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The article addresses how to improve the professional training of specialists in the transport industry using information and cognitive technologies. The article aims to study and explain the problems, opportunities, and prospects of implementing and using information and cognitive technologies in the educational environment, as well as the specifics of their implementation for teaching and learning in educational and professional programs for the transport specialists’ training. The following methods of semantic and comparative analysis, classification, generalization, and systematization, as well as competency-based, technological, and creative development approaches were used in the research. It has been found that cognitive technologies in the context of teaching and learning involve the use of methods and tools, which are based on understanding and activating cognitive processes such as thinking, memory, and attention. In cognitive learning, the educational process focuses not on absorbing information but on understanding the internal connections of the studied subjects, which encourages students to engage in dialogical, exploratory thinking, and increases mental activity concentration. By complementing information technologies with cognitive ones, which take into account the individual and general cognitive students’ abilities, it is possible not only to encourage professional training but also to enhance the overall cognitive efficiency of future specialists. For educators, these technologies facilitate the use of ICT as cognitive tools for transitioning from traditional teacher-centred methods to constructivist student-centred approaches. It has been established that information-cognitive technologies combine methods and algorithms based on insights into the cognition processes, learning, communication, and information processing, grounded in the achievements of neuroscience, digital and information technologies, and the mathematical modelling of consciousness. In the educational process, they are designed to support students in setting and solving creative tasks that are difficult to formalize, as well as to reveal and effectively operate their cognitive potential. In the professional training of students, certain information-cognitive technologies can be applied both with and without the use of computer systems. These include technologies for developing critical thinking, creatively developmental technologies, project technologies, foresight methodologies, problem-based learning technologies, mastery learning technologies, mental activity activation technologies, case studies, gaming technologies, visualization and data representation technologies, and learning through practice. Additionally, the use of multimedia technologies, scribing, virtual and augmented reality, project technologies based on networking, gamification, interactive technologies, primarily group work technologies, open, distance, and blended learning technologies, and also the Internet of Things, Blockchain, Big Data, expert systems, SMART technologies, and artificial intelligence significantly enhances the quality of training students in proficiency transport area. It has been concluded that information and cognitive technologies provide interactive, personalized, and practically oriented learning, contribute to the development of student’s critical thinking, and for future transport specialists can optimize logistics processes, automate diagnostics, repair, and operation of vehicles, in addition, increase the specialist’s adaptability to the complex conditions of the modern and future professional environment.

Objective: The present study examines the cognitive effects of placing icons in unexpected spatial locations within websites. Background: Prior research has revealed evidence for cognitive conflict when web icons occur in unexpected locations (e.g., cart, top left), generally consistent with a dynamical systems models. Here, we compare the relative strength of evidence for both dual and dynamical systems models. Methods: Participants clicked on icons located in either expected (e.g., cart, top right) or unexpected (e.g., cart, top left) locations while mouse trajectories were continuously recorded. Trajectories were classified according to prototypes associated with each cognitive model. The dynamical systems model predicts curved trajectories, while the dual-systems model predicts straight and change of mind trajectories. Results: Trajectory classification revealed that curved trajectories increased (+11%), while straight and change of mind trajectories decreased (−12%) when target icons occurred in unexpected locations (p <.001). Conclusion: Rather than employing a single cognitive strategy, users shift from a primarily dual-systems to dynamical systems strategy when icons occur in unexpected locations. Application: Potential applications of this work include the assessment of cognitive impacts such as mental workload and cognitive conflict during real-time interaction with websites and other screen-based interfaces, personalization and adaptive interfaces based on an individual's cognitive strategy, and data-driven A/B testing of alternative interface designs. [ABSTRACT FROM AUTHOR]

Cognitive Linguistics, as an expanding discipline in language study, delves into language as a mental phenomenon, presenting a holistic approach that integrates linguistic and cognitive elements. Unlike traditional linguistics, which scrutinizes syntactical patterns, word structures, grammar rules, phonology, semantics, and lexical meanings, Cognitive Linguistics offers a comprehensive model that interweaves various closely connected theories. This study seeks to elucidate the manifestations of Cognitive Linguistics in language, providing scholars with a discerning analysis of existing theories. Furthermore, employing methods of descriptive and comparative analysis, this study aims to demonstrate the impact of Cognitive Linguistics on language processing and acquisition. It highlights its significance in second language acquisition while elucidating the underlying mental processes involved. The paper offers a succinct overview of this evolving discipline, encapsulating its unique characteristics within the encompassing term Cognitive Linguistics. [ABSTRACT FROM AUTHOR]

Humans are universally good in providing stable and accurate judgments about what forms part of their language and what not. Large Language Models (LMs) are claimed to possess human-like language abilities; hence, they are expected to emulate this behavior by providing both stable and accurate answers, when asked whether a string of words complies with or deviates from their next-word predictions. This work tests whether stability and accuracy are showcased by GPT-3/text-davinci- 002, GPT-3/text-davinci- 003, and ChatGPT, using a series of judgment tasks that tap on 8 linguistic phenomena: plural attraction, anaphora, center embedding, comparatives, intrusive resumption, negative polarity items, order of adjectives, and order of adverbs. For every phenomenon, 10 sentences (5 grammatical and 5 ungrammatical) are tested, each randomly repeated 10 times, totaling 800 elicited judgments per LM (total n = 2,400). Our results reveal variable above-chance accuracy in the grammatical condition, below-chance accuracy in the ungrammatical condition, a significant instability of answers across phenomena, and a yes-response bias for all the tested LMs. Furthermore, we found no evidence that repetition aids the Models to converge on a processing strategy that culminates in stable answers, either accurate or inaccurate. We demonstrate that the LMs’ performance in identifying (un)grammatical word patterns is in stark contrast to what is observed in humans (n = 80, tested on the same tasks) and argue that adopting LMs as theories of human language is not motivated at their current stage of development. [ABSTRACT FROM AUTHOR]

With this manuscript we aim to initiate a discussion specific to educational actions around ultrasonics sonochemistry. The importance of these actions does not just derive from a mere pedagogical significance, but they can be an exceptional tool for illustrating various concepts in other disciplines, such as process intensification and microfluidics. Sonochemistry is currently a far-reaching discipline extending across different scales of applicability, from the fundamental physics of tiny bubbles and molecules, up to process plants. This review is part of a special issue in Ultrasonics Sonochemistry, where several scholars have shared their experiences and highlighted opportunities regarding ultrasound as an education tool. The main outcome of our work is that teaching and mentorship in sonochemistry are highly needed, with a balanced technical and scientific knowledge to foster skills and implement safe protocols. Applied research typically features the use of ultrasound as ancillary, to merely enhance a given process and often leading to poorly conceived experiments and misunderstanding of the actual effects. Thus, our scientific community must build a consistent culture and monitor reproducible practices to rigorously generate new knowledge on sonochemistry. These practices can be implemented in teaching sonochemistry in classrooms and research laboratories. We highlight ways to collectively provide a potentially better training for scientists, invigorating academic and industry-oriented careers. A salient benefit for education efforts is that sonochemistry-based projects can serve multidisciplinary training, potentially gathering students from different disciplines, such as physics, chemistry and bioengineering. Herein, we discuss challenges, opportunities, and future avenues to assist in designing courses and research programs based on sonochemistry. Additionally, we suggest simple experiments suitable for teaching basic physicochemical principles at the undergraduatelevel. We also provide arguments and recommendations oriented towards graduate and postdoctoral students, in academia or industry to be more entrepreneurial. We have identified that sonochemistry is consistently seen as a ‘green’ or sustainable tool, which particular appeal to process intensification approaches, including microfluidics and materials science. We conclude that a globally aligned pedagogical initiative and constantly updated educational tools will help to sustain a virtuous cycle in STEM and industrial applications of sonochemistry.

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The construction of cognitive models is the basis for cognitive diagnosis, and the cognitive models will change based on the purpose of the study. According to the purpose of mathematical education, the cognitive factorization model is constructed based on the competence and knowledge dimensions. The factorization cognitive model was preliminarily constructed using expert-defined and literature surveys, and a small-scale test was subsequently carried out. The rationality of the cognitive model was tested through verbal reports and the regression of the item's difficulty through the cognitive attributes. The study included a sample of 72 students from two eighth-grade classes in a junior high school located in Wuhan. A diagnosis was made based on the mastery of factorization knowledge and the level of mathematical operation ability of the eighth graders in the cognitive model. Research 1 demonstrates that the construction of the cognitive factorization model is reasonable. Research 2 shows that approximately 79% of students' mathematics operation ability can reach the level of knowledge understanding, 71% of students can reach the level of knowledge transfer, and only 28% of students can reach the level of knowledge innovation. [ABSTRACT FROM AUTHOR]

Background: This review provides an overview of obsessive‐compulsive disorder (OCD) symptoms, including the four partially distinct subtypes of the disorder, current diagnostic criteria, and common comorbidities. Critically, it focuses on the etiology of OCD, including its underlying neuropathology, and examines cognitive dysfunction in OCD. Methods: This review study was conducted by library method. Results: We show how dysfunction in cortico‐striato‐thalamo‐cortical (CSTC) circuits may underpin symptoms; and shed light on the putative neurochemistry within these loops such as the role of serotonin, dopamine, and glutamate systems. We also show how OCD is characterized by cognitive dysfunction including problems in cognitive flexibility, visuospatial memory, response inhibition, and goal‐directed behavior, linked to aberrant activity within CSTC circuits. Conclusions: In brief, research questions we shed light on include (1) what are the symptoms in OCD; (2) what is the etiology of the disorder and do existing models explain OCD; and (3) what are key cognitive deficits in OCD and do these improve with treatment? [ABSTRACT FROM AUTHOR]

This study aims to determine the effectiveness of the application of the project-based learning model to football learning outcomes. The research method used is a True experiment with a randomized posttest-only control group design. The research sample was 60 grade IX students of SMPN 4 Ciawigebang, Kuningan Regency, divided into two groups an experimental class of 30 students and 30 students for a control group. The sampling technique uses random cluster sampling. Research instruments to measure student football learning outcomes with passing-stoping tests (skills), attitude scales (affective), and knowledge tests in the form of questions (cognitive). The data analysis technique used is the independent sample test. The results showed that the application of the project-based learning model positively influenced student football learning outcomes in both psychomotor, affective, and cognitive aspects.

Abstract This paper explored entrepreneurial intentions in the Caribbean adult population using a social cognitive approach. It used the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS), which includes questions about entrepreneurial intentions of potential business owners and entrepreneurial perceptions, namely individual, entrepreneurial opportunities and socio-cultural, along with demographic and socio-economic variables. The effect of perceptions along with socio-economic control variables on entrepreneurial intentions was investigated using probit regression models. The results confirm that even after controlling for demographic and socio-economic and country fixed effects, social cognitive perceptions were indeed relevant in explaining entrepreneurial intentions in the region. This study therefore provides insights into understanding entrepreneurship in particular the decision to form a new business through the individual’s perceptions and intentions.

Abstract Background This review provides an overview of obsessive‐compulsive disorder (OCD) symptoms, including the four partially distinct subtypes of the disorder, current diagnostic criteria, and common comorbidities. Critically, it focuses on the etiology of OCD, including its underlying neuropathology, and examines cognitive dysfunction in OCD. Methods This review study was conducted by library method. Results We show how dysfunction in cortico‐striato‐thalamo‐cortical (CSTC) circuits may underpin symptoms; and shed light on the putative neurochemistry within these loops such as the role of serotonin, dopamine, and glutamate systems. We also show how OCD is characterized by cognitive dysfunction including problems in cognitive flexibility, visuospatial memory, response inhibition, and goal‐directed behavior, linked to aberrant activity within CSTC circuits. Conclusions In brief, research questions we shed light on include (1) what are the symptoms in OCD; (2) what is the etiology of the disorder and do existing models explain OCD; and (3) what are key cognitive deficits in OCD and do these improve with treatment?

Given identical informational content, the order in which youreceive spatial information may heavily influence the correct-ness of your mental representation. This can reveal importantinsights into the specifics of human spatial cognition and theway we integrate information. Despite its importance in ev-eryday life, its causes and the mental processes involved stillremain an open question. Most cognitive models so far havefocused on modeling only answer distributions or just the mostfrequent answer given by all participants.In this paper we take a rather radical approach: We turn tothe individual spatial reasoner and focus our analyses on thestream of spatial information and related reaction times, i.e.,how the spatial information is represented and cognitively pro-cessed. By spanning a space of 243 cognitive spatial models,some of which outperform the current state-of-the art models,it is possible to test the goodness of general principles under-lying such models.

The present article engages with multidimensional analysis of autointertextuality, putting forward a cognitive approach based on V. Evans' Cognitive Model and Lexical Concepts (LCCM) Theory. Taking into consideration a variety of viewpoints introduced in the current article, reference is made to traditional and most recent approaches provided by both Russian and European scholars for the purpose of analyzing intertextuality from the cognitive point of view, putting the imperative on the mental processes occurring in the literary text. The current article explores Roald Dahl's perception of the concept of "crime" through the prism of cognitive intertextuality. The subject matter of the article is the comprehensive analysis of the techniques employed in the literary text which can serve as prerequisites for the study of autointertextual relations. The topicality is determined by the current tendency of cognitive linguistics to interpret the literary text through the lens of semantic intertextual frames. Eventually, our research is devoted to the study of the concept of "crime" in R. Dahl's stories in the view of intertextual domain with reference to autointertextuality. [ABSTRACT FROM AUTHOR]

Background and hypothesis A plausible cause of distress for voice hearers is listening to and believing the threats and criticisms heard. Qualitative research indicates that patients have understandable reasons to listen. This study aimed to develop the understanding of distress using this listening and believing framework. Measures were developed of listening and believing voices and the reasons, and associations with distress tested. Study design A cross-sectional study of patients hearing derogatory and threatening voices (N  = 591). Listening and Believing–Assessment and Listening and Believing–Reasons item pools were completed, and assessments of distress. Exploratory and confirmatory factor analyses and structural equation modeling (SEM) were conducted. Study results 52% (n  = 307) of participants believed their voices most or all the time. Listening and believing had 4 factors: active listening, passive listening, believing, and disregarding. Higher levels of believing, active listening, and particularly passive listening were associated with higher levels of anxiety, depression, and voice distress. Reasons for listening and believing formed 7 factors: to better understand the threat; being too worn down to resist; to learn something insightful; being alone with time to listen; voices trying to capture attention; voices sounding like real people; and voices sounding like known people. Each type of reason was associated with active listening, passive listening, and believing. SEM showed that feeling worn down in particular accounted for listening and believing. Test–retest reliability of measures was excellent. Conclusions A framework of listening and believing negative voices has the potential to inform the understanding and treatment of voice distress. [ABSTRACT FROM AUTHOR]

This paper explored entrepreneurial intentions in the Caribbean adult population using a social cognitive approach. It used the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS), which includes questions about entrepreneurial intentions of potential business owners and entrepreneurial perceptions, namely individual, entrepreneurial opportunities and socio-cultural, along with demographic and socio-economic variables. The effect of perceptions along with socio-economic control variables on entrepreneurial intentions was investigated using probit regression models. The results confirm that even after controlling for demographic and socio-economic and country fixed effects, social cognitive perceptions were indeed relevant in explaining entrepreneurial intentions in the region. This study therefore provides insights into understanding entrepreneurship in particular the decision to form a new business through the individual's perceptions and intentions. [ABSTRACT FROM AUTHOR]

The present article reviews the monograph Representation of Food in Media Discourses: Cognitive and Pragmatic Aspects by V. Shevchenko and S. Tomascikova, which is devoted to investigating the process of cognitive models realization referring to food representation in American, British and Russian media discourses. The undertaken comparative analysis has demonstrated that the journalists tend to foreground the specific constituents of the cognitive models in accordance with their pragmatic goals. The author comes to a conclusion that the published monograph is a significant investigation of the essential problem of cognitive models and it contains important theoretical and practical results which can be used by teaching staff, graduate and postgraduate students in their scientific and educational activities.

In this paper, we propose a robotic cognitive system which can learn itself to do a specific assignment by accumulating experiences through bottom-up thinking to make decision by itself via top-down thinking according to the experiences. That is, the cognitive system has a self-learning ability which can accumulate its experiences to make itself smarter. In essence, the cognitive system possesses a perception model, a memory model, and a hypothesis model. The perception model converts image information into perception codes. The memory model stores experiences in the past and present to provide to the perception model and the hypothesis model. The hypothesis model, which generates the next decision according to the experiences from the memory model, is the most important part of the proposed cognitive system. To validate the performance of the proposed system, we utilize Chinese calligraphy writing tasks by a virtual robot through simulation to evaluate the abilities of the cognitive system. In order to generate the coordinates of the writing brush, we made the virtual robot practice to learn Chinese calligraphy through bottom-up thinking to construct the writing patterns. The illustrative examples in this paper show that the virtual robot can learn to write Chinese calligraphy by top-down thinking according to its own experiences.

To improve cyber defense, researchers have developedalgorithms to allocate limited defense resources optimally.Through signaling theory, we have learned that it is possible totrick the human mind when using deceptive signals. Thepresent work is an initial step towards developing apsychological theory of cyber deception. We use simulationsto investigate how humans might make decisions under variousconditions of deceptive signals in cyber-attack scenarios. Wecreated an Instance-Based Learning (IBL) model of theattacker decisions using the ACT-R cognitive architecture. Weran simulations against the optimal deceptive signalingalgorithm and against four alternative deceptive signalschemes. Our results show that the optimal deceptive algorithmis more effective at reducing the probability of attack andprotecting assets compared to other signaling conditions, but itis not perfect. These results shed some light on the expectedeffectiveness of deceptive signals for defense. The implicationsof these findings are discussed.

Categorization has a large impact on how people perceive theworld, especially when used to make inferences about uncer-tain features of new objects. While making these inferences,people tend to draw information from only one possible cate-gorization of a new object; in addition, people are sensitive topre-existing correlations between features. Here, we explainthese trends of feature inference using a priming-based cogni-tive process model, and show that our model is distinguishedin that it can explain not only these two main trends, but alsocases where people seem to reverse the first trend and base in-ferences on information from multiple categories.

We describe two different cognitive process models of a wellknown experiment on social influence (Salganik, Dodds, &Watts, 2006). One model, the social influence model,reproduced the choices that participants took by modelingboth the cognitive processes the participant engaged in andthe social influences that the participant saw. The secondmodel, the pure cognitive model, used only cognitivecapabilities and did not model any social influences that theparticipant saw. Somewhat surprisingly, the two modelsshowed no difference in quality of fit (the pure cognitivemodel actually fit slightly better than the social influencemodel), suggesting that social influence models should takecognitive functions into account in their theories.

The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labeling, network training, and fine-tuning of models. Sharing and reuse of these elaborated resources between intelligent systems of different environments, which is known as transfer learning, would facilitate the adoption of cognitive services for the users and accelerate the uptake of intelligent systems in smart building and smart city applications. Currently, machine learning processes are commonly built for intra-organization purposes and tailored towards specific use cases with the assumption of integrated model repositories and feature pools. Transferring such services and models beyond organization boundaries is a challenging task that requires human intervention to find the matching models and evaluate them. This paper investigates the potential of communication and transfer learning between smart environments in order to empower a decentralized and peer-to-peer ecosystem for seamless and automatic transfer of services and machine learning models. To this end, we explore different knowledge types in the context of smart built environments and propose a collaboration framework based on knowledge graph principles for describing the machine learning models and their corresponding dependencies.

E-learning is increasingly used to support student learning in higher education. This results in huge amounts of item response data containing valuable information about students’ strengths and weaknesses that can be used to provide effective feedback to both students and teachers. However, in current practice, feedback in e-learning is often given in the form of a simple proportion of correctly solved items rather than diagnostic, actionable feedback. Diagnostic classification models (DCMs) provide opportunities to model the item response data from formative assessments in online learning environments and to obtain diagnostic information to improve teaching and learning. This simulation study explores the demands on the data structure (i.e., assessment length, respondent sample size) to apply log-linear DCMs to empirical data. Thereby we provide guidance to educational practitioners on how many items need to be administered to how many students in order to accurately assess skills at different levels of specificity using DCMs. In addition, effects of misspecification of the dimensionality of the assessed skills on model fit indices are explored. Results show that detecting these misspecifications statistically with DCMs can be problematic. Recommendations and implications for educational practice are discussed.

Given the global challenges of security, both in physical andcyber worlds, security agencies must optimize the use of theirlimited resources. To that end, many security agencies havebegun to use "security game" algorithms, which optimally plandefender allocations, using models of adversary behavior thathave originated in behavioral game theory. To advance ourunderstanding of adversary behavior, this paper presentsresults from a study involving an opportunistic crime securitygame (OSG), where human participants play as opportunisticadversaries against an algorithm that optimizes defenderallocations. In contrast with previous work which oftenassumes homogeneous adversarial behavior, our workdemonstrates that participants are naturally grouped intomultiple distinct categories that share similar behaviors. Wecapture the observed adversarial behaviors in a set of diversemodels from different research traditions, behavioral gametheory, and Cognitive Science, illustrating the need forheterogeneity in adversarial models.

The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines. [ABSTRACT FROM AUTHOR]

The article considers English neologisms from the position of cognitive approach, which involves identifying knowledge domains represented by such words, describing the principles that ensure the creation of new knowledge based on existing ones. In addition, the author analyzes some cognitive mechanisms for creating neologisms that arise as a result of reinterpretation. The study was performed on the material of new language units selected on sites dedicated to English neologisms, using a continuous sampling method. The author uses a set of methods, the main of which are methods of conceptual and definition analysis and cognitive modeling. As a result of the research, it was found that neologisms represent knowledge domains that traditionally arouse interest (politics, economics, and the field of consumption). At the same time, there are many neologisms that reflect knowledge about computer and digital technologies and various types of human interaction with the virtual world. It is determined that neologisms that appear as a result of reinterpretation are created by cognitive mechanisms of “conceptual metaphor”, “conceptual metonymy”, “conceptual metaphtonymy”, and “concretization”. Neologisms are formed by the following metaphorical cognitive models: HUMAN BEING – ARTEFACT, ARTEFACT – ANIMAL, ARTEFACT – ARTEFACT, ABSTRACT – PHYSICAL, VIRTUAL – REAL, ANIMAL – ANIMAL, and HUMAN BEING – ANIMAL, which are used both when reinterpreting simple and compound words. The principles that determine the cognitive processes of creating neologisms that represent knowledge based on existing language structures are also described: principle of economy, principle of vividness and subjectivity, principle of using new interpretive realities, principle of remotivation.

This paper gives an overview of the theoretical underpinnings and current work in Critical Discourse Analysis (CDA). It defines CDA as a transdisciplinary, text-analytical approach to critical social research, aimed at revealing the power imbalance reflected in the use of language and patterns of dominance imposed through the use of language. Describing the most important schools and models in CDA, the paper demonstrates how critical approaches draw on recent developments in different areas of linguistics, such as pragmatics, cognitive linguistics and corpus studies. At the same time, it shows how the interdisciplinary research agenda of CDA attracts the ‘classic’ theories and tools of linguistics to new empirical territories in political/public discourse. The final part of the paper illustrates the explanatory power the legitimization-proximization model in CDA in a case study of the discourse of the war-on-terror.

An effective network perspective focused on measuring directional interactions of electroencephalographic in different cortical regions during a sustained attentive task requiring vigilance. A novel measure referred to as dynamic partial directed coherence was used to map the cognitive state of vigilance based on graph theory. In the right parieto-occipital area, the area is significantly higher than in other regions of interest (the areas are 0.601 and 0.632 for out-degree and in-degree, respectively). A similar analysis in the right fronto-central area revealed significant differences in the different cognitive states. Across the six regions of interest, significant differences of in-degree and out-degree based alpha band are observed in the right fronto-central and the right parieto-occipital (P < 0.05). The performance was compared with those from a support vector machine using different network-based phase-locking values, partial directed coherence, and dynamic partial directed coherence. Results show that dynamic partial directed coherence can provide more information about direction (compared with phase-locking values) and accuracy (when compared with partial directed coherence). The graph theoretical analysis shows that the effective network based dynamic partial directed coherence has a small-world property for synchronizing neural activity between brain regions. Moreover, the alpha band is well correlated with the cognitive state compared to other frequency bands.

In the last few decades, cognitive theories for explaining human spatial relational reasoning have increased. Few of these theories have been implemented as computational models, however, even fewer have been compared computationally to each other. A computational model comparison requires, among other things, a still missing quantitative benchmark of core spatial relational reasoning problems. By presenting a new evaluation approach, this paper addresses: (1) developing a benchmark including raw data of participants, (2) reimplementation, adaptation, and extension of existing cognitive models to predict individual responses, and (3) a thorough evaluation of the cognitive models on the benchmark data. The paper shifts the research focus of cognitive modeling from reproducing aggregated response patterns toward assessing the predictive power of models for the individual reasoner. It demonstrate that not all psychological effects can discern theories. We discuss implications for modeling spatial relational reasoning. [ABSTRACT FROM AUTHOR]

We present a computational cognitive model of mind wandering,an important cognitive phenomenon whose mechanismsare involved in insight, problem-solving, and creativity. Themodel posits that mind wandering begins when one is notengaged in goal-oriented cognition, whether when betweentasks or when in the middle of a task but not actively thinkingabout one’s goal. At such times, the model thinks about other,highly-activated thoughts in memory. This model sheds lighton both how task-oriented and more basic cognitive processesinteract, as well as how mind wandering content is generated;both unresolved questions for mind wandering research. Wecompare our model against data presented by McVay and Kane(2013), who induced mind wandering in a laboratory setting byembedding participants’ personal goals and concerns in a lexicalSART task. Overall, our model matched the data’s mindwandering rates very well. We discuss implications and futurework on the model.

Associative learning is an important part of human cognition,and is thought to play key role in list learning. We present herean account of associative learning that learns asymmetric itemto-item associations, strengthening or weakening associationsover time with repeated exposures. This account, combinedwith an existing account of activation strengthening and decay,predicts the complicated results of a multi-trial free and serialrecall task, including asymmetric contiguity effects thatstrengthen over time (Klein, Addis, & Kahana, 2005).

In the last few decades, cognitive theories for explaining human spatial relational reasoning have increased. Few of these theories have been implemented as computational models, however, even fewer have been compared computationally to each other. A computational model comparison requires, among other things, a still missing quantitative benchmark of core spatial relational reasoning problems. By presenting a new evaluation approach, this paper addresses: (1) developing a benchmark including raw data of participants, (2) reimplementation, adaptation, and extension of existing cognitive models to predict individual responses, and (3) a thorough evaluation of the cognitive models on the benchmark data. The paper shifts the research focus of cognitive modeling from reproducing aggregated response patterns toward assessing the predictive power of models for the individual reasoner. It demonstrate that not all psychological effects can discern theories. We discuss implications for modeling spatial relational reasoning.

Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations: the Euler method, and two methods we have developed and present here: a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods.

Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations: the Euler method, and two methods we have developed and present here: a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods. [ABSTRACT FROM AUTHOR]

• Sonochemistry in education builds valuable physico-chemical fundamentals. • Sonochemistry case studies reinforce process intensification knowledge. • Sonochemical case studies aid in building academic and industry-oriented careers. • A global initiative can sustain a virtuous cycle in STEM and entrepreneurship. • Sonochemistry supports STEM outcomes with added entrepreneurial skills. With this manuscript we aim to initiate a discussion specific to educational actions around ultrasonics sonochemistry. The importance of these actions does not just derive from a mere pedagogical significance, but they can be an exceptional tool for illustrating various concepts in other disciplines, such as process intensification and microfluidics. Sonochemistry is currently a far-reaching discipline extending across different scales of applicability, from the fundamental physics of tiny bubbles and molecules, up to process plants. This review is part of a special issue in Ultrasonics Sonochemistry , where several scholars have shared their experiences and highlighted opportunities regarding ultrasound as an education tool. The main outcome of our work is that teaching and mentorship in sonochemistry are highly needed, with a balanced technical and scientific knowledge to foster skills and implement safe protocols. Applied research typically features the use of ultrasound as ancillary, to merely enhance a given process and often leading to poorly conceived experiments and misunderstanding of the actual effects. Thus, our scientific community must build a consistent culture and monitor reproducible practices to rigorously generate new knowledge on sonochemistry. These practices can be implemented in teaching sonochemistry in classrooms and research laboratories. We highlight ways to collectively provide a potentially better training for scientists, invigorating academic and industry-oriented careers. A salient benefit for education efforts is that sonochemistry-based projects can serve multidisciplinary training, potentially gathering students from different disciplines, such as physics, chemistry and bioengineering. Herein, we discuss challenges, opportunities, and future avenues to assist in designing courses and research programs based on sonochemistry. Additionally, we suggest simple experiments suitable for teaching basic physicochemical principles at the undergraduatelevel. We also provide arguments and recommendations oriented towards graduate and postdoctoral students, in academia or industry to be more entrepreneurial. We have identified that sonochemistry is consistently seen as a 'green' or sustainable tool, which particular appeal to process intensification approaches, including microfluidics and materials science. We conclude that a globally aligned pedagogical initiative and constantly updated educational tools will help to sustain a virtuous cycle in STEM and industrial applications of sonochemistry. [ABSTRACT FROM AUTHOR]

Existing cognitive models of narrative creation provide accounts for story invention that, while useful, are too high level to be directly applied to formal systems like computational models of narrative generation. Inversely, existing automatic story generation systems that try to implement cognitive models can only rely on approximations to the general concepts these models describe. In order to provide insight to fill the gap between these two approaches, we have conducted a study in which human participants would invent and write short stories while reflecting on their thoughts out loud. The sessions and the analysis of the recordings was designed so that we could observe which specific modifications the participants apply to their story drafts, with the intention to inform the process of creating computational systems based on cognitive descriptions of the narrative creation process. After running the experiments, annotating the videos and analysing the output, we have concluded that there are a number of common modifications that humans tend to apply to a newly created draft, and that this information can be used to the development of storytelling systems.

Linguistic and cultural research of concepts of emotional states and values finds its place among the most frequently chosen research directions in contemporary linguistics. Such popularity could be determined, on the one hand, by the desire to penetrate the complicated mental structure of human consciousness and its cognitive and cultural determinants. On the other hand, it is due to the belief in close links between language, human cognitive abilities and culture, resulting from cognitive sciences, as well as treating the research on linguistic structures and semantics of language as a tool for reaching the way of understanding and evaluating the world (cf. Bartmiński 2007; Geeraerts 2006, Evans, Green 2006). This conviction is of particular importance in the study of the lexicalization of objects and abstracts invisible to the naked eye, including emotional states. One of the main goals of the analysis of the names of emotions is revealing the way of understanding and evaluating the underlying emotional states and the experiences associated with them. In order to achieve this goal, conceptual representations of individual categories of feelings are reconstructed on the basis of the American cognitive models proposed by cognitive scientists or the base image profiles proposed by J. Bartmiński. The aim of this article is to present cognitive models and lexical units related to them, which constitute a specific borderline of emotional states of SHAME and FEAR, e. g. shame as fear of rejection, fear as a consequence of shame, etc. Thus, these are cases of linguistically certified transmissions of certain aspects of the feeling of shame and fear and their mutual convergence. The analysis presents a linguistic image of the areas of emotional experiences that constitute the periphery of the category of feelings from the family of SHAME, but not yet included in the category of feelings of FEAR in its basic sense.

We present a novel Bayesian approach to semiotic dynamics, which is a cognitive analog of the naming game model restricted to two conventions. The model introduced in this paper provides a general framework for studying the combined effects of cognitive and social dynamics. The one-shot learning that characterizes the agent dynamics in the basic naming game is replaced by a word-learning process in which agents learn a new word by generalizing from the evidence garnered through pairwise-interactions with other agents. The principle underlying the model is that agents—like humans—can learn from a few positive examples and that such a process is modeled in a Bayesian probabilistic framework. We show that the model presents some analogies with the basic two-convention naming game model but also some relevant differences in the dynamics, which we explain through a geometric analysis of the mean-field equations.

The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phases of the training. In this study, the Digital Twin is proposed as a framework for helping athletes, during realization of training sessions, to make the proper decisions in situations they encounter. The digital twin for artificial sport trainer is based on the cognitive model of humans. This concept has been applied to cycling, where a version of the system on a Raspberry Pi already exists. The results of porting the digital twin on the mentioned platform shows promising potential for its extension to other sport disciplines.

Critical conservation issues such as bushmeat hunting, which exist in complex social, political, and policy landscapes, require the incorporation of diverse sources of knowledge as a key aspect of decision making. We demonstrate the utilization of both individual and collective stakeholder knowledge to contribute toward decision making. We used fuzzy cognitive mapping in a twostage process to investigate bushmeat hunting and consumption in Malawi as a case study, and arrived at models of the bushmeat hunting and consumption systems in the form of cognitive maps. We also explored the effect of three different intervention scenarios, namely wildlife farming, microenterprise initiatives, and ecotourism. We found that the concept of hunting was perceived as more complex than consumption, and that poverty, human population, and political will were shared as important drivers of both issues. The two-stage process we used indicated that individual and group phases were equally important. Key concepts were drawn out during the individual elicitation stage, while the participatory group phase allowed nuanced understanding of many of these concepts. We found that wildlife farming was predicted to be the most effective scenario for meeting many of the key state outcomes for both hunting and consumption. These results provide an example of using fuzzy cognitive mapping in a multistage process and illustrate its utility for arriving at decisions regarding interventions in complex social-ecological systems. [ABSTRACT FROM AUTHOR]

AVATAR therapy represents an effective new way of working with distressing voices based on face-to-face dialogue between the person and a digital representation (avatar) of their persecutory voice. To date, there has been no complete account of AVATAR therapy delivery. This article presents, for the first time, the full range of therapeutic targets along with information on acceptability and potential side effects. Interest in the approach is growing rapidly and this report acts as a necessary touchstone for future development. [ABSTRACT FROM AUTHOR]

This paper presents a cognitive conversational agent for use in teaching and learning processes named THOTH (Training by Highly Ontologyoriented Tutoring Host) that is capable of formulating and enunciating a welldefined set of small talk segments in a Q&A (Question and Answer) interaction. The small talk structures are placed within the tutoring conversation by an agent designed as a cognitive assistant, in order to make communication smoother and less formal, presenting a more "concerned" behavior. Twelve small talk segments are suggested, included in conversation stages such as opening and closing the conversation, maintaining the rhythm and managing learning. We also explore some branches of the theoretical assumptions and concepts grounding THOTH, such as Dennett's intentional stance, Bloom's taxonomy and microlearning theory. In order to measure the perception and effects of using THOTH, we performed a quantitative and qualitative study with a group of students from a course in Applied Artificial Intelligence over one semester. The outcomes are classified into two main categories of analysis - interactivity and intentionality - informing the discussion on the potential uses of a small talk agent as a valuable resource in tutoring interaction, and also raising some points for improvement. In addition to this study, we also drew a small talk profile for this group of students revealing what structures and topics they use the most, as well as a partial performance analysis that allows identifying some effects on learning. [ABSTRACT FROM AUTHOR]