Self-Directed Learning: A Cognitive and Computational Perspective

Estimating a Theoretically Consistent Human Capital Production Function With an Application to Head Start

This article describes a conceptual and empirical approach for estimating a human capital production function of child development that incorporates mother- or child-fixed effects. The use of mother- or child-fixed effects is common in this applied economics literature, but its application is often inconsistent with human capital theory. We outline the problem and demonstrate its empirical importance with an analysis of the effect of Head Start and preschool on child and adult outcomes. The empirical specification we develop has broad implications for a variety of applied microeconomic analyses beyond our specific application. Results of our analysis indicate that attending Head Start or preschool had no economically or statistically significant effect on child or adult outcomes.

Effects of sample size information and within- and between-category similarity on study choices in self-regulated learning

Category learning is usually better supported by interleaved training (alternating between exemplars from different categories) than by blocked training (studying all exemplars within a category sequentially), yet when asked to choose between the two strategies most people endorse blocking as superior. We used a prototype category-learning task to examine the effects of between- and within-category similarity and knowledge of the number of stimuli to be studied on study sequencing choices during self-regulated learning. Across three experiments (including a complete replication), participants who viewed the number of stimuli in each category showed more interleaving in comparison with those who did not, indicating that participants adjusted their strategy based on the projected length of the study phase. Participants informed about the number of stimuli also showed greater interleaving when within-category similarity was high and greater blocking when within-category similarity was low; this difference was not found when participants were not told the number of stimuli to be studied. Between-category similarity did not affect degree of blocking versus interleaving. Overall, interleaving decreased over training and blocking increased. Most participants endorsed hybrid strategies in which blocking was combined with at least some interleaving on a metacognitive questionnaire, but when forced to choose between exclusive blocking and exclusive interleaving, the majority endorsed blocking. These results indicate that participants are sensitive to category structure and expectations about task length when choosing stimuli to study during self-regulated learning, and adjust their strategy across the time course of study.

Active control over exploration improves memory in toddlers

Across two experiments, we implemented a novel gaze-contingent eye-tracking paradigm to investigate the early emergence of memory benefits from active control over exploration and to examine how exploratory behaviours affect memory formation in early development. Toddlers (experiment 1: n = 36, 18-36 months; experiment 2: n = 41, 23-36 months) were either allowed to actively control their exploration (active condition) or presented with the same information that they could only passively observe (passive condition in experiment 1; yoked condition in experiment 2). They were then tested in a preferential-looking paradigm in which familiar versus novel stimuli were presented in pairs. Evidence from eye-movement patterns indicates that toddlers demonstrate improved recognition memory when given active control over learning. Toddlers' pace of learning (i.e. visitation rate) explains the recognition improvement in their active exploration. Their memory improvement is also related to individual differences in the systematicity of exploratory behaviour (i.e. sequence entropy). These findings suggest that toddlers exhibit more sophisticated exploratory strategies than previously believed, revealing the early emergence and development of their ability to adapt these strategies to enhance memory and therefore support learning.

Systematic Curiosity as an Integrative Tool for Human Flourishing: A Conceptual Review and Framework

This paper reviews seventy years of theoretical research and proposes systematic curiosity as an integrative tool for human flourishing with a focus on four key aspects: firstly, acknowledge curiosity's multidimensional nature instead of harmonizing its complex taxonomy; secondly, emphasizing intentional curiosity as opposed to impulsive curiosity; thirdly, prioritizing domain-general curiosity for broader applicability across educational, organizational, and therapeutic settings; and lastly, focusing on curiosity as a developable skill rather than an innate trait. By segmenting systematic curiosity into cognitive, emotional, and behavioral components, and relating these to interactions with the self, others, and the world, the framework aims to apply across the spectrum of human experience. Furthermore, the framework encourages an exploration of various evidence-based activities for flourishing so individuals can discover the most suitable strategies for their specific context. Implications for both theory and practice are examined, limitations are discussed, and avenues for future research are suggested.

Grounded language acquisition through the eyes and ears of a single child

Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child's everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child's input.

Local Search and the Evolution of World Models

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

Neural signatures of memory gain through active exploration in an oculomotor-auditory learning task

Active engagement improves learning and memory, and self- versus externally generated stimuli are processed differently: perceptual intensity and neural responses are attenuated. Whether the attenuation is linked to memory formation remains unclear. This study investigates whether active oculomotor control over auditory stimuli-controlling for movement and stimulus predictability-benefits associative learning, and studies the underlying neural mechanisms. Using EEG and eye tracking we explored the impact of control during learning on the processing and memory recall of arbitrary oculomotor-auditory associations. Participants (N = 23) learned associations through active exploration or passive observation, using a gaze-controlled interface to generate sounds. Our results show faster learning progress in the active condition. ERPs time-locked to the onset of sound stimuli showed that learning progress was linked to an attenuation of the P3a component. The detection of matching movement-sound pairs triggered a target-matching P3b. There was no general modulation of ERPs through active learning. However, we found continuous variation in the strength of the memory benefit across participants: some benefited more strongly from active control during learning than others. This was paralleled in the strength of the N1 attenuation effect for self-generated stimuli, which was correlated with memory gain in active learning. Our results show that control helps learning and memory and modulates sensory responses. Individual differences during sensory processing predict the strength of the memory benefit. Taken together, these results help to disentangle the effects of agency, unspecific motor-based neuromodulation, and predictability on ERP components and establish a link between self-generation effects and active learning memory gain.

The interactive effect of external rewards and self-determined choice on memory

Both external motivational incentives (e.g., monetary reward) and internal motivational incentives (e.g., self-determined choice) have been found to promote memory, but much less is known about how these two types of incentives interact with each other to affect memory. The current study (N = 108) examined how performance-dependent monetary rewards affected the role of self-determined choice in memory performance, also known as the choice effect. Using a modified and better controlled version of the choice paradigm and manipulating levels of reward, we demonstrated an interactive effect between monetary reward and self-determined choice on 1-day delayed memory performance. Specifically, the choice effect on memory decreased when we introduced the performance-dependent external rewards. These results are discussed in terms of understanding how external and internal motivators interact to impact learning and memory.

Korean infants' perceptual responses to Korean and Western music based on musical experience

This study investigates infants' enculturation to music in a bicultural musical environment. We tested 49 12- to 30-month-old Korean infants on their preference for Korean or Western traditional songs played by haegeum and cello. Korean infants have access to both Korean and Western music in their environment as captured on a survey of infants' daily exposure to music at home. Our results show that infants with less daily exposure to any kind of music at home listened longer to all music types. The infants' overall listening time did not differ between Korean and Western music and instruments. Rather, those with high exposure to Western music listened longer to Korean music played with haegeum. Moreover, older toddlers (aged 24-30 months) maintained a longer interest in songs of an origin with which they are less familiar, indicating an emerging orientation towards novelty. Early orientation of Korean infants toward the novel experience of music listening is likely driven by perceptual curiosity, which drives exploratory behavior that diminishes with continued exposure. On the other hand, older infants' orientation towards novel stimuli is led by epistemic curiosity, which motivates an infant to acquire new knowledge. Korean infants' lack of differential listening likely reflects their protracted period of enculturation to ambient music due to complex input. Further, older infants' novelty-orientation is consistent with findings in bilingual infants' orientation towards novel information. Additional analysis showed a long-term effect of music exposure on infants' vocabulary development. A video abstract of this article can be viewed at https://www.youtube.com/watch?v=Kllt0KA1tJk RESEARCH HIGHLIGHTS: Korean infants showed novelty-oriented attention to music such that infants with less daily exposure to music at home showed longer listening times to music. 12- to 30-month-old Korean infants did not show differential listening to Korean versus Western music or instruments, suggesting a protracted period of perceptual openness. 24- to 30-month-old Korean toddlers' listening behavior indicated emerging novelty-preference, exhibiting delayed enculturation to ambient music compared to Western infants reported in earlier research. 18-month-old Korean infants with a greater weekly exposure to music had higher CDI scores a year later, consistent with the well-known music-to-language transfer effect.

Active inductive inference in children and adults: A constructivist perspective

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.

Toddlers' action learning and memory from active and observed instructions

From early in life, children learn to perform actions on the objects in their environments. Although children learn from observing others' actions, actively engaging with the material to be learned can be important for learning. This study tested whether instruction that included opportunities for children to be active supported toddlers' action learning. In a within-participants design, 46 22- to 26-month-old toddlers (average age = 23.3 months; 21 male) were introduced to target actions for which instruction was either active or observed (instruction order counterbalanced across children). During active instruction, toddlers were coached to perform a set of target actions. During observed instruction, toddlers saw a teacher perform the actions. Toddlers were then tested on their action learning and generalization. Surprisingly, action learning and generalization did not differ between instruction conditions. However, toddlers' cognitive maturity supported their learning from both types of instruction. One year later, children from the original sample were tested on their long-term memory for information learned from active and observed instructions. Of this sample, 26 children provided usable data for the follow-up memory task (average age = 36.7 months, range = 33-41; 12 male). Children demonstrated better memory for information learned from active instruction than for information learned from observed instruction (odds ratio = 5.23) 1 year after instruction. Active experience during instruction appears to be pivotal for supporting children's long-term memory.

Learning strategy impacts medical diagnostic reasoning in early learners

Relating learned information to similar yet new scenarios, transfer of learning, is a key characteristic of expert reasoning in many fields including medicine. Psychological research indicates that transfer of learning is enhanced via active retrieval strategies. For diagnostic reasoning, this finding suggests that actively retrieving diagnostic information about patient cases could improve the ability to engage in transfer of learning to later diagnostic decisions. To test this hypothesis, we conducted an experiment in which two groups of undergraduate student participants learned symptom lists of simplified psychiatric diagnoses (e.g., Schizophrenia; Mania). Next, one group received written patient cases and actively retrieved the cases from memory and the other group read these written cases twice, engaging in a passive rehearsal learning strategy. Both groups then diagnosed test cases that had two equally valid diagnoses-one supported by "familiar" symptoms described in learned patient cases, and one by novel symptom descriptions. While all participants were more likely to assign higher diagnostic probability to those supported by the familiar symptoms, this effect was significantly larger for participants that engaged in active retrieval compared to passive rehearsal. There were also significant differences in performance across the given diagnoses, potentially due to differences in established knowledge of the disorders. To test this prediction, Experiment 2 compared performance on the described experiment between a participant group that received the standard diagnostic labels to a group that received fictional diagnostic labels, nonsense words designed to remove prior knowledge with each diagnosis. As predicted, there was no effect of diagnosis on task performance for the fictional label group. These results provide new insight on the impact of learning strategy and prior knowledge in fostering transfer of learning, potentially contributing to expert development in medicine.

Emerging Principles of Attention and Information Demand

I review recent literature on information demand and its implications for attention control. I argue that this literature motivates a view of attention as a mechanism that reduces uncertainty by selectively sampling sensory stimuli on the basis of expected information gain (EIG). I discuss emerging evidence on how individuals estimate the two quantities that determine EIG, prior uncertainty and stimulus diagnosticity (predictive accuracy). I also discuss the neural mechanisms that compute EIG and integrate it with rewards in frontoparietal, executive, and neuromodulatory circuits. I end by considering the implications of this framework for a broader understanding of the factors that assign relevance to sensory stimuli and the role of attention in decision making and other cognitive functions.

Self-motivated and directed learning across the lifespan

Self-motivated and directed learning is integral to knowledge base expansion for learners of all ages. Both motivational and cognitive processes drive self-motivated and directed lifelong learning, yet how these different processes operate together from childhood through adulthood is largely unknown. In this review, we discuss the role of personal motivators, such as beliefs in self-efficacy and personality traits in self-motivated and directed learning across the lifespan. We then consider the role of cognitive processes that contribute to knowledge base expansion in learners of all ages, specifically executive functions. We focus on working memory, inhibitory control, and task switching as potential determinants of lifelong learning. Finally, we integrate the two literatures, to discuss ways in which personal motivators may influence deployment of executive functions under self-motivated and directed conditions as a learner advances along a developmental trajectory. We also suggest ways to move the study of self-motivated and directed learning beyond observation and self-report measures thus affording experimental control. We aim to provide a more comprehensive understanding and novel insight to the mechanisms and processes of self-motivated and directed learning across the lifespan.

Humans combine value learning and hypothesis testing strategically in multi-dimensional probabilistic reward learning

Realistic and complex decision tasks often allow for many possible solutions. How do we find the correct one? Introspection suggests a process of trying out solutions one after the other until success. However, such methodical serial testing may be too slow, especially in environments with noisy feedback. Alternatively, the underlying learning process may involve implicit reinforcement learning that learns about many possibilities in parallel. Here we designed a multi-dimensional probabilistic active-learning task tailored to study how people learn to solve such complex problems. Participants configured three-dimensional stimuli by selecting features for each dimension and received probabilistic reward feedback. We manipulated task complexity by changing how many feature dimensions were relevant to maximizing reward, as well as whether this information was provided to the participants. To investigate how participants learn the task, we examined models of serial hypothesis testing, feature-based reinforcement learning, and combinations of the two strategies. Model comparison revealed evidence for hypothesis testing that relies on reinforcement-learning when selecting what hypothesis to test. The extent to which participants engaged in hypothesis testing depended on the instructed task complexity: people tended to serially test hypotheses when instructed that there were fewer relevant dimensions, and relied more on gradual and parallel learning of feature values when the task was more complex. This demonstrates a strategic use of task information to balance the costs and benefits of the two methods of learning.

Comparing models of learning and relearning in large-scale cognitive training data sets

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals' performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.

Controllability boosts neural and cognitive signatures of changes-of-mind in uncertain environments

In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography, and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of one's own actions.

Capturing, clarifying, and consolidating the curiosity-creativity connection

The cognitive-motivational concepts of curiosity and creativity are often viewed as intertwined. Yet, despite the intuitively strong linkage between these two concepts, the existing cognitive-behavioral evidence for a curiosity-creativity connection is not strong, and is nearly entirely based on self-report measures. Using a new lab-based Curiosity Q&A task we evaluate to what extent behaviorally manifested curiosity-as revealed in autonomous inquiry and exploration-is associated with creative performance. In a preregistered study (N = 179) we show that, as hypothesized, the novelty of the questions that participants generated during the Curiosity Q&A Task significantly positively correlated with the originality of their responses on a divergent-thinking task (the conceptually-based Alternative Uses Task). Additionally, the extent to which participants sought out information that was implicitly missing in the presented factual stimuli ("gap-related information foraging") positively correlated with performance on two predominantly convergent-thinking tasks (the Remote Associates Task and Analogy Completion). Question asking, topic-related information foraging, and creative performance correlated with trait-based "interest-type" curiosity oriented toward exploration and novelty, but not with "deprivation-type" curiosity focused on dispelling uncertainty or ignorance. Theoretically and practically, these results underscore the importance of continuing to develop interventions that foster both creative thinking and active autonomous inquiry.

Algorithms of adaptation in inductive inference

We investigate the idea that human concept inference utilizes local adaptive search within a compositional mental theory space. To explore this, we study human judgments in a challenging task that involves actively gathering evidence about a symbolic rule governing the behavior of a simulated environment. Participants learn by performing mini-experiments before making generalizations and explicit guesses about a hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants' initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guess. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants' hypothesis revisions better than a range of alternative explanations. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.

Rational use of cognitive resources in human planning

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near optimal under some circumstances but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

An energizing role for motivation in information-seeking during the early phase of the COVID-19 pandemic

The COVID-19 pandemic has highlighted the importance of understanding and managing information seeking behavior. Information-seeking in humans is often viewed as irrational rather than utility maximizing. Here, we hypothesized that this apparent disconnect between utility and information-seeking is due to a latent third variable, motivation. We quantified information-seeking, learning, and COVID-19-related concern (which we used as a proxy for motivation regarding COVID-19 and the changes in circumstance it caused) in a US-based sample (n = 5376) during spring 2020. We found that self-reported levels of COVID-19 concern were associated with directed seeking of COVID-19-related content and better memory for such information. Interestingly, this specific motivational state was also associated with a general enhancement of information-seeking for content unrelated to COVID-19. These effects were associated with commensurate changes to utility expectations and were dissociable from the influence of non-specific anxiety. Thus, motivation both directs and energizes epistemic behavior, linking together utility and curiosity.

Information-seeking behavior in humans is often viewed as irrational rather than utility maximizing. Here the authors describe data obtained in Spring 2020 showing that participants’ concern about COVID-19 was related not only to their drive to seek information about the virus, but also to their curiosity about other more general topics.

Explanation impacts hypothesis generation, but not evaluation, during learning

A large body of research has shown that engaging in self-explanation improves learning across a range of tasks. It has been proposed that the act of explaining draws attention and cognitive resources towards evidence that supports good explanations-information that is broad, abstract, and consistent with prior knowledge-which in turn aids discovery and promotes generalization. However, it remains unclear whether explanation impacts the learning process via improved hypothesis generation, increasing the probability that the most generalizable hypotheses are considered in the first place, or hypothesis evaluation, the appraisal of such hypotheses in light of observed evidence. In two experiments with adults, we address this question by separating hypothesis generation and evaluation in a novel category learning task and quantifying the effect of explaining on each process independently. We find that explanation supports learners' generation of broad and abstract hypotheses but does not impact their evaluation of them. These results provide a more precise account of the process by which explanation impacts learning and offer additional support for the claim that hypothesis generation and evaluation play distinct roles in problem solving.

Control over sampling boosts numerical evidence processing in human decisions from experience

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over information acquisition affect the quality of sample-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

Immersive Anatomy Atlas: Learning Factual Medical Knowledge in a Virtual Reality Environment

In order to improve learning efficiency and memory retention in medical teaching, furthering active learning seems to be an effective alternative to classical teaching. One option to make active exploration of the subject matter possible is the use of virtual reality (VR) technology. The authors developed an immersive anatomy atlas which allows users to explore human anatomical structures interactively through virtual dissection. Thirty-two senior-class students from two German high schools with no prior formal medical training were separated into two groups and tasked with answering an anatomical questionnaire. One group used traditional anatomical textbooks and the other used the immersive virtual reality atlas. The time needed to answer the questions was measured. Several weeks later, the participants answered a similar questionnaire with different anatomical questions in order to test memory retention. The VR group took significantly less time to answer the questionnaire, and participants from the VR group had significantly better results over both tests. Based on the results of this study, VR learning seems to be more efficient and to have better long-term effects for the study of anatomy. The reason for that could lie in the VR environment's high immersion, and the possibility to freely and interactively explore a realistic representation of human anatomy. Immersive VR technology offers many possibilities for medical teaching and training, especially as a support for cadaver dissection courses.

Intrinsic Structural Connectivity of the Default Mode Network and Behavioral Correlates of Executive Function and Social Skills in Youth with Autism Spectrum Disorders

Brain connectivity of individuals with autism spectrum disorders (ASD) is heterogenous, as are the behavioral manifestations. The current study investigated brain-behavior relationships in the context of social skills and executive function profiles with data from the Autism Brain Imaging Database Exchange II. We calculated connectivity measures from diffusion tensor imaging using Bayesian estimation and probabilistic tractography. Subsequently, we performed structural equation modeling by regressing three latent factors, yielded from an exploratory factor analysis, onto total default mode network (DMN) connectivity. Both social regulation processing and self-directed cognitive processing factors moderately, negatively correlated with total DMN connectivity. Our findings indicate social regulation processing difficulties in youth with ASD may be attributable to impaired connectivity between the anterior and posterior DMN.

Academic motivation and self-directed learning readiness of nursing students during the COVID-19 pandemic in three countries: A cross-sectional study

BACKGROUND

It is crucial to evaluate student academic motivation and self-directed learning (SDL) readiness while teaching online or flexibly. During the coronavirus disease 2019 epidemic, there were few investigations on the link between academic motivation and SDL readiness.

AIM

This study investigated the connection between academic motivation and SDL readiness and the three academic motivation domains' predictive features.

METHODS

This cross-sectional study used convenience sampling to recruit 1187 nursing students from four nursing colleges in three countries. We utilized the Academic Motivation Scale College Version and Self-directed Learning Readiness Scale for Nurse Education to collect data. Descriptive and inferential statistics were employed to analyze the data.

RESULTS

Extrinsic motivation received the highest mean. Most nursing students exhibited SDL readiness, whereas "desire for learning" was rated the highest dimension of SDL readiness. We found significant differences in nursing students' intrinsic and extrinsic motivation and amotivation between the three countries. Finally, country, gender, and intrinsic motivation were significant predictors of the nursing students' SDL readiness.

CONCLUSION

Among Filipino, Saudi, and Thai nursing students, their SDL readiness is influenced by the intrinsic motivation domain. Therefore, nursing students with higher levels of intrinsic motivation are proactive learners for SDL.

A Platform for Cognitive Monitoring of Neurosurgical Patients During Hospitalization

Intracranial recordings in epilepsy patients are increasingly utilized to gain insight into the electrophysiological mechanisms of human cognition. There are currently several practical limitations to conducting research with these patients, including patient and researcher availability and the cognitive abilities of patients, which limit the amount of task-related data that can be collected. Prior studies have synchronized clinical audio, video, and neural recordings to understand naturalistic behaviors, but these recordings are centered on the patient to understand their seizure semiology and thus do not capture and synchronize audiovisual stimuli experienced by patients. Here, we describe a platform for cognitive monitoring of neurosurgical patients during their hospitalization that benefits both patients and researchers. We provide the full specifications for this system and describe some example use cases in perception, memory, and sleep research. We provide results obtained from a patient passively watching TV as proof-of-principle for the naturalistic study of cognition. Our system opens up new avenues to collect more data per patient using real-world behaviors, affording new possibilities to conduct longitudinal studies of the electrophysiological basis of human cognition under naturalistic conditions.

The effect of choice on intentional and incidental memory

Recent studies have revealed that memory performance is better when participants have the opportunity to make a choice regarding the experimental task (choice condition) than when they do not have such a choice (fixed condition). These studies, however, used intentional memory tasks, leaving open the question whether the choice effect also applies to incidental memory. In the current study, we first repeated the choice effect on the 24-h delayed intentional memory performance (experiment 1). Next, using an incidental paradigm in which participants were asked to judge the category of the items instead of intentionally memorizing them, we observed the choice effect on judgment during encoding and memory performance in a 24-h delayed surprise test (experiment 2). Participants judged more accurately and quickly and had better recognition memory for items in the choice condition than for items in the fixed condition. These results are discussed in terms of the role of choice in both intentional and incidental memory.

Volitional learning promotes theta phase coding in the human hippocampus

While behavioral evidence shows that volitionally controlled learning benefits human memory, little is known about the neural mechanisms underlying this effect. Insights from spatial navigation research in rodents point to the relevance of hippocampal theta oscillations. However, the mechanisms through which theta might support the beneficial effects of active learning in humans are currently unknown. Here, we demonstrate hippocampal theta oscillations increase during volitional learning, promoting a segregation of task-relevant representational signals according to their semantic content. Our results constitute a direct link to the animal literature on hippocampal theta oscillations and its relation to volition and memory processes.

Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

Chained study and the discovery of relational structure

Prior knowledge of relational structure allows people to quickly make sense of and respond to new experiences. When awareness of such structure is not necessary to support learning, however, it is unclear when and why individuals "spontaneously discover" an underlying relational schema. The present study examines the determinants of such discovery in discrimination-based transitive inference (TI), whereby people learn about a hierarchy of interrelated premises and are tested on their ability to draw inferences that bridge studied relations. Experiencing "chained" sequences of overlapping premises during training was predicted to facilitate the discovery of relational structure. Among individuals without prior knowledge of the hierarchy, chaining improved relational learning and was most likely to result in explicit awareness of the underlying relations between items. Observation of chained training sequences was also more effective than the self-generation of training sequences. These findings add to growing evidence that the temporal dynamics of training, including successive presentation of overlapping associations, are key to understanding spontaneous relational discovery during learning.

Social information use and social information waste

Social information is immensely valuable. Yet we waste it. The information we get from observing other humans and from communicating with them is a cheap and reliable informational resource. It is considered the backbone of human cultural evolution. Theories and models focused on the evolution of social learning show the great adaptive benefits of evolving cognitive tools to process it. In spite of this, human adults in the experimental literature use social information quite inefficiently: they do not take it sufficiently into account. A comprehensive review of the literature on five experimental tasks documented 45 studies showing social information waste, and four studies showing social information being over-used. These studies cover 'egocentric discounting' phenomena as studied by social psychology, but also include experimental social learning studies. Social information waste means that human adults fail to give social information its optimal weight. Both proximal explanations and accounts derived from evolutionary theory leave crucial aspects of the phenomenon unaccounted for: egocentric discounting is a pervasive effect that no single unifying explanation fully captures. Cultural evolutionary theory's insistence on the power and benefits of social influence is to be balanced against this phenomenon. This article is part of the theme issue 'Foundations of cultural evolution'.

Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science

Drawing on the philosophy of psychological explanation, we suggest that psychological science, by focusing on effects, may lose sight of its primary explananda: psychological capacities. We revisit Marr's levels-of-analysis framework, which has been remarkably productive and useful for cognitive psychological explanation. We discuss ways in which Marr's framework may be extended to other areas of psychology, such as social, developmental, and evolutionary psychology, bringing new benefits to these fields. We then show how theoretical analyses can endow a theory with minimal plausibility even before contact with empirical data: We call this the theoretical cycle. Finally, we explain how our proposal may contribute to addressing critical issues in psychological science, including how to leverage effects to understand capacities better.

Children's exploratory play tracks the discriminability of hypotheses

Effective curiosity-driven learning requires recognizing that the value of evidence for testing hypotheses depends on what other hypotheses are under consideration. Do we intuitively represent the discriminability of hypotheses? Here we show children alternative hypotheses for the contents of a box and then shake the box (or allow children to shake it themselves) so they can hear the sound of the contents. We find that children are able to compare the evidence they hear with imagined evidence they do not hear but might have heard under alternative hypotheses. Children (N = 160; mean: 5 years and 4 months) prefer easier discriminations (Experiments 1-3) and explore longer given harder ones (Experiments 4-7). Across 16 contrasts, children's exploration time quantitatively tracks the discriminability of heard evidence from an unheard alternative. The results are consistent with the idea that children have an "intuitive psychophysics": children represent their own perceptual abilities and explore longer when hypotheses are harder to distinguish.

The distributional properties of exemplars affect category learning and generalization

What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies.

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

Here, the authors show that humans perceive uncertain environments as more stable when actively interacting with them than when observing them. Magnetoencephalographic signals in the temporal lobe were associated with the increased stability of beliefs during active sampling.

A description-experience gap in statistical intuitions: Of smart babies, risk-savvy chimps, intuitive statisticians, and stupid grown-ups

Comparison of different lines of research on statistical intuitions and probabilistic reasoning reveals several puzzling contradictions. Whereas babies seem to be intuitive statisticians, surprisingly capable of statistical learning and inference, adults' statistical inferences have been found to be inconsistent with the rules of probability theory and statistics. Whereas researchers in the 1960s concluded that people's probability updating is "conservatively" proportional to normative predictions, probability updating research in the 1970s suggested that people are incapable of following Bayes's rule. And whereas animals appear to be strikingly risk savvy, humans often seem "irrational" when dealing with probabilistic information. Drawing on research on the description-experience gap in risky choice, we integrate and systematize these findings from disparate fields of inquiry that have, to date, operated largely in parallel. Our synthesis shows that a key factor in understanding inconsistencies in statistical intuitions research is whether probabilistic inferences are based on symbolic, abstract descriptions or on the direct experience of statistical information. We delineate this view from other conceptual accounts, consider potential mechanisms by which attributes of first-hand experience can facilitate appropriate statistical inference, and identify conditions under which they improve or impair probabilistic reasoning. To capture the full scope of human statistical intuition, we conclude, research on probabilistic reasoning across the lifespan, across species, and across research traditions must bear in mind that experience and symbolic description of the world may engage systematically distinct cognitive processes.

People as Intuitive Scientists: Reconsidering Statistical Explanations of Decision Making

A persistent metaphor in decision-making research casts people as intuitive statisticians. Popular explanations based on this metaphor assume that the way in which people represent the environment is specified and fixed a priori. A major flaw in this account is that it is not clear how people know what aspects of an environment are important, how to interpret those aspects, and how to make decisions based on them. We suggest a theoretical reorientation away from assuming people's representations towards a focus on explaining how people themselves specify what is important to represent. This perspective casts decision makers as intuitive scientists able to flexibly construct, modify, and replace the representations of the decision problems they face.

Self-directed learning by preschoolers in a naturalistic overhearing context

Three studies investigated preschoolers' self-directed learning ability in a naturalistic context: learning from overheard speech. In Experiment 1, 4.5- to 6-year-olds were exposed to 4 novel words and 6 arbitrary facts corresponding to a set of co-present toys; in Experiment 2, 3- to 4.5-year-olds heard 5 nouns and 3 facts. In the Pedagogical conditions, children were taught the information with the aid of multiple pedagogical cues, but in the Overhearing conditions, children had to 'listen in' to one side of a phone call to learn the information. Older preschoolers (Experiment 1) learned all items above chance in both conditions. Younger preschoolers (Experiment 2) learned words and facts above chance in the Pedagogical condition, but were at chance at learning words in the Overhearing condition, despite reliably learning facts from overhearing. Experiment 3 demonstrated that younger children's difficulty at learning new words from overhearing could not be explained by only being able to hear one side of the phone conversation, as they similarly struggled when the phone call took place over speakerphone. Measures of children's touch behavior suggest that older children were better able to coordinate their attention between the overheard speech and objects, though even younger children showed evidence of attention to the overheard speech. Together, our results demonstrate that by age 5, children can learn multiple new words and facts via overhearing. This self-directed learning ability depends on being able to coordinate attention between speech and the surrounding environment, a capacity that develops throughout preschool.

Digital Learning Games for Mathematics and Computer Science Education: The Need for Preregistered RCTs, Standardized Methodology, and Advanced Technology

In today's digital information society, mathematical and computational skills are becoming increasingly important. With the demand for mathematical and computational literacy rising, the question of how these skills can be effectively taught in schools is among the top priorities in education. Game-based learning promises to diversify education, increase students' interest and motivation, and offer positive and effective learning experiences. Especially digital game-based learning (DGBL) is considered an effective educational tool for improving education in classrooms of the future. Yet, learning is a complex psychological phenomenon and the effectiveness of digital games for learning cannot be taken for granted. This is partly due to a diversity of methodological approaches in the literature and partly due to theoretical and practical considerations. We present core elements of psychological theories of learning and derive arguments for and against DGBL and non-DGBL. We discuss previous literature on DGBL in mathematics education from a methodological point of view and infer the need for randomized controlled trials for effectiveness evaluations. To increase comparability of empirical results, we propose methodological standards for future educational research. The value of multidisciplinary research projects to advance the field of DGBL is discussed and a synergy of Affective Computing and Optimal Experimental Design (OED) techniques is proposed for the implementation of adaptive technologies in digital learning games. Finally, we make suggestions for game content, which would be suitable for preparing students for university-level mathematics and computer science education, and discuss the potential limitations of DGBL in the classroom.

Understanding Human Intelligence through Human Limitations

Recent progress in artificial intelligence provides the opportunity to ask the question of what is unique about human intelligence, but with a new comparison class. I argue that we can understand human intelligence, and the ways in which it may differ from artificial intelligence, by considering the characteristics of the kind of computational problems that human minds have to solve. I claim that these problems acquire their structure from three fundamental limitations that apply to human beings: limited time, limited computation, and limited communication. From these limitations we can derive many of the properties we associate with human intelligence, such as rapid learning, the ability to break down problems into parts, and the capacity for cumulative cultural evolution.

The rhythm of learning: Theta oscillations as an index of active learning in infancy

Active learning is a critical component of human development, however, the mechanisms supporting it are not fully understood. Given that early learning experiences may affect both infants' immediate learning success, as well as their motivation to learn, it is particularly important to investigate the mechanisms of active learning in this period, when the foundations of learning habits and curiosity are built. Traditional behavioural approaches of studying infant learning face challenges that emerging tools from neuroscience may help relieve. We introduce one such tool, EEG theta oscillations, and propose this neural marker has great potential for offering novel insights into active learning. Theta activity, recorded prior to or during learning, has been shown to be predictive of learning success. We argue that this involvement in memory formation, combined with theta activity's tight association with reward processing, makes theta oscillations a uniquely suited tool for the investigation of motivational mechanisms underlying active learning. We outline research questions as well as methodological approaches pertinent to infant learning and suggest how and why theta oscillations may offer complementary insights. As such, we aim to bridge the gap between cognitive and neural approaches, and advance our knowledge of active learning in development more broadly.

Strategies for selecting and evaluating information

Within the domain of psychology, Optimal Experimental Design (OED) principles have been used to model how people seek and evaluate information. Despite proving valuable as computational-level methods to account for people's behaviour, their descriptive and explanatory powers remain largely unexplored. In a series of experiments, we used a naturalistic crime investigation scenario to examine how people evaluate queries, as well as outcomes, in probabilistic contexts. We aimed to uncover the psychological strategies that people use, not just to assess whether they deviated from OED principles. In addition, we explored the adaptiveness of the identified strategies across both one-shot and stepwise information search tasks. We found that people do not always evaluate queries strictly in OED terms and use distinct strategies, such as by identifying a leading contender at the outset. Moreover, we identified aspects of zero-sum thinking and risk aversion that interact with people's information search strategies. Our findings have implications for building a descriptive account of information seeking and evaluation, accounting for factors that currently lie outside the realm of information-theoretic OED measures, such as context and the learner's own preferences.

Children's Social Information Seeking is Sensitive to Referential Ambiguity

We examined children's spontaneous information seeking in response to referential ambiguity. Children ages 2-5 (n = 160) identified the referents of familiar and novel labels. We manipulated ambiguity by changing the number of objects present and their familiarity (Experiments 1 and 2), and the availability of referential gaze (Experiment 2). In both experiments, children looked to the face of the experimenter more often while responding, specifically when the referent was ambiguous. In Experiment 2, 3- to 4-year olds also demonstrated sensitivity to graded referential evidence. These results suggest that social information seeking is an active learning behavior that could contribute to language acquisition in early childhood.

Category similarity affects study choices in self-regulated learning

During learning, interleaving exemplars from different categories (e.g., ABCBCACAB) rather than blocking by category (e.g., AAABBBCCC) often enhances inductive learning, especially when the categories are highly similar. However, when allowed to select their own study schedules, learners overwhelmingly tend to block rather than interleave. Category similarity has been shown to moderate the relative benefit of interleaved versus blocked study. We investigated whether learners were sensitive to category similarity when choosing exemplars for study, and whether these choices predicted their learning outcomes. In Experiment 1, learners interleaved more often when the categories were highly similar (difficult to discriminate from each other), compared with when similarity was low. In Experiment 2, learners were presented with two sets of categories to learn; categories within each set were similar to each other, but categories were dissimilar across sets. When learners chose to interleave, they tended to switch to a similar rather than dissimilar category. Importantly, learners' study choices predicted their subsequent categorization performance. Our findings suggest that learners are strategic in their search for commonalities within versus differences among categories and can regulate their study behaviors based on category similarity.