurations in a speeded reaction time task and performed a similarity judgment task to probe for changes in the psychological representation of the recently acquired skill. We sought to determine if learned representations of action can be found through behavioral methods and if such representations are organized based on similarity and independent reorganization patterns predicted by theories of sensorimotor neuroscience. Together, we propose a novel behavioral measure complementary to neuroimaging capable of observing human representational change of a motor skill over time.

Explicit strategies, drawing on working memory and executive function, play an important role in motor learning and adaptation. Here, using a visuomotor rotation task in which participants explicitly reported their aim angle, we examined the influence of task complexity on explicit learning, with an emphasis on capacity limitations that influence the number of unique solutions that observers are able to keep in memory. We found that increasing target set size (from 1 to 4 targets) resulted in slower learning and slower RTs, likely due to a combination of algorithmic simulation and memory retrieval strategies. However, when participants were required to learn four unique target-rotation pairs simultaneously, we observed constant RTs and a similar rate of learning across rotation magnitude, in line with participants explicitly memorizing and retrieving unique solutions for each target. These findings suggest that participants may adopt different explicit strategies depending on the complexity of the sensorimotor task.

Metacognition can improve with practice, yet the mechanisms underlying metacognitive skill learning remain unclear and lack a robust theoretical framework. We propose that metacognitive skill learning can be largely explained by the skill acquisition model advanced by Fitts (1964) and Anderson (1982). While this model has been successful within the domains of motor skill and cognitive skill, it has not yet been applied to metacognitive skill. This novel framework can help to explain metacognitive skill learning, its cognitive underpinnings, and shed light on otherwise unexplainable empirical data.

Despite the long-standing recognition that individuals vary in their cognitive performance across relatively short time periods, little research has integrated an understanding of short-term within-individual variation in cognitive performance into our theories of cognitive ability. We contend that systematic patterns of between-individual differences in within-individual variation are meaningful and should not be viewed merely as measurement error. We argue that predominant cognitive testing methods using between-individual analysis of single-occasion scores are limited in their capacity to develop a process account of why individuals with the same test score differ in practical contexts. We propose that short-term repeated measures paradigms (e.g., the Experience Sampling Method) be used to understand the nature and sources of between-individual differences in within-individual variation. Finally, we outline considerations for researchers when adapting this paradigm for cognitive assessment and present initial findings from our lab on the feasibility of this paradigm.

We investigated whether metacognitive errors (e.g., over- and under-confidence) were subject to a hypercorrection effect – an asymmetric adjustment for high- vs. low-confidence errors – analogous to that which has been demonstrated with performance errors. In a cue-outcome prediction task, on each trial, participants indicated which word would follow a particular image, and rated how confident they were in their answer. Consistent with the conventional hypercorrection effect in performance accuracy, there was an asymmetry in the adjustment of confidence ratings that did not match performance. However, contrary to previous reports of performance hypercorrection, metacognitive adjustment was greater for under- than over-confident responses.

When we exercise sequences of actions, their execution becomes more fluent and precise. Here, we consider the possibility that exercised action sequences can also be used to make planning faster and more accurate by focusing expansion of the search tree on paths that have been frequently used in the past, and by reducing deep planning problems to shallow ones via multi-step jumps in the tree. To capture such sequences, we use a flexible Bayesian action chunking mechanism which finds and exploits statistically reliable structure at different scales. This gives rise to shorter or longer routines that can be embedded into a Monte-Carlo tree search planner. We show the benefits of this scheme using a physical construction task patterned after tangrams.

In his famous address “You can’t play 20 questions with nature and win”, Newell (1973) predicted that experimental psychology’s focus on falsifying theories of individual phenomena (e.g., subitizing, directed forgetting, etc.) would not lead to a unifying theory subsuming several (let alone all) of the phenomena. Instead of seeking to detect effects, and binary searching our way to the explananda of these phenomena, Newell argued psychologists should build predictive theories of behavior by modeling 1) the structure of the task environment (i.e., context), 2) the subjects’ own goals, and 3) the invariant structure of the subjects’ processing mechanisms. Newell concludes that psychology will only make real progress in understanding the mind by 1) targeting tasks that are complex enough to cover the space of naturalistic behavior, 2) developing models that can competently perform the task, and 3) generalizing such models to perform multiple complex tasks. For 1), Newell chose to study chess; we believe he would find video games an even more compelling paradigm (see Gobet, 2017).

The definition of cognitive flexibility remains debated in the literature. Some refer to flexibility as a general shifting ability. Others argue that it is a context-dependent skill. For others, flexibility is a dimensional construct including reactive flexibility and spontaneous flexibility. To help decide between these views, we analyzed the performances of first-graders across four flexibility tasks. Two tasks focused on reactive flexibility (rule-shifting and verbally-cued-shifting), and two targeted spontaneous flexibility (role-shifting and divergent thinking). Three additional executive tasks investigated the contribution of inhibition, working memory, and processing speed. Significant — though moderate — positive correlations emerged between all but one of the pairs of flexibility tasks. Besides, executive functions were not found to systematically correlate with any measure of flexibility. Furthermore, confirmatory factorial analyses failed to identify a unique or bi-dimensional structure of flexibility. Taken together, these results did not provide evidence for a domain-general ability. Implications and future directions are discussed.

How do children – who are undeniably productive learners – think about their learning? Do children understand, as adults do, that learning is a process of continuous improvement over time? To explore children’s emerging representations of the learning process, we created a non-verbal motor learning paradigm where 4- to 8-year-olds predicted their own learning curve without prior experience. We found that by age 7, children predicted improved performance over time. Younger children, however, were overly optimistic about how well they would do at the game and often predicted near-perfect performance across trials. This work suggests that children’s predictions of their future learning curve become more accurate with age, which may have implications for young children’s learning decisions.

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion. SDT-style concepts were used to alter sequencing, and separately to define mastery (category retirement). SDT retirement used a running d’ estimate calculated from a recent window of trials based on hit and false alarm rates. Undergraduate participants used a Skin Cancer PALM (perceptual adaptive learning module) to learn classification of 10 cancerous and readily-confused non-cancerous skin lesion types. Four adaptive conditions varied either the type of adaptive sequencing (standard vs. SDT) or retirement criteria (standard vs. SDT). A non-adaptive control condition presented didactic instruction on dermatologic screening in video form, including images, classification schemes, and detailed explanations. All adaptive conditions robustly outperformed the non-adaptive control in both learning efficiency and fluency (large effect sizes). Between adaptive conditions, SDT retirement criteria produced greater learning efficiency than standard, accuracy-based mastery criteria at both immediate and delayed posttests (medium effect sizes). SDT sequencing and standard adaptive sequencing did not differ. SDT enhancements to adaptive perceptual learning procedures have potential to enhance learning efficiency.

Advanced digitization and related information overload foster the prevalence of disruptive stimuli that constantly challenge people in learning and working contexts. The high variety of potential distractions increasingly reduces attention and subsequently minimizes capacities for productive study and work habits. Addressing this challenge, intelligent training software can leverage metacognitive feedback to support people with resisting situational temptations and keeping focused on meaningful tasks. While pilot studies indicate benefits to strengthen attention control skills, they also show hesitation towards using such software at all. Hence, the present study investigates users’ acceptance towards an AI-based software to promote attention control in more detail. A sample of 71 volunteers completed a survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) to identify factors determining people’s willingness to use the software. Results indicate that performance expectancy is an important predictor to consider when introducing the software to potential target groups.

Resources and examples of building equity into the design of rigorous education studies of cognitive interventions is lacking. Here we describe an early-stage attempt to build equity into a large-scale randomized trial of a cognitive intervention - i.e., interleaved practice - for middle-school mathematics learning. Our approach is informed by WestEd's Anti-Racist Evaluation Strategies (WestEd, 2021) which is informed by literature on equitable research and evaluation. It incorporates three overarching themes: self reflection and learning, equitable partnerships, and understanding context. Our approach incorporates the themes into two major strands of equitable research practices: (1) engaging the study team in learning and self-reflection around issues of diversity, equity, and inclusion and (2) Engaging relevant parties in the research processes. Though in the early stages of the project, we will report both successes and challenges of these efforts and aim to foster discussion about how to best incorporate equity-based approaches in cognitive science research.

Normative accounts of decision-making predict that people attempt to balance the immediate rewards associated with correct responses against the costs of deliberation. However, humans frequently deliberate longer than normative models say they should. We propose that people try to optimize not only their rate of material rewards, but also their rate of information gain. A computational model that implements this idea successfully mimics human decision makers, reproducing key patterns of behavior not predicted by alternative models. Moreover, simulations reveal a normative basis for our model: An agent that exchanges even a small amount of immediate reward for information will improve its decision-making ability through learning, allowing it to earn more reward in the long run than an agent disinterested in information. Maximizing a combination of reward and information rate is a simple yet effective strategy for solving the speed-accuracy tradeoff that may resolve lingering mysteries about human decision-making.

Perceptual-motor tasks offer redundant solutions to achieve a goal. However, not all solutions are equally robust to error-producing variability and thus, skill learning requires the search for behaviors that are error-tolerant. Throwing a ball to hit a target is a skill that has been studied via the “skittles” task, a simplified 2-dimensional throwing environment. The study investigated whether participants learning the skittles task benefit from task scaffolding via the placements of targets provided by either a human confederate posing as a teacher, or by the participant. These groups were compared to a third sample who received an unstructured curriculum consisting of randomly placed targets. The results demonstrate that participants in the unstructured training condition were better able to perceive the task’s underlying solution geometry, compared to scaffolding provided by a human. Future work will explore methods to design training curricula that are more efficient in facilitating perceptual-motor skill development.

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

Response inhibition—our ability to suppress actions—is achieved through coordinated reactive and proactive processes that may recruit different neurophysiological mechanisms. Here we adapted a two-step continuous performance task in which the decision to respond depends on a combination of an initial context cue and a subsequent target probe. Using transcranial magnetic stimulation (TMS), we mapped changes in corticospinal excitability and inhibition, providing indices of reactive and proactive processes that influence the state of the motor system in the lead-up to initiating or suppressing a response. We found distinct changes in corticospinal excitability at critical timepoints when participants were preparing in advance to inhibit a response (during the cue) and while inhibiting a response (during the probe). Motor system activity during early timepoints correlated with behavioural indices of proactive capacity and predicted whether participants would later successfully inhibit their response. Implications for theories of inhibitory control will be discussed.

Commutativity–the fact that changing the order of operands in an operation does not change the result–is a fundamental principle of mathematics. How does its understanding develop in children? While this question has mostly been addressed for addition, here we focus on multiplication. We ask whether children’s formal learning of multiplication commutativity relies on pre-existing non-symbolic intuitions, or whether it is first learnt symbolically and only later elaborated as a general principle that extends to concrete situations. 2nd and 3rd graders received an intervention on commutative multiplication, before and after which they played a number comparison game testing their understanding of commutativity in symbolic and non-symbolic contexts. Our results suggest that the commutative principle of multiplication may not be available to intuition before formal teaching: perceiving that 2 groups of 3 dots and 3 groups of 2 dots contain the same number of dots requires mastering commutativity symbolically first.

Video games represent a complex, engaging task domain that has shown great promise as an experimental paradigm for the study of cognition and skill acquisition. The methods that have thus far been used to study video games, however, have been sub-optimal, and will likely become more outdated as the video gaming landscape continues to change. In this work, I first give a brief overview of the previous literature surrounding video games and cognition. I next address the critical methodological problems such as measuring expertise based solely through time-on-task as a heuristic of expertise rather than true metrics of skilled performance itself. I then suggest how the burgeoning sub-domain of competitive video gaming, or “eSports,” provides an elegant solution to these problems. Finally, I discuss the current and future directions of eSports cognition research.

Deep reinforcement learning (DRL) is capable of training agents that exceed human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans or augmenting human actions in cooperative or team-based tasks. Indeed, recommender systems designed to augment human decision-making need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. The current study simultaneously explored skill learning performance of human learners when working alongside different artificial agents (AAs) during a collaborative problem-solving task, as well as evaluated the effectiveness of the same AAs as action decision recommender systems to aid learning. The action decisions of the AAs were either modelled by a heuristic model based on human performance or by a deep neural network trained by reinforcement learning using self-play. In addition to evaluating skill learning performance, the current study also tested the congruence between the decisions of the AAs with actual decisions made by humans. Results demonstrate that the performance of humans was significantly worse when working alongside the DRL AA compared to the heuristic AA. Additionally, the action decisions participants made also showed less allignment with the recommendations made by the DRL AA.

Prediction, explanation, and control are basic cognitive abilities. Here we show how they can arise, simultaneously, from underlying mental models built during unstructured, exploration-based learning. Our experimental paradigm, involving interaction with a symbolic "chatbot", allows us to vary the relative difficulty of the tasks, and to measure how participants leverage the Bayesian evidence of their mental models for decision-making. Our experimental manipulation focuses on hidden information and task complexity. With full information, there are significant differences between the three tasks: for example, people are more sensitive to Bayesian evidence in prediction than in control or explanation. When information is hidden, however, performance equalizes. Taken together, our results suggest that, while specific heuristics may lead to different levels of performance in cases with full information, more fundamental forms of reasoning, based on an underlying mental model, and less sensitive to the specific task, come into play when pieces are missing.

Research suggests that perspective-taking in children is associated with greater cognitive and emotion understanding as well as parent use of mental state language—words concerning inner mental states or feelings. However, prior research shows conflicting results on which categories of mental state language—cognition (e.g., think, know), emotion (e.g., happy), or desire (e.g., want)—facilitate children’s perspective-taking. The current study examines the relationship between parent mental state language and children’s perspective-taking.We are coding parent mental state language categories from a wordless book task with two-year-old children and assessing children’s perspective-taking using a card task (Flavell et al., 1981). Preliminary results (N=19, planned 41) show that, on average, parents use more cognition words per session than desire words and, on average, children score at chance on the perspective-taking task. Differences between mental state categories on children’s perspective-taking skills as well as individual variability will be analyzed.

We investigated the effect of implicit theories (belief about malleability of ability) on help-seeking behavior in peer learning situation. We predicted that entity theorists (those who believe that ability is fixed) are less likely to seek help because they anticipate that peer members would lower their evaluation of competence. We conducted a scenario experiment and required participants to indicate to what extent they would ask questions to the peer member when they encounter an incomprehensive term which was already explained, and others seemed to understand. The results did not support our hypotheses. The results revealed that the interaction between one’s own and perceived others implicit theories predicted intention to ask questions. Specifically, while perceived others’ implicit theory did not affect incremental theorists’ (those who believe that ability is malleable) intention to ask a question, entity theorists were more likely to ask a question when they perceive others as entity theorists.

Computer programs correspond to instructions that tell a computer how to perform a task. Because computers do not understand human languages, programs have to be written in a specialized programming language. Such languages have unique syntax and semantics that on the surface appear quite different from human languages. To foster understanding what a program means, novices need to translate the programming language instructions into a familiar human language. We developed a tutoring system that provides translation activities, from the language Python into plain English, and feedback on student answers. One version of the tutoring system provides open-ended input boxes that students type translations into. A second version instead asks students to generate answers to the translation using drop-down menus. The latter provides more scaffolding but also may encourage shallow behaviors such as guessing and checking. To shed light on how these designs influence learning, we evaluate them with novice programmers. Here, we report on the results of the evaluation and next steps.