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Despite growing interest in spaced retrieval methods, a research gap has been discovered, with insufficient data to support best practises in year-one science. After analysing five systematic action cycles, four interrelated themes emerged: dual coding, cognitive load, peer communication, and feedback-driven metacognition. While literature emphasises the need to take a child's cognitive load into account, the themes of peer communication and dual coding provoked debate among participants. Despite this conflict, an agreement was reached on how limited reading abilities affect retrieval practise. While this small-scale study cannot draw definitive conclusions, I discovered that by optimising cognitive load, teachers can ensure that a task is desirably difficult using dual coding, discussion, and feedback-driven metacognition, all of which contributed to the success of knowledge acquisition methodologies. According to my research, ensuring a retrieval task is challenging, increases children's development of retention of scientific knowledge. However, minimising literacy constraints is critical to avoid thinking being exhausted by writing or reading, rather than recalling and applying. Consequently, the relevance of the study for education and society is illustrated by the findings, which emphasise the necessity to reduce extraneous load (reading and writing) during retrieval tasks and propose potential solutions to this issue.

Bayesian Knowledge Tracing (BKT) is a popular interpretable computational model in the educational mining community that can infer a student's knowledge state and predict future performance based on practice history, enabling tutoring systems to adaptively select exercises to match the student's competency level. Existing BKT implementations do not scale to large datasets and are difficult to extend and improve in terms of prediction accuracy. On the other hand, uninterpretable neural network (NN) student models, such as Deep Knowledge Tracing, enjoy the speed and modeling flexibility of popular computational frameworks (e.g., PyTorch, Tensorflow, etc.), making them easy to develop and extend. To bridge this gap, we develop a collection of BKT recurrent neural network (RNN) cells that are much faster than brute-force implementations and are within an order of magnitude of a fast, fine-tuned but inflexible C++ implementation. We leverage our implementation's modeling flexibility to create two novel extensions of BKT that significantly boost its performance. The first merges item response theory (IRT) and BKT by modeling multidimensional problem difficulties and student abilities without fitting student-specific parameters, allowing the model to easily generalize to new students in a principled way. The second extension discovers the discrete assignment matrix of problems to knowledge components (KCs) via stochastic neural network techniques and supports further guidance via problem input features and an auxiliary loss objective. Both extensions are learned in an end-to-end fashion; that is, problem difficulties, student abilities, and assignments to knowledge components are jointly learned with BKT parameters. In synthetic experiments, the skill discovery model can partially recover the true generating problem-KC assignment matrix while achieving high accuracy, even in some cases where the true KCs are structured unfavorably (interleaving sequences). On a real dataset where problem content is available, the skill discovery model matches BKT with expert-provided skills, despite using fewer KCs. On seven out of eight real-world datasets, our novel extensions achieve prediction performance that is within 0.04 AUC-ROC points of state-of-the-art models. We conclude by showing visualizations of the parameters and inferences to demonstrate the interpretability of our BKT RNN models on a real-life dataset.

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving-related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal-affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later-occurring positive experiences, child-perceived attachment security will inform their content. The sample comprised 160 children (ages 6-12 at Visit 1; 87F/73 M), 66% of whom experienced crEAs (n = 105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental-reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent-child separation); while child-reports assessed concurrent attachment security. A false memory task was administered online [approximately]2.5 years later (Visit 2) to probe the content of affective semantic memories--specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema-based false recognition. Findings suggest that interpersonal-affective semantic schemas include representations of parent-child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment-relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework.

The use of smart technologies in bilingual education opens up new opportunities for language learning. This study aims to examine the influence of bilingualism on neural connections and brain activity in the context of education based on smart technologies. To achieve this goal, an experiment used functional magnetic resonance imaging (fMRI). An additional method was pre-testing and post-testing of students. The study took place at East China Normal University and Shandong University. The participants were 120 students; 50 of them were Americans, 50 were British, and 20 were Australians. All participants were randomly divided into two groups (control and experimental) with 60 participants each. In the comparison of fMRI data between the control and experimental groups, the experimental group demonstrated stronger and broader connectivity in speech-related brain regions, including those involved in speech processing. In addition, bilingualism was found to increase the volume of gray matter in areas related to speech processing, attention, and executive functions. Bilingualism was also associated with increased neuroplasticity, improved executive functions, and increased cognitive flexibility. The members of the experimental group scored more points on the final test (74.71) than the members of the control group (65.9). These findings serve as a basis for further research because they contribute to the theoretical understanding of how bilingualism influences neural connections. The practical implication of the study is that incorporating smart technologies in bilingual education can open up new opportunities for language learning.

How does the act of explaining influence learning? Prior work has studied effects of explaining through a predominantly proximal lens, measuring short-term outcomes or manipulations within lab settings. Here, we ask whether the benefits of explaining extend to academic performance over time. Specifically, does the quality and frequency of student explanations predict students' later performance on standardized tests of math and English? In Study 1 (N = 127 5th-6th graders), participants completed a causal learning activity during which their explanation quality was evaluated. Controlling for prior test scores, explanation quality directly predicted both math and English standardized test scores the following year. In Study 2 (N = 20,384 10th graders), participants reported aspects of teachers' explanations and their own. Controlling for prior test scores, students' own explanations predicted both math and English state standardized test scores, and teacher explanations were linked to test performance through students' own explanations. Taken together, these findings suggest that benefits of explaining may result in part from the development of a metacognitive explanatory skill that transfers across domains and over time. Implications for cognitive science, pedagogy, and education are discussed.

This study used a cluster randomized controlled trial to investigate the effectiveness of two approaches to increasing middle school students' science learning when using an inquiry-based science curriculum. Eighty-nine schools, with 253 teachers and 20,591 students, were randomly assigned into one of three conditions: (a) a treatment condition in which the textbook curriculum was modified based on three principles of cognitive science coupled with teacher professional development (PD), (b) a second treatment condition in which teachers received PD designed to improve their knowledge of the science content, and (c) a business-as-usual control group. The cognitive science treatment had a small but statistically significant positive effect on content learning, with a stronger impact on students of higher prior achievement. Compared to business-as-usual, the intervention to increase teacher content knowledge had no impact.

There are increasingly frequent calls for school mathematics curricula to be informed by robust research evidence. One approach to achieving this is designing evidence-informed learning and teaching resources for the classroom. In this paper, we reflect on our experiences of designing a free and fully resourced complete set of secondary mathematics curriculum materials. We explore in detail the challenges we have encountered in our attempts to apply principles from the cognitive theory of multimedia learning. We focus on tensions we have experienced when simultaneously applying multiple principles and balancing these with other educational considerations. Specifically, we consider trade-offs between redundancy versus clarity, seductive details versus richness, personalisation and emotional design versus abstraction, spatial contiguity and signalling versus parsimony, and pre-training and worked examples versus exploration. We examine the choices and dilemmas we faced, and illustrate our emerging attempts to resolve these tensions through presenting multiple examples from our design work. We conclude with recommendations about how tensions among these design principles might be navigated in curriculum design and we suggest possible avenues for further research in this area.

Metaphor generation is both a creative act and a means of learning. When learning a new concept, people often create a metaphor to connect the new concept to existing knowledge. Does the manner in which people generate a metaphor, via sudden insight (Aha! moment) or deliberate analysis, influence the quality of generation and subsequent learning outcomes? According to some research, deliberate processing enhances knowledge retention; hence, generation via analysis likely leads to better concept learning. However, other research has shown that solutions generated via insight are better remembered. In the current study, participants were presented with science concepts and descriptions, then generated metaphors for the concepts. They also indicated how they generated each metaphor and rated their metaphor for novelty and aptness. We assessed participants' learning outcomes with a memory test and evaluated the creative quality of the metaphors based on self- and crowd-sourced ratings. Consistent with the deliberate processing benefit, participants became more familiar with the target science concept if they previously generated a metaphor for the concept via analysis compared to via insight. We also found that metaphors generated via analysis did not differ from metaphors generated via insight in quality (aptness or novelty) nor in how well they were remembered. However, participants' self-evaluations of metaphors generated via insight showed more agreement with independent raters, suggesting the role of insight in modulating the creative ideation process. These preliminary findings have implications for understanding the nature of insight during idea generation and its impact on learning.

While research evidence has the potential to improve classroom practice, research-practice gaps continue to persist not least owing to the limited relevance of research findings for practice. A common approach in healthcare to address the research-practice gap is to form research priority setting partnerships (PSPs) in which stakeholders identify questions they would like research to answer. This paper presents the results from such a PSP in education with a focus on cognitive science research, which has received increased attention in the past few years owing to its potential to explain memory and learning processes and inform classroom practice. Over 400 questions from teachers were collected using an online survey. The final 15 research priorities highlight the need for research on a wider range of subjects, settings and phases as well as research designs that take the complexity of classrooms into consideration and aim to answer how different teaching strategies interact with each other as well as student motivation and agency at the micro- and macro-level. The role of teacher expertise vs. fidelity to original research designs should also be investigated further. Overall, this paper highlights the importance of taking teacher voice into account to ensure that new research in the field is both academically rigorous and practically relevant.

Research on the development of active learning and information search behaviors has been growing rapidly, drawing interest from multiple disciplines, from developmental psychology to cognitive science and artificial intelligence. These different perspectives can open pathways to understanding how preschool-age children grow into adaptive and efficient active learners. However, the lack of a shared vocabulary, operationalizations, and research paradigms has led to limited cross-talk and some conflicting findings. In this article, we advocate for using a shared operationalization of a "good" information-search strategy, as a function of its "efficiency" and "effectiveness" within a given ecology, based on the information-theoretic measure of expected information gain and observed behavioral outcomes, respectively. We also discuss factors that should be considered when designing experiments that examine children's information-search competence, specifically, using formal models as performance benchmarks and accounting for children's prior knowledge, assumptions, and self-generated goals.

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account.

Financial literacy is an important life skill, yet the impact of financial education has often been found to be modest. We conducted a field experiment to assess the effectiveness of a postinstruction intervention using a smartphone app that incorporated cognitive science principles aimed at improving learning. College students who completed a required credit review workshop during their sophomore year used the smartphone app in one of three practice conditions: control (no practice), massed, or spaced retrieval practice with elaborative feedback. On a final assessment about 5 months later, students who engaged in spaced retrieval practice were superior to those in the control and massed practice conditions in terms of knowledge. Given the ubiquity of smartphones today, the results highlight the potential of harnessing easily accessible technology as learning tools to augment the retention and transfer of knowledge.

Purpose: This study aims to identify how Brazilian followers of an X/Twitter profile engage in theory-building processes leading up to the January 8, 2023 riots in Brasília, the Brazilian capital. This paper seeks to understand how cognitive and sociocultural processes weave together to weaponize collective knowledge construction that, in isolation, could be seen as virtuous but, in specific contexts, might lead to radicalization. Design/methodology/approach: This study uses qualitative content analysis of comments on ambiguous X/Twitter posts published by a conspiratorial profile associated with former President Jair Bolsonaro. Content was published in the three weeks that preceded the coup d'état attempt by Bolsonaro supporters on January 8, 2023. Findings: Findings point to users' resorting to intuitive knowledge to support sensemaking processes in their search for subliminal meanings in tweets. That includes, for example, attempts to crack binary code-encrypted messages. This study also identified practices of cross-media sourcing, where users collect evidence from alternative social media channels to interpret messages containing verbal and visual information. Finally, this study found that religious symbols are often instrumentalized and become a lens through which followers organize information to integrate with their existing knowledge and assumptions. Research limitations/implications: With this work, the authors build on existing scholarship on epistemologies used by conspiratorial and radicalized groups as they engage in systematic sensemaking and often refer to religion to interpret messages that motivate extreme political position-taking. This study addresses a similar phenomenon as it unfolds in an understudied geographical context (Brazil) and seeks to demonstrate how individuals engage in collective sensemaking practices. The authors hope that their findings inform educators as they explore the affordances of social media to foster positive collective learning experiences in reasoning supported by social media. Originality/value: The originality of this study is twofold. First, this study uses an analytical lens that draws on the learning sciences and cognitive science for inquiry of radicalization happening around social media. The authors understand that social media lend themselves particularly interesting to the analysis, as they are settings where notions of mastery blur, and individuals engage in conversations on complex, controversial topics. With that engagement, they demonstrate willingness to reason collectively. Second, this study investigates how those phenomena unfold in an understudied context, responding to calls for more diversity in research in the learning sciences as well as in media studies.

Educational inclusion aims to support all students through instructional and contextual designs that promote learning. Educators are assigned the task of teaching an increasingly neurodiverse population with varying neurological processes and perceptions. Traditional school-based settings are designed for individuals with less neurodivergence, and, therefore, may not adequately facilitate an environment supportive of learning for all students. Additionally, neurodiverse learners, including those with autism, may have sensory processing differences that impact occupational participation and performance. Understanding the processing differences of neurodivergent students and interventions that support those needs may facilitate successful inclusion programs. Research and data analysis confirmed the "Ready to Learn and Play (RtLP)" as an evidence-based framework to facilitate participation and performance for individuals with neurodiversity and sensory processing challenges, both for in-person (statistical significance p = 0.001) and virtual presentation of the "RtLP" content (statistical significance p = 0.00000607). Through a neuroaffirming approach to teaching, coaching, and training, "RtLP" may assist in supporting individuals with sensory regulation challenges to participate to the greatest extent possible in inclusive education.

Abundant laboratory and classroom research demonstrate the superior effectiveness of effortful learning strategies based on cognitive science over re-reading, highlighting, and other strategies more widely used by college students. However, persuading students to adopt and adhere to effective strategies is difficult. This article outlines a novel, peer-to-peer intervention rooted in the Knowledge, Belief, Commitment, and Planning theoretical framework (McDaniel & Einstein, 2020) that emphasizes the need for students to believe in, commit to, and plan to use effective strategies rather than simply "know" them. Opportunities for faculty and learning center personnel to incorporate elements of the intervention into existing programming and adapt them to local institutional needs are described.

This study explored the effects of a professional development (PD) on neuroeducation general pedagogical knowledge and instructional strategies for three cohorts of in-service teachers on their teaching self-efficacy beliefs. Teacher self-efficacy is one of the most influential factors for teaching practices, student outcomes, and teacher persistence in the profession. We utilized a neuroeducation instructional framework in the PD to provide knowledge of learning sciences research to inform practices across all curricula. The PD was designed to help teachers translate research into general pedagogical practices to address topics such as classroom emotional climate, student attention and memory, meaningful application of knowledge, and creative problem solving. Two cohorts of teachers engaged in the PD for five months during two consecutive spring semesters, and one cohort participated in a two-week session of the PD. We used a pretest-posttest quasi-experimental design and findings revealed increases in teachers' self-efficacy beliefs in all cohorts. Implications for practice and research are discussed.

Cognitive science-informed approaches have gained considerable influence in education in the UK and internationally, but not much is known about how teachers perceive cognitive science-informed strategies or enact them within the contexts of their everyday classrooms. In this paper, we discuss the perceptions and experiences of cognitive science-informed strategies of 13 teachers in England. The paper critically explores how the teachers understood and used cognitive science-informed strategies in their teaching, their views of the benefits and challenges for different subjects and groups of learners, and their reflections on supporting factors and barriers for adopting the strategies in their schools. The teachers' accounts illustrate some of the many complexities of adopting cognitive science-informed approaches in real-life educational settings. Drawing on their narratives, the paper emphasises the importance of acknowledging different contextual dimensions and the dynamic interactions between them to understand when and how teachers enact cognitive science-informed approaches in their classrooms.

The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal errors and trigger belief revision in the service of error correction. Across two experiments, 1149 participants engaged in reasoning about force and motion (a domain with well-documented misconceptions) in a pre-training-training-post-training design. The two experiments manipulated the type of mental model manipulated in the thought experiments (i.e., whether participants reasoned about forces acting on their own bodies vs. on external objects), as well as the level of relational and argumentative reasoning about the outcomes of the thought experiments. The results showed that: (i) thought experiments can serve as a tool to elicit inconsistencies in one's representations; (ii) the level of relational and argumentative reasoning determines the level of belief revision in the service of error correction; and (iii) the type of mental model manipulated in a thought experiment determines its outcome and its potential to initiate belief revision. Thought experiments can serve as a valuable teaching and learning tool, and they can help us better understand the nature of error detection and correction systems.

This article's authors describe both the advantages of a literacy-targeted introductory course and how it might be taught by employing evidence-based teaching practices developed by cognitive scientists to maximize learning. This pairing of literacy-focused content with evidence-based pedagogy is intended to enhance student learning while focusing on economic literacy rather than mastery of an encyclopedia of models. The literacy-targeted approach reduces the number of models and concepts introduced, therefore leaving more time for teaching strategies that increase comprehension and retention. The authors propose using two foundational economic models that can be utilized to illuminate a variety of economic concepts. These two models are illustrative of this approach, and others might be used. Frequent and varied use of these models deepens student understanding and lengthens retention.

Research in cognitive science has highlighted the effectiveness of several learning techniques, and a number of studies have analyzed their prevalence among university students and their relationship with academic achievement. In this study, we surveyed a large, heterogeneous sample of secondary school students to reveal how often they use research-supported techniques in comparison with other frequent techniques, and we analyzed the association between their study strategies and school achievement. We also assessed the associations between study techniques and several students' beliefs and attitudes toward learning (self-efficacy, goal orientation, control beliefs, growth mindset, and examination anxiety). Results showed that, except for distributed practice, only those techniques that are supported by previous research yielded an association with achievement, and they exhibited higher associations with self-efficacy, growth mindset, control beliefs, and learning goal orientation than non-supported techniques.

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

The approach that students take in their studies at university is critical not only for their academic success but is equally important in life-long learning for their career and professional development. Heutagogy is the study of self-determined learning and it is important that we appraise how students in higher education are developing their metacognitive awareness in how they learn and study effectively. Cognitive science is an interdisciplinary research area that involves the scientific study of the human mind. It helps provide new knowledge in relation to areas such as memory, problem solving, knowledge transfer and understanding of complex topics. Cognitive science has demonstrated that re-testing oneself on material when learning, enhances and promotes greater retention of knowledge compared to re-reading the material. Learning that is distributed or spaced out over multiple study sessions also allows for greater retention of knowledge in the longer-term compared to 'cramming' of information. To evaluate the use of effective study approaches and habits across three different levels of study in an undergraduate pharmacy programme, a survey study was employed. A paper-based survey was completed by first-, second- and third-year undergraduate pharmacy students (n=192) during class sessions. Although there was some evidence of metacognitive awareness such as using testing (retrieval practice) with practice problems; across all years, suboptimal study approaches such as rereading, copying notes and cramming were endorsed. A schedule of deadlines shaped the organisation of study and time management for most students. Self-testing was predominantly used to test learning rather than an approach used during learning. There was evidence of a difference between the cohorts in relation to decisions for prioritizing studying, returning to review course material and re-reading. The evidence from this study demonstrates that learners would require training on metacognitive awareness and effective study strategies to enable their self-determined learning capabilities to evolve. The linear progression through a programme of study alone will not achieve this. There is a need to embed and emphasize effective strategies for learning into curricula and for faculty to utilise metacognitive awareness in their teaching.

Cognitive science has gathered robust evidence supporting the hypothesis that cognitive processes do not occur in an amodal format but take shape through the activation of the sensorimotor systems of the agent body, which works as simulation system upon which concepts, words, and thought are based. However, studies that have investigated the relationship between language and cognitive processes, as both embedded processes, are very rare. In this study, we investigated the hypothesis that intelligence is associated with referential competence, conceived as the ability to find words to refer to our subjective and perceptual experience, and to evoke understanding of this experience in the listener. We administered the WAIS-IV test to 32 nonclinical subjects and collected autobiographical narratives from them through the Relationship Anecdotes Paradigm Interview. The narratives were analyzed linguistically by applying computerized measures of referential competence. Intelligence scores were found to correlate with the use in narratives of words related to somatic and sensory sensations, while they were not associated with other measures of referential competence related to more abstract domains of experience or based on vivid or reflective dimensions of language style. The results support the hypothesis that sensorimotor schemas have an intrinsic role in language and cognition.

Cognitive epidemiology investigates cognitive predictors of health and disease outcomes. Post-operative cognitive impairment is a common complication of surgery but has been neglected as a health outcome in cognitive epidemiology research. This is despite the fact that knowledge of cognitive predictors of post-operative cognitive impairment can be utilized for risk stratification, informed decision-making (in elective surgery), and personalized care of patients during the postoperative period. In this narrative review, the current literature on cognitive predictors of post-operative cognitive impairment and gaps therein are summarized.

The interaction between humans and computers is one of the most pressing challenges in contemporary society. Subtleties of this communication require that human-computer interfaces are conceptualized and tested by highly specialized experts. Here we describe a novel academic course for human-oriented designers and project managers, with the emphasis being put on the communication between the human brain and computer. Outcomes of the course were validated on 12 course participants both: qualitatively -- educational experience, and quantitatively -- outcomes of a project and final exam. T-test comparison revealed that students who participated in the course gained specialized knowledge, as compared to students who did not take part in the course. Moreover, students generally agreed that the course was helpful and interesting, however, about one-third of them considered the course hard. All in all, we find our brain-computer interfaces course useful in instructing specialist oriented on the human-factor of the communication between human and the machine.

Children's informal and formal learning experiences with geometric shapes currently result in misconceptions that persist into adulthood. Here, we combine research from mathematics education as well as cognitive science pertaining to concepts, categories, and learning strategies to propose a more optimal progression that is better specified and justified than the current standards. To do so, we reframed what constitutes a "simple" shape from perceptual simplicity to simplicity of properties. Our Property-Based Shape Sequence uses property-based criteria of what makes shapes "simple" and progresses in a way that affords opportunities for learners to develop hierarchical conceptions of two-dimensional and three-dimensional shapes. Our goals are threefold: (1) recommend an optimal, mathematically-correct shape learning sequence, (2) correct misconceptions that adults and children harbor about shapes, and (3) encourage cross-disciplinary collaborations between mathematics education and psychology researchers to validate the proposed learning sequence.

A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when "everything is connected." This paper offers a framework for tackling that challenge by identifying *scale* as a conceptual mooring for the interdisciplinary study of language systems. The paper begins by defining the scale concept--simply, the possibility for a measure to be larger or smaller in different instances of a system, such as a phonemic inventory, a word's frequency value in a corpus, or a speaker population. We review sites of scale difference in and across linguistic subsystems, drawing on findings from linguistic typology, grammatical description, morphosyntactic theory, psycholinguistics, computational corpus work, and social network demography. We consider possible explanations for scaling differences and constraints in language. We then turn to the question of *dependencies between* sites of scale difference in language, reviewing four sample domains of scale dependency: in phonological systems, across levels of grammatical structure (Menzerath's Law), in corpora (Zipf's Law and related issues), and in speaker population size. Finally, we consider the implications of the review, including the utility of a scale framework for generating new questions and inspiring methodological innovations and interdisciplinary collaborations in cognitive-scientific research on language.

Given the recent call to strengthen collaboration between researchers and relevant practitioners, we consider participatory design as a way to advance Cognitive Science. Building on examples from the Learning Sciences and Human-Computer Interaction, we (a) explore "what," "why," "who," "when," and "where" researchers can collaborate with community members in Cognitive Science research; (b) examine the ways in which participatory-design research can benefit the field; and (c) share ideas to incorporate participatory design into existing basic and applied research programs. Through this article, we hope to spark deeper discussions on how cognitive scientists can collaborate with community members to benefit both research and practice.

The historical tendency to view medicine as both an art and a science may have contributed to a disinclination among clinicians towards cognitive science. In particular, this has had an impact on the approach towards the diagnostic process which is a barometer of clinical decision-making behaviour and is increasingly seen as a yardstick of clinician calibration and performance. The process itself is more complicated and complex than was previously imagined, with multiple variables that are difficult to predict, are interactive, and show nonlinearity. They appear to characterise a complex adaptive system. Many aspects of the diagnostic process, including the psychophysics of signal detection and discrimination, ergonomics, probability theory, decision analysis, factor analysis, causal analysis and more recent developments in judgement and decision-making (JDM), especially including the domain of heuristics and cognitive and affective biases, appear fundamental to a good understanding of it. A preliminary analysis of factors such as manifestness of illness and others that may impede clinicians' awareness and understanding of these issues is proposed here. It seems essential that medical trainees be explicitly and systematically exposed to specific areas of cognitive science during the undergraduate curriculum, and learn to incorporate them into clinical reasoning and decision-making. Importantly, this understanding is needed for the development of cognitive bias mitigation and improved calibration of JDM in clinical practice.

When the Learning Sciences emerged in 1991, there was an ethos of studying learning in humans and machines in conjunction with one another. This ethos reflected three decades of prior work on the interdisciplinary study of learning; however, in the three decades since the emergence of the Learning Sciences, it seems to have largely disappeared. I begin by describing the ethos that was prevalent in 1991 using quotations from the inaugural editorial of the "Journal of the Learning Sciences." I then describe how this ethos was prevalent decades before the Learning Sciences in four distinct approaches to cognitive science research, which I call the "Four C's"--cognitivism, constructivism, cybernetics, and connectionism. I suggest three reasons why the Learning Sciences moved away from the use of artificial intelligence as a central tool for thinking about learning, noting that these reasons do not suggest a fundamental incompatibility between the two. I end by discussing how Learning Scientists might once again embrace artificial intelligence and computational modeling and use them as tools for gaining insight into the constructivist, situated, and socio-cultural nature of learning.

Network analyses of the phonological mental lexicon show that words are clustered into communities and phonologically dissimilar words can be connected to each other through distant paths. Here we investigate whether behavioral traces of the large-scale structure of the phonological lexicon can be obtained. Participants listened to pairs of spoken words and made phonological similarity judgments for word pairs with varying path lengths and community membership. Path length in the phonological network represented the number of steps needed to traverse from one word to another word in the phonological network. Word pairs were either from the same phonological community or from different communities. Results indicated that participants were sensitive to large-scale structure of the phonological lexicon. Word pairs residing in the same community were more likely rated as similar sounding than word pairs from different communities. Word pairs with longer path lengths were less likely rated as similar sounding than word pairs with shorter path lengths. Computational simulations suggested that the behavioral findings could be accounted for via a spreading activation mechanism implemented on the phonological network. Taken together, our results provide converging evidence that people are sensitive to the large-scale structure of the phonological language network and have implications for our understanding of the nature of phonological similarity representations in the mental lexicon.

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.

Research regarding dysgraphia, an impairment in writing, is attaining more attention in recent times. The existing studies on dysgraphia draw insights from cognitive, behavioural, neurological, and genetic fields of knowledge. However, these multiple studies on dysgraphia fail to illustrate how these cognitive, behavioural, neurological, and genetic systems interact and intersect in dysgraphia. Therefore, the studies could not offer a comprehensive understanding of dysgraphia. In order to fill this gap, the review attempts to study dysgraphia using the notion of modularity by accommodating insights from cognitive, behavioural, neurological, and genetic aspects of dysgraphia. Such a profound understanding could facilitate an early diagnosis and holistic intervention towards dysgraphia.

Purpose: I take as a starting point the disparaging comments about the place of history and philosophy of education in initial teacher education (ITE) made by the chair of the Teacher Education Expert Panel established by the Australian Government in 2023, which I take to be the most recent attempt at resurrecting the tired debate over "the art versus science of teaching". I draw on an example from my own ITE classroom to tease possibilities for how historical research and research in the sciences of learning can be in dialogue and collaborate to deepen educational scholarship. Design/Methodology/Approach: The purpose of this essay is to reflect on the scholarly discussions and debates in the sciences of learning that historical researchers of education have largely been absent or excluded from. I argue that historical researchers have an important role to play as "critical friends" to the burgeoning fields of cognitive science and educational neuroscience. Findings: From my cursory and singular account, I highlight two key areas of education where historical research and the sciences of learning can be fruitfully in dialogue and collaboration: Firstly, on the cultural dimension that prefigures learners' approach to learning; and secondly, on the factors that enable or disable effective learning. Originality/Value: This essay sets an agenda for historical researchers in education to exercise "critical friendship" with the cognitive and brain sciences of learning, specifically by adding temporal considerations to the way biology, psychology, and sociality interact to produce different educational outcomes. This will be of "practical" service to ITE -- and to students in schools.

Perceivers spontaneously segment ongoing activity into discrete events. This segmentation is important for the moment-by-moment understanding of events, but may also be critical for how events are encoded into episodic memory. In 3 experiments, we used priming to test the possibility that perceptual event boundaries organize memory for everyday activity into episodic units. Viewers watched movies of everyday activities, such as someone washing a car, and then performed a yes-no recognition task using pictures taken from the movies. Some target pictures were preceded by a prime picture taken from 5 s previously in the movie. This produced priming, reducing response times for the target picture. Priming was greater when the prime was part of the same perceptual event as the target than when it was part of a different event, suggesting that event structure organizes episodic memory. This effect persisted when the sequence of activity was scrambled during encoding, which suggests that it reflects, in part, knowledge about event types and not just the specifics of a given episode.

The critical question for cognitive scientists is what does cognitive science do, if anything, for people? Cognitive science is primarily concerned with human cognition but has fallen short in continuously and critically assessing the "who" in human cognition. This complacency in a world where white supremacist and patriarchal structures leave cognitive science in the unfortunate position of potentially "supporting" those structures. We take it that many cognitive scientists operate on the assumption that the study of human cognition is both interesting and important. We want to invoke that importance to note that cognitive scientists must continue to work to show how the field is useful to all of humanity and reflects a humanity that is not white by default. We wonder how much the field has done, and can do, to show that it is useful not only in the sense that we might make connections with researchers in other fields, win grants and write papers, even of the highest quality, but useful in some material way to the billions of non-cognitive scientists across the globe.

There's no doubt that research bearing directly on classroom practice is crucial. In this article Daniel Willingham maintains that it's useful for educators also to know the basic science around children's cognition, emotion, and motivation, because beliefs about what children are like inevitably influence teaching practice. The purpose of learning the science would be to broaden and deepen that knowledge, and to disabuse teachers of any misconceptions they might have. Still, not everything of concern to scientists is valuable to educators. The author makes the point that some statements concerning children's learning are perfectly sound scientifically but should not influence educational decisions. That includes some statements that seem like they ought to have a direct bearing on classrooms. In this article, Willingham describes three types of statements scientists make, only one of which he thinks ought to influence teaching practice, but he points out that even that type of statement, can be misapplied.

A critical goal for science education is to design and implement learning activities that develop a deep conceptual understanding, are engaging for students, and are scalable for large classes or those with few resources. Approaches based on peer learning and online technologies show promise for scalability but often lack a grounding in cognitive learning principles relating to conceptual understanding. Here, we present a novel design for combining these elements in a principled way. The design centers on having students author multiple-choice questions for their peers using the online platform PeerWise, where beneficial forms of cognitive engagement are encouraged via a series of supporting activities. We evaluated an implementation of this design within a cohort of 632 students in an undergraduate biochemistry course. Our results show a robust relationship between the quality of question authoring and relevant learning outcomes, even after controlling for the confounding influence of prior grades. We conclude by discussing practical and theoretical implications.

Understanding large number data is essential for making sense of real-world problems. For the research reported here, our intent was to find connections between quantitative cognitive science studies and our prior qualitative study about participants' understanding of the relative size of large numbers. While all 23 cognitive science research studies that we reviewed focused only on participants' placements of large numbers on number lines, our study focused on preservice teachers' (PSTs; n = 128) strategies -- placements "and explanations" for their placements -- of one billion on a number line bounded by zero and one trillion. Our intent was to answer the following question: What are the connections between three plausible placements substantiated by cognitive scientists -- linear; log-linear; and segmented linear -- of large numbers on number line tasks and the nine strategies identified in our qualitative study? We highlight exemplars from the nine PSTs' strategies to show connections between the three plausible placements. In doing so, we illustrate that asking participants to place large numbers on number lines only gives a partial picture of their understanding. Their explanations expand the placements by revealing inconsistencies among the placements and explanations. Implications for future research are also discussed.