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Cognitive modeling involves the creation of computer simulations that emulate the internal processes of the mind. This set of papers are the five best representatives of the papers presented at the 17th International Conference on Cognitive Modeling, ICCM 2019. While they represent a diversity of techniques and tasks, they all also share a striking similarity: They make strong statements about the importance of accounting for individual differences., (© 2020 Her Majesty the Queen in Right of Canada Topics in Cognitive Science © 2020 Cognitive Science Society.)

When two cognitive processes contribute to a behavioral output-each process producing a specific distribution of the behavioral variable of interest-and when the mixture proportion of these two processes varies as a function of an experimental condition, a common density point should be present in the observed distributions of the data across said conditions. In principle, one can statistically test for the presence (or absence) of a fixed point in experimental data to provide evidence in favor of (or against) the presence of a mixture of processes, whose proportions are affected by an experimental manipulation. In this paper, we provide an empirical diagnostic of this test to detect a mixture of processes. We do so using resampling of real experimental data under different scenarios, which mimic variations in the experimental design suspected to affect the sensitivity and specificity of the fixed-point test (i.e., mixture proportion, time on task, and sample size). Resampling such scenarios with real data allows us to preserve important features of data which are typically observed in real experiments while maintaining tight control over the properties of the resampled scenarios. This is of particular relevance considering such stringent assumptions underlying the fixed-point test. With this paper, we ultimately aim at validating the fixed-point property of binary mixture data and at providing some performance metrics to researchers aiming at testing the fixed-point property on their experimental data., (© 2023. The Author(s).)

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.

Teams are a fundamental aspect of life-from sports to business, to defense, to science, to education. While the cognitive sciences tend to focus on information processing within individuals, others have argued that teams are also capable of demonstrating cognitive capacities similar to humans, such as skill acquisition and forgetting (cf., Cooke, Gorman, Myers, & Duran, 2013; Fiore et al., 2010). As artificially intelligent and autonomous systems improve in their ability to learn, reason, interact, and coordinate with human teammates combined with the observation that teams can express cognitive capacities typically seen in individuals, a cognitive science of teams is emerging. Consequently, new questions are being asked about teams regarding teamness, trust, the introduction and effects of autonomous systems on teams, and how best to measure team behavior and phenomena. In this topic, four facets of human-autonomy team cognition are introduced with leaders in the field providing in-depth articles associated with one or more of the facets: (1) defining teams; (2) how trust is established, maintained, and repaired when broken; (3) autonomous systems operating as teammates; and (4) metrics for evaluating team cognition across communication, coordination, and performance., (© 2024 Cognitive Science Society LLC.)

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.

Abstract concepts are relevant to a wide range of disciplines, including cognitive science, linguistics, psychology, cognitive, social, and affective neuroscience, and philosophy. This consensus paper synthesizes the work and views of researchers in the field, discussing current perspectives on theoretical and methodological issues, and recommendations for future research. In this paper, we urge researchers to go beyond the traditional abstract-concrete dichotomy and consider the multiple dimensions that characterize concepts (e.g., sensorimotor experience, social interaction, conceptual metaphor), as well as the mediating influence of linguistic and cultural context on conceptual representations. We also promote the use of interactive methods to investigate both the comprehension and production of abstract concepts, while also focusing on individual differences in conceptual representations. Overall, we argue that abstract concepts should be studied in a more nuanced way that takes into account their complexity and diversity, which should permit us a fuller, more holistic understanding of abstract cognition. [ABSTRACT FROM AUTHOR]

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.

Reading primary research literature is an essential skill for all scientists and students on science degree programmes, however little is known about how researchers at different career stages interact with and interpret scientific papers. To explore this, we conducted a survey of 260 undergraduate students and researchers in Biological Sciences at a research intensive UK university. Responses to Likert scale questions demonstrated increases in confidence and skill with reading the literature between individuals at each career stage, including between postdoctoral researchers and faculty academics. The survey indicated that individuals at different career stages valued different sections of scientific papers, and skill in reading the results section develops slowly over the course of an academic career. Inexperienced readers found the methods and results sections of research papers the most difficult to read, and undervalued the importance of the results section and critical interpretation of data. These data highlight a need for structured support with reading scientific literature at multiple career stages, and for senior academics to be aware that junior colleagues may prioritise their reading differently. We propose a model for the development of literature processing skills, and consider the need for training strategies to help inexperienced readers engage with primary literature, and therefore develop important skills that underpin scientific careers. We also encourage researchers to be mindful of language used when writing papers, and to be more inclusive of diverse audiences when disseminating their work. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine existing learning innovation systems and propose a systematic methodology of delivering educational innovations in the right amount, in the right place and at the right time. Design/methodology/approach: Higher education is not effectively incorporating new discoveries in cognitive science and human learning into effective teaching strategies. In this paper, the authors explore the various impediments to change. To partially overcome these barriers, Drexel University, in collaboration with Untra Academic Management Solutions, LLC, has embarked on the development of a knowledge management system to assist instructors in obtaining, implementing, evaluating and disseminating new educational innovations. The system as envisioned would be capable of adapting to various educational environments and evolving with changes in curricula, faculty expectations, learning outcomes and student characteristics. Findings: The SocraticNet as a learning environment is stimulated, based on Socratic inquiry among teachers and learners. It is an interactive social network system utilising Web 2.0-based emerging technologies fostering communication and sharing among faculty, students and others (e.g. librarians) engaged in a particular course or other educational experience. Students learn by sharing what they know, by asking questions, judging and evaluating the retrieved information, and using this information efficiently in completing their assignments or research papers. This approach results in multidimensional information flow: instructor to instructor, instructor to student, student to instructor, student to student: which adds a new richness to the interactions between faculty and students providing the framework for a true learning community. Research limitations/implications: The present work has been conducted at one institution. This can be extended to include more institutions and tried with different disciplines. There are implications for changes in relation to teaching and learning approached adopted in higher education and also for development of technology tailored to address issues emanating from the scenario. Practical implications: The research work presented has the potential to stimulate debate to further develop and refine thinking on the role and use of technology directed at improving teaching and learning in higher education. Originality/value: This paper examines learning and teaching styles in higher education taking cognizance of conditions and factors impeding new innovations in practice. Details discussions are presented and unique suggestions are made.

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.

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them., (© 2021 Cognitive Science Society LLC.)

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.

Do highly productive researchers have significantly higher probability to produce top cited papers? Or do high productive researchers mainly produce a sea of irrelevant papers—in other words do we find a diminishing marginal result from productivity? The answer on these questions is important, as it may help to answer the question of whether the increased competition and increased use of indicators for research evaluation and accountability focus has perverse effects or not. We use a Swedish author disambiguated dataset consisting of 48.000 researchers and their WoS-publications during the period of 2008–2011 with citations until 2014 to investigate the relation between productivity and production of highly cited papers. As the analysis shows, quantity does make a difference. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on the topics including demonstrates exciting results based on self-imitation learning within a cross-modal setting; and providing advances in controllable generation and interpolation across attributes.

This paper discusses adapting Churches' approach to large-scale teacher/researcher conceptual replications of major "science of learning" findings, to increase teachers' engagement with empirical research on, and building research networks for, gathering data on the science of learning. The project here demonstrated the feasibility of teacher-led randomised controlled trials for conceptually replicating the effects of cognitive science on learning, as specified by researchers. It also indicated high levels of interest by teachers in applying more science of learning in their practice. The approach gave freedom to teachers to design interventions, choose research methods, and measure outcomes, even though such freedom would be in tension with some scientific research which relies on constraining the sources of variation. This paper discusses how a balance can be struck between the objectives of teachers and researchers engaged in replicating cognitive science findings, and promoting teacher engagement in conceptual replication research.

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species. Our letter formulates open research questions for cognitive science concerning the varieties of rules and representations that underwrite various LoT-based systems and how these variations can help researchers taxonomize cognitive systems., (© 2022 Cognitive Science Society LLC.)

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.

Engaging students in authentic problem solving concerning environmental issues in near-surface complex Earth systems involves both developing student conceptualization of Earth as a system and applying that scientific knowledge using techniques that model those used by professionals. In this first paper of a two-part series, we review the state of the geoscience education research field related to systems thinking in the context of Earth systems. The purpose of this study is to build on previous syntheses by conducting a configurative literature review that addresses the following research questions: (1) What are the characteristics of studies that address systems thinking in the context of Earth systems? (2) What conceptual frameworks for systems are present in the geoscience education research literature on systems thinking in the context of Earth systems? (3) How are these conceptual frameworks operationalized in research and educational interventions aimed at understanding and supporting systems thinking in the context of Earth systems? Twenty-seven papers met inclusion and exclusion criteria. Content analysis was conducted on each of these papers, and systems ideas were analyzed using the constant comparative method. Four conceptual frameworks were identified: Earth systems perspective, Earth systems thinking skills, complexity sciences, and authentic complex Earth and environmental systems. This study is, to our knowledge, the first systematic review in this area and allows a more consistent comparison of new findings with previous work. It also facilitates strengthening connections with cognitive science and education research literature related to systems thinking and complex systems.

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

The need to complete multiple tasks concurrently is a common occurrence both daily life and in occupational activities, which can often include simultaneous cognitive and physical demands. As one example, there is increasing availability of head-worn display technologies that can be employed when a user is mobile (e.g., while walking). This new method of information presentation may, however, introduce risks of adverse outcomes such as a decrement to gait performance. The goal of this study was thus to quantify the effects of a head-worn display (i.e., smart glasses) on motor variability during gait and to compare these effects with those of other common information displays (i.e., smartphone and paper-based system). Twenty participants completed four walking conditions, as a single task and in three dual-task conditions (three information displays). In the dual-task conditions, the information display was used to present several cognitive tasks. Three different measures were used to quantify variability in gait parameters for each walking condition (using the cycle-to-cycle standard deviation, sample entropy, and the “goal-equivalent manifold” approach). Our results indicated that participants used less adaptable gait strategies in dual-task walking using the paper-based system and smartphone conditions compared with single-task walking. Gait performance, however, was less affected during dual-task walking with the smart glasses. We conclude that the risk of an adverse gait event (e.g., a fall) in head-down walking conditions (i.e., the paper-based system and smartphone conditions) were higher than in single-task walking, and that head-worn displays might help reduce the risk of such events during dual-task gait conditions. [ABSTRACT FROM AUTHOR]

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co‐authorship network—edge density, transitivity, and maximum subgraph size—as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co‐authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community. DeStefano, Oey, Brockbank, and Vul explore interdisciplinary collaboration using data‐driven measures of research topics and co‐authorship, constructed from a rich dataset of over 11,000 Cogsci conference papers. Findings suggest the cognitive science research community has become increasingly integrated in the last 19 years. [ABSTRACT FROM AUTHOR]

Cognitive science has much to contribute to the general scientific body of knowledge, but it is also a field rife with possibilities for providing background research that can be leveraged by artificial intelligence (AI) developers. In this introduction, we briefly explore the history of AI. We particularly focus on the relationship between AI and cognitive science and introduce this special issue that promotes the method of inspiring AI development with the results of cognitive science research., (© 2022 Cognitive Science Society LLC.)

An editorial is presented which discusses the collaborative writing with multiple authors which has additional challenges including varied levels of engagement of coauthors, provision of fair credit through authorship or acknowledgements and acceptance of diversity of work styles. Also discussed are the 10 simple rules for collaboratively writing a multi-authored paper which include to build a writing team wisely; to create a data management plan and to jointly decide on authorship guidelines.

Today, scenario is one of development & growing field and from which more attention in industry sector. Artificial Intelligence (AI) is one of method from which can improve the industrialization. Using AI in industry can make the industry to depend on stronger, inexpensive and more precise mode of marketing. Artificial Intelligence (AI) is a cognitive science to enables human to explore many intelligent ways to model our sensing and reasoning processes. Industrial AI is a methodical order to allow engineers to methodically expand and organize AI algorithms with repeating and steady successes. In this paper, the key enablers for this transformative technology along with their significant advantages are discussed. [ABSTRACT FROM AUTHOR]

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.

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.

[This paper is part of the Focused Collection on Curriculum Development: Theory into Design.] This paper discusses the theoretical framework and curriculum materials that form the basis of the Investigative Science Learning Environment (ISLE) approach to learning and teaching physics. ISLE, as a philosophical approach to learning, has two core intentionalities: (i) We want students to learn physics by thinking like physicists; by engaging in knowledge-generating activities that mimic the actual practices of physics and using the reasoning tools that physicists use when constructing and applying knowledge. (ii) The way in which students learn physics should enhance their well being. These intentionalities form the basis upon which we build a bricolage of multiple theoretical perspectives. We will show how the ISLE approach and its implementation is shaped by (a) the epistemological commitments of physics, (b) the findings of cognitive science, (c) theories of learning communities, and (d) the perspective of universal design. We will present both qualitative and quantitative data that demonstrate the effectiveness of ISLE in helping students to achieve our intentionalities. We conclude with a call to curriculum developers and implementers to explicitly articulate their intentionalities and theoretical perspectives so that we may forge deeper connections between educational theories, curriculum development, and implementation.

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. [ABSTRACT FROM AUTHOR]

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.

Purpose: While many approaches in the field of unlearning aim at describing, understanding or explaining the "what" and/or "how" of unlearning, this paper aims to focus on the "where-to" and the goal of unlearning. In many cases, unlearning starts off with a specific result or goal in mind. This paper suggests that such an approach has to be challenged in the context of a highly complex and uncertain world and to introduce a mode of unlearning following a strategy of future-oriented open-endedness. Design/methodology/approach: This conceptual paper draws on (both theoretical/philosophical and empirical) interdisciplinary evidence from a wide variety of fields, such as organization studies, organizational (un)learning, systems theory, cognitive science and innovation studies. Findings: It turns out that open-endedness in unlearning processes plays a central role, especially if we are confronted with high levels of uncertainty and complexity. In such an environment, following a strategy of co-becoming with an unfolding environment and with an emergent goal seems to be more promising than aiming at a preconceived (un-)learning goal. Originality/value: The unlearning literature provides various approaches to what unlearning is and how it can be executed. However, understanding the actual goals and outcomes of unlearning and how these goals are identified and determined is a rather under-researched field. In many cases, they are preconceived in advance finding their realization in new forms of knowledge, assumptions, belief systems, values or routines. This paper challenges this strategy and addresses the gap of how it is possible to unlearn toward an uncertain future. This has an impact on the process of unlearning itself; it has to be reframed and understood as an open-ended strategy for identifying emerging future potentials, purposes and goals in a process of co-becoming with an unfolding future.

This paper comments on the theoretical formulations and usage of the construct of teacher noticing in a selection of the papers in this special issue of "ZDM Mathematics Education." The analysis of how the notion of teacher noticing is used in the papers suggests that it draws attention to several interdependencies involved that have not been attended to in the past. However, the contributions in this special issue have only partially accounted for the dynamic interactions in teacher noticing, suggesting that there is potential for enriching our understanding of the complexities involved in the realm of teacher noticing. The purpose of this commentary is to stimulate the current discussion on teacher noticing by providing insights from cognitive science and the applied science of human factors, which have the potential to challenge the current understanding of noticing. In doing so, the paper sets the stage for several related constructs from these research disciplines to raise awareness of aspects that recent conceptualizations of teacher noticing may have blinded rather than enlightened.

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

The purpose of this article is to offer insights into current understanding of digital learning environments (DLEs) from a neuroscientific perspective. Cognitive neuroscience methods are increasingly applied in educational research to examine the neural underpinnings of learning. As such, neuroscientific evidence can play an important role in advancing current knowledge base from the existing self-reported data and behavioural measures in the field of educational technology. In this paper, we focus our review of neuroscience research on DLEs that can potentially transform the way we view learning and instruction. We discuss recent empirical studies done on DLEs using common cognitive neuroscience methods which included eye tracking, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). We offer recommendations for future applications of neuroscience methods in behavioural research within DLEs.

The study of everyday numeric thinking in adults directs our attention to several aspects of number cognition that have received almost no attention in the experimental cognitive science literature, namely the influences of socially situated artifact use on numeric processing. The current studies explore numeral recognition and conceptualisation processes in business people who engage in different types of numeracy practices; orally based numeracy practices which involve very little use of written records compared to paper-based numeracy practices. Ethnographic observations of Lebanese business people were conducted to gain a detailed understanding of the socio-cognitive demands in orally-based paperless and paper-based business settings. These observations were in turn used to design experimental reaction time studies which investigated currency based numeral recognition and conceptualisation processes. The results of the numeral recognition and priming studies clearly illustrate that the use of artifacts in everyday numeracy practices influences numeral recognition and conceptualisation in a way that suggests tight linkages between the visio-spatial processes involve in recognizing numerals embedded in cultural artifacts and the semantically based processes involved in the conception of these numerals. The relevance of the current findings for the main models of adult numeric cognition and for research on everyday numeracy will be discussed. [ABSTRACT FROM AUTHOR]

This study explores the patterns of exchange of research knowledge among Education Research, Cognitive Science, and what we call "Border Fields." We analyze a set of 32,121 articles from 177 selected journals, drawn from five sample years between 1994 and 2014. We profile the references that those articles cite, and the papers that cite them. We characterize connections among the fields in sources indexed by Web of Science (WoS) (e.g., peer-reviewed journal articles and proceedings), and connections in sources that are not (e.g., conference talks, chapters, books, and reports). We note five findings—first, over time the percentage of Education Research papers that extensively cite Cognitive Science has increased, but the reverse is not true. Second, a high percentage of Border Field papers extensively cite and are cited by the other fields. Border Field authors' most cited papers overlap those most cited by Education Research and Cognitive Science. There are fewer commonalities between Educational research and Cognitive Science most cited papers. This is consistent with Border Fields being a bridge between fields. Third, over time the Border Fields have moved closer to Education Research than to Cognitive Science, and their publications increasingly cite, and are cited by, other Border Field publications. Fourth, Education Research is especially strongly represented in the literature published outside those WoS-indexed publications. Fifth, the rough patterns observed among these three fields when using a more restricted dataset drawn from the WoS are similar to those observed with the dataset lying outside the WoS, but Education Research shows a far heavier influence than would be indicated by looking at WoS records alone. [ABSTRACT FROM AUTHOR]

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel "assembled coherence (AC) theory" of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with "Timber," a computational model of conceptual change that revises its beliefs and generates human-like explanations in commonsense science. "Timber" represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. "Timber" then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low-cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in "Timber"; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use "Timber" to simulate the belief changes of students during clinical interviews about how the seasons change. We show that "Timber" produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory.

In a society where computers have become ubiquitous, it is necessary to develop a broader understanding of the nature of computing and programming, not just from a technical viewpoint but also from a historical and philosophical perspective. Computers and computer programs do not exist in a vacuum. Instead, they are a part of a rich socio-technological context that provides ways for understanding computers and reasoning about programs. This includes not only formal logic, mathematics, sciences, and technology but also cognitive sciences and sociology. The focus of this special issue is on questions that arise when we consider computing and programming in a wider context. In particular, the papers in this special issue explore the interplay between computing or programming and mathematics, formal logic, sciences, technology, and society. [ABSTRACT FROM AUTHOR]

All papers will be fully peer reviewed with publication of the full issue scheduled for later in the same year. Belief in conspiracy theories has become widespread, from COVID-19 and vaccinations to climate change and QAnon. [Extracted from the article]

In the paper, I Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non i - I floating vehicle i , Chen et al. ([4]) present the method of estimating non-floating vehicles' driving information according to floating vehicles' information. The authors presented the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy (EWVDFS) based on Weighted Vehicle Density Feedback Strategy (WVDFS). In the paper, A I robust lane detection method based on hyperbolic model i , Li et al. ([9]) investigated a robust lane detection method under structured roads. The paper I Road network i - I based region of interest mining and social relationship recommendation i by Tan and Zhang ([17]) proposed road context-based active region extraction algorithm (RAREA) which explores the method to extract the specific regions within the road network. [Extracted from the article]

Computational models of memory presented in this issue reflect varied empirical data and levels of representation. From mathematical models to neural and cognitive architectures, all aim to converge on a unified theory of the mind. [ABSTRACT FROM AUTHOR]

The case study focuses on the interactional mechanisms through which online collaborative teams co-construct a shared understanding of an analytical geometry problem by using dynamic geometry representations. The collaborative study consisted of an assignment on which the learners worked together in groups to solve a ship navigation problem as described in the Virtual Math Teams (VMT) environment. In this paper, a single group's problem solving activities were qualitatively analyzed to understand how they achieved a sense of joint perception of their shared workspace as a navigational chart which encodes the physical relationships between landmarks in the form of angle and distance measures. The analysis of the excerpts indicated that the initial struggles and their resolution in interaction as participants mastered the use of dynamic features in their shared workspace helped them gradually develop a shared understanding of the key spatial relationships among landmarks encoded in the 2D chart.

Cognitive science has converged in many ways with cognitive psychology, but while also maintaining a distinctive interdisciplinary nature. Here we further characterize this existing state of the field before proposing how it might be reconceptualized toward a broader and more distinct, and thus more stable, position in the realm of sciences., (© 2019 Cognitive Science Society, Inc.)