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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]

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.

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.

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]

"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.)

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.

[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.

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.

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.

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 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.

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]

What is the most prototypical form of African literature? Shouldn't we be using African languages to produce African literary texts, shouldn't we produce more Afriphone African literature compared to Europhone African literature or Afro-Europhone literature? This issue underlies the reality that the vast majority of African writers presumably think in one language and express themselves (speak, enchant, or write) in another. This problematic, crystalized in major debates between Ngugi wa Thiongo and others, on the one hand, and Chinua Achebe and others, on the other hand, has resulted in great challenges as to how we can define or even conceptualize the discipline of African literature. Is it literature written by Africans in African languages for Africans or is it literature written by anybody including nonAfricans in non-African languages? Or is it somewhere in-between these two extremes? The paper discusses several positions on this major question in African literature before advancing a novel proposal based on insights and evidence from proto-type theory within Linguistics and the Cognitive Sciences. This proposal leads to a somewhat provocative conclusion about the gradation of African literatures, where "African language literatures" or Afrophone literatures, comprise the core, proto-typical category in a 21st Century African literature constellation, whereas foreign language and diasporic literatures such as "Afro-European literatures," "Afro-American literatures," and "Afro-Chinese literatures" are the hybrid and thus more recessive, peripheral types of African literature.

This paper critically examines the application of research into cognitive neuroscience to educational contexts. It first considers recent warnings from within the neuroscientific community itself about the limitations of current neuroscientific knowledge and the urgent need to dispel popular "neuromyths" which have become accepted in many classrooms. It also criticises the use of over-simplified neuroscience to add scientific credibility to curricular reform, as has been the case in the rationale behind the recent implementation of the Northern Ireland Revised Curriculum. The paper then draws on the philosophy of Wittgenstein to highlight a further conceptual confusion which often surrounds the application of neuroscience to education.