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The article presents a synopsis on group-format dual-task training which reduces dual-task interference in mobility tasks in people with chronic stroke. Topics discussed include potential advantages in terms of increasing social interactions, improving adherence, and reducing costs; simultaneous performance practice of walking and concurrent tasks across numerous participants; and reductions in dual-task interference effects were observed only in the group receiving dual-task training.

• Interaction feature pair (IFP) is introduced as carriers of interaction intents. • A product information model is established for interaction intent transmission. • Behaviors of cognizing interaction structures are achieved based on IFP matching. • Parts are endowed with the cognition behavior to mate with each other autonomously. Interactions of a part with other parts in an assembly constitute the behavior of the part. However, currently parts interact with each other lifelessly in assembly environment due to their incapability of cognizing interaction structures between them, making the assembly modeling of complex products suffer from considerable manual interventions. An intelligent assembly modeling method is proposed for complex products in this paper, by endowing parts with the ability of cognizing interaction structures between them. In this method, the concept of interaction feature pair (IFP) and the corresponding automatic construction algorithms are developed to explicitly describe interaction intent (i.e., how a part will mate with other parts) in designer's mind, providing carriers for part cognition behaviors and dynamical interaction intent in the assembly modeling process. On this basis, an object-oriented product information model considering IFP is established to facilitate the complete capture of interaction intent at part modeling stage and the consistent transmission of it to assembly modeling stage, endowing parts with the cognition ability of foreseeing how to mate with other parts before and after loading into assembly modeling environment. Then, IFP matching algorithms are designed to support the part cognition behaviors of actively cognizing interaction structures, perceiving potential mating parts and mating with other parts with high efficiency. Assembly modeling case studies of a reducer and a Mecanum wheel with few manual interventions indicate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Objectives: To synthesize the findings of cognitive interview and usability studies performed to assess the measurement equivalence of patient-reported outcome (PRO) instruments migrated from paper to electronic formats (ePRO), and make recommendations regarding future migration validation requirements and ePRO design best practice.Methods: We synthesized findings from all cognitive interview and usability studies performed by a contract research organization between 2012 and 2015: 53 studies comprising 68 unique instruments and 101 instrument evaluations. We summarized study findings to make recommendations for best practice and future validation requirements.Results: Five studies (9%) identified minor findings during cognitive interview that may possibly affect instrument measurement properties. All findings could be addressed by application of ePRO best practice, such as eliminating scrolling, ensuring appropriate font size, ensuring suitable thickness of visual analogue scale lines, and providing suitable instructions. Similarly, regarding solution usability, 49 of the 53 studies (92%) recommended no changes in display clarity, navigation, operation, and completion without help. Reported usability findings could be eliminated by following good product design such as the size, location, and responsiveness of navigation buttons.Conclusions: With the benefit of accumulating evidence, it is possible to relax the need to routinely conduct cognitive interview and usability studies when implementing minor changes during instrument migration. Application of design best practice and selecting vendor solutions with good user interface and user experience properties that have been assessed in a representative group may enable many instrument migrations to be accepted without formal validation studies by instead conducting a structured expert screen review. [ABSTRACT FROM AUTHOR]

Objectives: A new patient-reported outcome (PRO) instrument to measure fatigue symptoms and impacts in relapsing multiple sclerosis (RMS) was developed in a qualitative stage, followed by psychometric validation and migration from paper to an electronic format.Methods: Adult patients with relapsing-remitting multiple sclerosis (RRMS) were interviewed to elicit fatigue-related symptoms and impacts. A draft questionnaire was debriefed in cognitive interviews with further RRMS patients, and revised. Content confirmation interviews were conducted with patients with progressive-relapsing multiple sclerosis (PRMS) and relapsing secondary-progressive multiple sclerosis (RSPMS). Psychometric analyses used data from adult patients with different RMS subtypes and matched non-RMS controls in a multicenter, observational study. After item reduction, the final instrument was migrated to a smartphone (eDiary) and usability was confirmed in interviews with additional adult RMS patients.Results: The qualitative stage included 37 RRMS, 5 PRMS, and 5 RSPMS patients. Saturation of concepts was reached during concept elicitation. Cognitive interviews confirmed that participants understood the instructions, items, and response options of the instrument-named FSIQ-RMS-as intended. Psychometric validation included 164 RMS and 74 control patients. Internal consistency and test-retest reliability were demonstrated. The symptoms domain discriminated along the RMS symptom-severity continuum and between patients and controls. Patients were able to attribute fatigue-related symptoms to RMS. Usability and conceptual equivalence of the eDiary were confirmed (n = 10 participants).Conclusions: With 7 symptom items and 13 impact items (in 3 impacts subdomains: physical, cognitive and emotional, and coping) after item reduction, the FSIQ-RMS is a comprehensive, valid, and reliable measure of fatigue-related symptoms and impacts in RMS patients. [ABSTRACT FROM AUTHOR]

Deep AI and big data have reshaped the IT field. Since AI refers to the simulation of human intelligence processes by computer systems for problem solving, the current landscape of IT provides an excellent opportunity for us to re-examine how human cognition has responded to new developments in IT. In this paper, we discuss several issues related to human cognition in IT at AI-enriched big data era. First, we note that deep AI reflects human beings' needs for causality and explanations, and review a couple of related aspects. We then examine human cognition in IT from two perspectives: On the theoretical side, recent developments in quantum computing has the potential to reveal complex nature of human cognition in the form of quantum cognition, and on the practical side, cognitive bias may play a negative role, as demonstrated in the way of dealing with fake news. The complexity of human cognition in IT leads to a proposed multi-layer concept description which is based on our view that a complete concept definition is impossible without incorporating human cognitive feedback in the loop. We conclude this paper by emphasizing the need for integrated study of human cognition in IT. [ABSTRACT FROM AUTHOR]

The terms "extended cognition" and the "extended mind" identify two strands of philosophical argument that are commonly subsumed under the general heading of active externalism. The present paper describes an integrated approach to understanding extended cognition and the extended mind—one that papers over the differences between these two, ostensibly distinct, forms of cognitive extension. As an added bonus, the paper describes how active externalism might be applied to the realm of non-cognitive phenomena, thereby yielding an expansion in the theoretical and empirical scope of the active externalist enterprise. Both these points of progress stem from what is called the dispositional hypothesis. According to the dispositional hypothesis, extended cognition occurs when the mechanisms responsible for the manifestation of dispositional properties include components that lie beyond the borders of the thing to which the dispositional properties are ascribed. • A clearer explication of active externalist theses is required. • According to the dispositional hypothesis, cognitive extension occurs when the mechanisms responsible for the manifestation of dispositional properties extend beyond the borders of the entity to which dispositional properties are ascribed. • A consideration of dispositional properties provides the basis for a unified approach to understanding extended cognition and the extended mind. • Active externalism can be applied to phenomena that do not fall within the empirical orbit of cognitive science. [ABSTRACT FROM AUTHOR]

Van Duijn and colleagues propose that minimal cognition can be understood in terms of sensorimotor coordination – that is the coordination between sensory apparatus of an organism and the motor processes that move it around the environment (2006). Despite there being much to recommend this account it still faces some challenges. For example, it does not accord with the intuition that there are cognitive processes that have little to do with sensorimotor coupling, it relies on a strong distinction between metabolic and cognitive processes, and perhaps counterintuitively it denies cognition to plants but grants it to the bacteria on their roots that perform various functions for them. Moreover, it is difficult to see how to account for cognition over the transition from single to multicellular organisms. This paper proposes taking a more radical view of cognition to be what happens in swarms – the coordination of multiple processes through the traces of their actions in the environment. Key to this approach is the observation that the structure of the environment plays an active coordinative role and that this structure results in part from the actions of the system that it helps coordinate. Systems develop sensitivity to the appropriate trace variables in the environment. Viewed as collections of processes bacteria, biofilms, plants, animals can be viewed as cognitive systems in this framework and it has the potential to be applied to the social world. [ABSTRACT FROM AUTHOR]

The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation. [ABSTRACT FROM AUTHOR]

Military information is gradually overloaded due to the diversity of sources and the exponential growth in quantity, which greatly affects the accuracy of intelligence personnel in extracting and analyzing military information. The modern warfare approach has also evolved from the traditional physical domain to the cognitive domain, and competing for advantages in the cognitive domain has become a key objective of combat. Therefore, constructing domain knowledge graphs and mining the relationships between data play an important role in cognitive domain analysis. In this paper, we propose a convolutional network recognition method based on improved two-layer bi-directional BiLSTM networks named the BERT- α BiLSTMs-RECNN-CRF (BDBRC) model. For the difficulties of military entities generally having long names and low extraction accuracy, as well as the existence of a large number of composite entities that are difficult to recognize, an improved two-layer BiLSTM model is devised first. In view of the fact that the BiLSTMs model always extracts features equally in long-distance text sequences without actually considering the different influences of different sentence contexts, contribution factor a is added to extract the contribution of the above and below to the target entity in different sentences respectively. Then, aiming at the strong problem of the domain of military news texts and the high level of inter-entity ambiguity, we propose a method that utilizes a modified convolutional network (RECNN) for partial feature extraction and jointly with a modified two-layer BiLSTM network for entity recognition. The experiment on the self-constructed dataset shows that the F1 value of the model proposed in this paper reaches 93.18%, and the F1 value, P, and H of our model are all better than the baseline model, which verifies the performance of the model. At the same time, we use public data sets MSRA and CLUB2020, and the experimental results show that the model proposed in this paper also has a good performance in the public data set, verifying the universality of the model. It can provide methodological support for the construction of the military knowledge graph. [ABSTRACT FROM AUTHOR]

There is a need for effective interventions to stave off cognitive decline in older adults. Cognitive training has variably produced gains in untrained tasks and daily functioning. Combining cognitive training with transcranial direct current stimulation (tDCS) may augment cognitive training effects; however, this approach has yet to be tested on a large-scale. This paper will present the primary findings of the Augmenting Cognitive Training in Older Adults (ACT) clinical trial. We hypothesize that receiving active stimulation with cognitive training will result in greater improvements on an untrained fluid cognition composite compared to sham following intervention. 379 older adults were randomized, and 334 were included in intent-to-treat analyses for a 12-week multidomain cognitive training and tDCS intervention. Active or sham tDCS was administered at F3/F4 during cognitive training daily for two weeks then weekly for 10 weeks. To assess the tDCS effect, we fitted regression models for changes in NIH Toolbox Fluid Cognition Composite scores immediately following intervention and one year from baseline controlling for covariates and baseline scores. Across the entire sample, there were improvements in NIH Toolbox Fluid Cognition Composite scores immediately post-intervention and one year following baseline; however, there were no significant tDCS group effects at either timepoint. The ACT study models rigorous, safe administration of a combined tDCS and cognitive training intervention in a large sample of older adults. Despite potential evidence of near-transfer effects, we failed to demonstrate an additive benefit of active stimulation. Future analyses will continue to assess the intervention's efficacy by examining additional measures of cognition, functioning, mood, and neural markers. • Post-tDCS and cognitive training, the sample improved on an untrained fluid cognition composite. • Improvements on the untrained fluid cognition composite maintained one year following baseline. • In the overall sample, there were no significant tDCS effects on changes in cognitive performance. • It is feasible to administer tDCS and cognitive training safely in older adults on a large scale. • Using individualized tDCS dosing parameters may better facilitate a treatment response. [ABSTRACT FROM AUTHOR]

This Editor's Introduction to the Cognitive Systems Research special issue, "Innovative Dynamical Approaches to Cognitive Systems," has three aims: First, the background and motivation for the topic are stated. Second, overviews of the contributing papers are presented. Third, based on the papers, speculations on future directions in dynamical approaches to the investigation of cognitive systems are presented. Here, the focus is on concepts, data analysis methods, and computational modeling. [ABSTRACT FROM AUTHOR]

This paper analyzes the discourse of 19 episodes from Seasons 1 and 2 of "Paranormal Survivor," a reality TV show about ghosts, haunted property, eeriness, fear, dread, and relief. Using various approaches to discourse analysis and multimodal discourse analysis we uncover the macro-, meso, and micro-level patterns of meaning creation in "Paranormal Survivor" that relate dually to the affective/psychological/sensory experiences of the protagonists as well as the cognitive puzzles, doubts, and (un)certainties underlying such paranormal phenomena. The macro-level analysis relates to the narrative structure of the program delivered through achronological multi-party perspectives, reflecting varying levels of emotional intensity/objectivity. The meso-level uncovers the various coherently related themes underlying the program. The micro-level examines the smaller bits of discourse that combine as they build the meso- (thematic) and macro- (structural) components of the larger spates of discourse through affect, emotion, believability, evidence, and empathy. This three-tiered analytic approach serves to systematically reveal the ways in which discursive mechanisms combine to enhance suspense, intensify horror, bolster both believability and relatability, and ultimately seek to establish and augment audience engagement. • Stance-taking is a discursively psycho-social cognitive and affective reaction to external stimuli. • Reality TV is both a reflection and magnification of everyday life. • Audience engagement in reality TV is a complex communicative process. • Suspense, curiosity, and surprise link both the affective and epistemic/cognitive domains of stance. [ABSTRACT FROM AUTHOR]

Resilient system performance in high-stakes settings, which includes the ability to monitor, respond, anticipate, and learn, can be enhanced for trainees through simulation of realistic scenarios enhanced by augmented reality. Active learning strategies can enhance simulation-based training, particularly the mental model articulation principle where students are prompted to anticipate what will happen next and the reflection principle where students self-assess their performance compared to a gold standard expert model. In this paper, we compared simulation-based training for trauma care with and without active learning strategies during pauses in the simulated action for progressively deteriorating patients. The training was conducted online and real-time without a facilitator, with 42 medical students viewing training materials and then immediately taking an online quiz for three types of trauma cases: hemorrhage, airway obstruction, and tension pneumothorax. Participants were randomly assigned to either the experimental or control condition in a between-subjects design. We compared performance in the control and experimental conditions based on: A) the proportion of cues correctly recognized, B) the proportion of accurate diagnoses, C) the proportion of appropriate treatment interventions, and D) verbal briefing quality on a 1–5 scale. We found that the training intervention increased recognition of subtle cues critical for accurate diagnosis and appropriate treatment interventions; the training did not improve the accuracy of diagnoses or the quality of the verbal briefing. We conclude that incorporating active learning strategies in simulation-based training improved foundational capabilities in detecting subtle cues and intervening to rescue deteriorating patients that can increase the readiness for trainees to contribute to resilient system performance in the high-stakes setting of emergency care in hospitals. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, new wireless applications and services are exploding. These new applications and services usually need wider bandwidth and higher speed. The voice of people calling for a fast and convenient network is getting stronger and stronger, and the wireless network must be the main development direction of the network in the future world. In order to make the development of wireless communication technology not limited by limited resources, resource optimization design has always been a research hotspot of scientists. Among them, the optimal resource algorithm can get the theoretical global optimal solution, while the sub-optimal resource allocation algorithm, as a supplement to the former, can be used to reduce costs and improve efficiency when the system structure is too complex. In this paper, a resource optimization method based on sparse clustering algorithm is proposed, and the matching relationship between spectrum resources and the services carried by cognitive terminals is established, so that the effectiveness of collaboration and cognitive systems is verified. [ABSTRACT FROM AUTHOR]

In view of the current demand of heterogeneous, cross domain, many service platforms, wide distribution and low-cost resource sharing platform construction of agricultural public welfare information service resources, this paper puts forward a set of standardized and orderly data acquisition and interpretation sharing methods. Methods combined with the characteristics of multi domain resource sharing, metadata semantic modeling based on cognition is proposed to establish the mapping between metadata cognition and metadata semantics to resource description model, build a basic framework of resource sharing, extract the minimum core metadata required for resource sharing, define the basic rules for the existence of resource sharing data, and give the resource sharing description model. At the same time, in order to clearly explain the semantic annotation information in the model, the construction method of shared resource semantics is introduced. In order to reduce the difficulty of resource description, sharing and publishing on heterogeneous platforms, a shared resource description tool based on "resource sharing semantic description model" is introduced. This method combines the advantages of existing methods. At the same time, through the production of description tools and the introduction of semantics, the development difficulty and development volume of each service platform are reduced, the development cost is saved, the data sharing efficiency is improved, and the requirements of low-cost, cross domain, light dependence, resource readability and machine solvability for the construction of agricultural public welfare information service resource sharing platform are better solved. [ABSTRACT FROM AUTHOR]

In essence, information retrieval is a cognitive process, which is the matching process between the reader's or user's cognition and the system's pre-set cognition. At present, cognitive view has become a research hotspot in the field of information retrieval. The research of information retrieval cognitive model has laid a theoretical foundation for the development of information retrieval system. Based on scarcity theory, this paper analyses the dynamic change of user's cognitive ability and constructs a cognitive model of information retrieval based on scarcity theory. After analysis, this paper evaluates the effect of information retrieval cognitive model based on scarcity theory, and clarifies the specific problems that the model can solve. [ABSTRACT FROM AUTHOR]

Abstract The present paper seeks to find a notionally neat category for the pragmatic idiom That's what she said. While this may seem like a trivial goal at first glance, this paper will systematically uncarve the internal and contextual complexities of this formula and the challenges entailed in this aim. While That's what she said merges features and functions that have been defined and labelled by scholars of Pragmatics, Cognition and Psychology as well as the highly interdisciplinary field of Humor Studies, these approaches have not been sufficiently aligned. The present discussion thus forms part of a larger-scale issue, i.e. that humor-related utterances on the one hand and aspects and frameworks of formulaic speech on the other hand have so far not explicitly been brought together. The present paper aspires to do exactly that, putting forth an extended formulaic continuum that does not only fit That's what she said in particular, but potentially other similar formulae that may not yet have been assigned their place on e.g. Kecskés's continuum in its present form and scope either (2000, 2003, 2010, 2013). The present paper therefore proposes the new notion of Isolated Formulaic Punchlines for such formulae, acknowledging their supposedly incoherent or isolated occurrence in the discourse, their formulaic makeup and their humorous potential. Highlights • This paper deals with the That's what she said , a pragmatic idiom which has so far hardly received any attention. • An extended formulaic continuum is proposed offering classificational space for That's what she said. • The notion Isolated Formulaic Punchline is introduced as an additional subcategory to pragmatic idioms. • This is done so to ultimately bridge the gap between the state-of-the-art formulaic framework by Kecskés on the one and the wide range of formulaic verbal humor strategies on the other hand. [ABSTRACT FROM AUTHOR]

Abstract This paper discusses how natural language could be supported in further developing the Common Model of Cognition following the TalaMind approach toward human-level artificial intelligence. These thoughts are presented as answers to questions posed by Peter Lindes, Paul Rosenbloom, and reviewers of this paper, followed by a description of the TalaMind demonstration system, and a general discussion of theoretical and strategic issues for the Common Model of Cognition. [ABSTRACT FROM AUTHOR]

Abstract Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition. [ABSTRACT FROM AUTHOR]

Abstract The paper considers the well known cognition emotion primacy debate between Zajonc and Lazarus in the period 1980-1982. The paper also considers the less known contribution to that debate, given in the same time, in 1981, by an Artificial Intelligence system, the Crossbar Adaptive Array (CAA) artificial neural network. The CAA system was the first artificial neural network that implemented cognitive processing together with emotion processing. While Zajonc proposed that emotion is computed first, and Lazarus proposed that cognition is computed first, the CAA architecture suggested that both emotion and cognition are computed simultaneously. This paper reviews the cognition-emotion primacy problem stated by Zajonc and Lazarus in 1980-82, and also reviews the 1981-82 CAA solution of that problem. [ABSTRACT FROM AUTHOR]

Establishing an initial belief rule base (BRB) expert system typically relies on expert knowledge. However, the heterogeneity in knowledge arising from variations in expert cognition directly impacts the accuracy and robustness of BRB. Furthermore, the existing research lacks a discussion on building the initial BRB under multi-expert knowledge. In response to this challenge, a novel game-based belief rule base (GBRB) is proposed in this paper. First, a consensus matrix is introduced to formalize the interaction among expert knowledge, which is the foundation for constructing a multi-expert evolutionary game. Subsequently, the replicator equation for population dynamics is employed to simulate the dynamic process of the multi-expert evolutionary game. Finally, optimal expert knowledge, which refers to referential values and belief degrees, is determined based on the evolution results to participate in establishing GBRB. Case studies are conducted to illustrate the construction process and effectiveness of the GBRB. The experimental results are compared with machine learning-related methodologies. The comparative results demonstrate the good performance of GBRB in modeling effectiveness and prediction accuracy. [ABSTRACT FROM AUTHOR]

As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learn new concepts from data and the general decision rule that comes from an independent decision table, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Decision tables corresponding to four granularities form a multi-granularity decision table group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework for mining multi-granularity decision rules, which involves going from a multi-granularity decision table group to the frequent maximal attribute patterns to the decision rules to the credible decision rules. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity decision table group, and downward monotonicity of credibility, and observed the impact of the parameter min_cov and min_conf on execution times. [ABSTRACT FROM AUTHOR]

The human vestibular system is adversely affected by the aging process. Recent evidence indicates that vestibular information and cognitive functions are related, suggesting that age-related vestibular loss may contribute to cognitive impairment. In this study, we aimed to investigate the effects of repetitive, home-based galvanic vestibular stimulation (GVS) on cognitive functions in healthy older adults. Twenty-one participants (age = 64.66 ± 2.97 years, 12 females) were randomly allocated to either a home-based GVS or an active control group. The GVS intervention lasted 20 min per session, five times a week, for two weeks (10 sessions). Cognitive functions were assessed before and after the intervention using the Stroop Test, Trail Making Test A&B, and Dual-Task (digit recall and paper-pencil tracking test). Our findings revealed a significant group-by-time interaction effect for the tracking accuracy (F (1,18) = 7.713, p = 0.012, η p 2 = 0.30), with only the home-based GVS group showing significant improvement (t = −2.544, p = 0.029). The proposed home-based GVS protocol offers a promising non-pharmacological avenue for enhancing visuospatial ability in healthy older adults. Further research is needed to investigate the effects of different GVS protocols on various cognitive functions, particularly in older individuals with different health conditions. • Home-based GVS is feasible for older adults. • Repetitive GVS improves visuospatial ability in healthy older adults. • GVS is a promising non-pharmacological tool for improving cognition. [ABSTRACT FROM AUTHOR]

Purpose: Cognition elaboration cues play critical roles in the innovation diffusion process of Internet innovative products. This paper aims to theorize and examine how central cognition elaboration cue and peripheral cognition elaboration cue impact users' continuance intention of mobile health applications in the context of the developing markets.Design/methodology/approach: Applying the elaboration likelihood model as the basic theoretical framework, this paper proposed the hypotheses and research model to explore the internal formation mechanism of users' continuance intention of mobile health applications. And the moderating effect of privacy concern was especially studied under the context of the developing market. The empirical investigation (N = 284) collects research data through questionnaire by selecting a certain mobile health application from Chinese markets.Findings: The results show that, both perceived usefulness and trust positively affect users' continuance intention. For central route, doctor's service quality and doctor's information quality positively influence perceived usefulness. For peripheral route, app's reputation and app's institution assurance significantly promote users' trust. What's more, for the moderating effect, privacy concern can enhance the impact of perceived usefulness on continuance intention and strengthen the effect of trust on continuance intention to a great extent.Originality/value: This paper is one of the first studies to focus on users' continuance intention of mobile health apps with the moderating effect of privacy concern in the developing markets. It is valuable to develop the study from the perspective of process-oriented to persuasion to explore users' behavioral intention. [ABSTRACT FROM AUTHOR]

Abstract Advances in hardware technology have enabled more integration of sophisticated software, triggering progresses in development and employment of Unmanned Vehicles (UVs), and mitigating restraints for onboard intelligence. As a result, UVs can now take part in more complex mission where continuous transformation in environmental condition calls for higher level of situational responsiveness. This paper serves as an introduction to UVs mission planning and management systems aiming to highlight some of the recent developments in the field of autonomous underwater and aerial vehicles in addition to stressing some possible future directions and discussing the learned lessons. A comprehensive survey over autonomy assessment of UVs, and different aspects of autonomy such as situation awareness, cognition, and decision-making has been provided in this study. The paper separately explains the humanoid and autonomous system's performance and highlights the role and impact of a human in UVs operations. [ABSTRACT FROM AUTHOR]

Human-extended machine cognition is a specific form of artificial intelligence in which the casually-active physical vehicles of machine-based cognitive states and processes include one or more human agents. Human-extended machine cognition is thus a specific form of extended cognition that sees human agents as constituent parts of the physical fabric that realizes machine-based cognitive capabilities. This idea is important, not just because of its impact on current philosophical debates about the extended character of human cognition, but also because it helps to focus scientific attention on the potential role of the human social environment in realizing novel forms of artificial intelligence. The present paper provides an overview of human-extended machine cognition and situates the concept within the broader theoretical framework of active externalism. The paper additionally shows how the concept of human-extended machine cognition can be applied to existing forms of human–machine interaction, especially those that occur in the context of the contemporary Internet and Web. [ABSTRACT FROM AUTHOR]

The question “Are we alone?” lingers in the human mind since ancient times. Early human civilisations populated the heavens above with a multitude of Gods endowed with some all too human characteristics – from their outer appearance to their innermost motivations. En passant they created thereby their own cultural founding myths on which they built their understanding of the world and its phenomena and deduced as well rules for the functioning of their own society. Advancing technology has enabled us to conduct this human quest for knowledge with more scientific means: optical and radio-wavelengths are being monitored for messages by an extra-terrestrial intelligence and active messaging attempts have also been undertaken. Scenarios have been developed for a possible detection of extra-terrestrial intelligence and post-detection guidelines and protocols have been elaborated. The human responses to the whole array of questions concerning the potential existence, discovery of and communication/interaction with an extra-terrestrial intelligence share as one clear thread a profound anthropomorphism, which ascribes classical human behavioural patterns also to an extra-terrestrial intelligence in much the same way as our ancestors attributed comparable conducts to mythological figures. This paper aims at pinpointing this thread in a number of classical reactions to basic questions related to the search for extra-terrestrial intelligence. Many of these reactions are based on human motives such as curiosity and fear, rationalised by experience and historical analogy and modelled in the Science Fiction Culture by literature and movies. Scrutinising the classical hypothetical explanations of the Fermi paradox under the angle of a potentially undue anthropomorphism, this paper intends to assist in understanding our human epistemological limitations in the search for extra-terrestrial intelligence. This attempt is structured into a series of questions: I. Can we be alone? II. Who are we looking for? III. Or what are we looking for? IV. Where is everybody? V. What if we make contact and VI. So, what now? [ABSTRACT FROM AUTHOR]

This paper discusses the foundations of the theory of socionics, including the rationale behind Aušra Augustinavičiūtė’s model of information processing by the human psyche, and the philosophical thought behind the concept of information metabolism elements. Socionics was developed in the former Soviet Union and may be regarded as analogous to the Myers-Briggs Type Indicator typology of personality. The main goal of this paper is to present socionics to the English-speaking community, in order to better take advantage of its explanatory potential in the fields of interpersonal communication and psychological disorders. Reviewed topics include aspects of Jungian analytical psychology and Kępiński’s information metabolism theory, upon which Augustinavičiūtė based her model. After the model is presented, its theoretical implications are briefly discussed. [ABSTRACT FROM AUTHOR]

Multi-attribute group decision-making (MAGDM) is an analytical decision-making framework which gathers the views of multiple experts, and they often assess alternatives by degree adverbs in realistic scenarios. In order to treat expert opinions well, q-rung orthopair fuzzy sets (q-ROF) emerged as a data disposal tool, however, how to aggregate the information rationally remains a challenge. In this paper, q-ROF evidential reasoning methodology (q-ROF ERM) is proposed to guarantee the functionality in the case of conflicting, extreme information evaluations from experts and attributes. Second, the q-ROF attribute reduction (AR) framework is invented for attribute importance measurement. The flexibility and elegance of the model are boosted by the variation of the λ parameter and its lead to the elimination of redundant attributes. In addition to this, the q-ROF output three-way decision (TWD) framework is created to give the classification and ranking of alternatives. The relative output function derived from fuzzy distance measure and the adjustable utility pursuit coefficient τ afford both objectivity and subjectivity to the program selection, allowing the evaluation outcomes to be relevant to human cognition. Finally, the comparative experiments and sensitivity analyses of the two cases illustrate the scientificity and reliability of the presented framework in the MAGDM scenario. • A q-ROF contradictory information fusion method is proposed. • A similarity attribute reduction with λ method is established for q-ROF weights. • Q-ROF output three-way decision considers the utility pursuit τ is designed. • A q-ROF classification and ranking that fits human psychology is conducted. [ABSTRACT FROM AUTHOR]

With the rapid development of China's cultural innovation industry, enhancing brand equity and increasing brand value has become the only way for enterprises to achieve leapfrog development. Taking Chinese animation brand equity as an example, this paper constructs a brand equity driven model from the perspective of consumer cognition. Firstly, based on the brand resonance model, the theoretical model of animation brand equity is established. And then from the mass of product reviews, the focus of consumers on the brand is mined to form the latitude of brand equity. Finally, the theoretical model of brand equity is tested based on the Chinese consumer questionnaire data and structural equation model. This study provides a reference for the construction of Chinese animation brand equity, and also provides a new idea for the establishment of cultural brand equity management. [ABSTRACT FROM AUTHOR]

Recently, the architectural styles of 5G technology started supporting advance vehicle-to-everything (V2X) services. From standardization viewpoint, Service-oriented architecture Model Language (SoaML) specification has been used to model and design services for reusable environments. However, the system designers made less effort in addressing the issues related to visual representations (notations) in SoaML. In this paper, we present a novel evaluation approach to obtain the cognitive effectiveness of SoaML's visual notations by applying the scientific principles from Physics of Notations (PoNs). Furthermore, we evaluated results of the cognitive effectiveness of SoaML via survey-based empirical study for reference architecture, intra-cloud space, and organizational boundary symbols. Our results indicate the applicability of the new proposed notations and improvements in cognitive effectiveness of the SoaML notation. For future work, we plan to test the proposed architectural improvements for designing V2X service-based use cases. [ABSTRACT FROM AUTHOR]

With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intra- and inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social–cyber–physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion. [Display omitted] • Brain informatics-guided systematic fusion and brain investigation. • Brain big data integration and analytics based on the extensible Data-Brain. • Multi-source fusion of data, information and knowledge for brain computing. • Fusion and inference to interpret multiple uncertainties of brain computing findings. • Predictive model to build a bridge between brain computing and smart health services. [ABSTRACT FROM AUTHOR]

In this paper I respond to some provocative remarks by Zhang and Zhang (2017) and I argue that their claim that there is no compatibility between Default semantics and Relevance theory is exaggerated. In Capone (2011a,b) , I provided a synthetic approach which did not presuppose that we necessarily slavishly adhere to all details of Jaszczolt's or Relevance Theory. In that paper, I tried to make sense of the idea of modularity of mind. However, those ideas could also be integrated into a theory of neural networks. In this paper, I point out, however briefly, that it makes sense to connect the generalizations offered by Default Semantics with the role played by Broca’ s area in cognition (here I follow Pennisi and Falzone's (2016) ideas) (see also Capone, 2017 ). [ABSTRACT FROM AUTHOR]

This paper exploits three rounds of panel data provided by the Peruvian dataset of the Young Lives study to investigate the relationship between child cognition and obesity status among the poor. Child weight status is measured by a full distribution of child weight, from severely thin to obese, using data from a z-score for body mass index and cognition is measured by the Spanish version of the Picture Peabody Vocabulary Test (PPVT). This relationship is studied at age five and age eight (school age), and disaggregated across socioeconomic factors of gender, urban/rural setting and indigenous/nonindigenous status. The initial results suggests that obese children have higher cognitive scores and that this result is driven by those who are female, non-indigenous and live in an urban region. However, after correcting for possible bias due to unobservable heterogeneity, there is little evidence of this relationship. The one exception is for a weakly significant relationship between obese female children and higher cognition, a relationship which tends to weaken between the ages of five and eight. On the other end of the weight distribution, indigenous children who are severely thin or thin have significantly lower cognitive scores, a relationship that holds after correcting for possible bias and appears to strengthen between ages of five and eight. This paper contributes to a very small set of literature on child cognition and obesity, points to the importance of controlling for unobserved heterogeneity in estimation, and is the first of its kind to study this relationship in a developing country. [ABSTRACT FROM AUTHOR]

This paper provides a review of the analysis and modeling of human spatial planning, perception, and learning based on the dynamics of agent-environment interactions. The approach stems from an analysis and modeling framework that was previously conceived using interaction patterns emerging from system-wide interactions as the basic unit of analysis. The paper first discusses the rationals for using patterns in agent-environment interactions as units of organization of behavior, and as functional units of the modeling framework. These concepts are then illustrated through two applications using experimental data from a first-person flight simulator that implements agile obstacle navigation tasks. The first application focuses on the analysis of the formation and evolution of interaction patterns over successive trials, and the use of these patterns as basic elements of the task environment representation, enabling the evaluation of the learning process and assessment of the operator performance. The second application focuses on the analysis of interaction patterns as functional units supporting the modeling of the underlying perceptual guidance and control mechanisms. These examples demonstrate the relevance of dynamics in agent-environment interactions for studying a wide range of functions across the human control hierarchy. [ABSTRACT FROM AUTHOR]

My paper discusses the current nexus between spatial cognition and critical discourse analysis (CDA), assessing its strengths, limitations, and prospects in the area of political/ideological discourse studies. In the first part, I review the models of spatial cognition that have made the most significant contribution to CDA. Discussing Deictic Space Theory and Text World Theory, among others, I argue that cognitive models reveal further theoretical potential which has not been exploited yet. Focusing on how representations and ideologically charged worldviews are established, they fail to provide a pragmatic explanation of how addressees are made to establish a worldview, in the service of speaker's goals. The second part of the paper outlines Proximization Theory, a discursive model of crisis and conflict construction in political discourse. I argue that, unlike the other models, it fully captures the complex ideological positioning in political discourse space and, crucially, the dynamics of conflict between the opposing ideologies of the space. [ABSTRACT FROM AUTHOR]

The concepts of sustainability and innovation represent a fundamental conflict of objectives. This fact may help explain why the management literature has been ambiguous in its approach to sustainability-oriented innovation. The main purpose of this article is to develop an analytical framework to further study the influence of mindfulness. Mindfulness can support the development of the cognitive abilities, attitudes and behaviors of individuals who work within organizations. It can help them identify and utilize creative solutions that can lead to sustainability-oriented innovation. This paper presents a theoretical study that has been designed to support future research in sustainability-oriented innovation in the organizational context. A systematic literature review provides empirical evidence that mindfulness training can promote changes in individual cognition mechanisms that favor creativity and ecological concerns. As an isolated training technique, mindfulness can be used as a tool to reduce the stress that is caused by efficiency-driven business models. To help influence management actions in favor of sustainability-oriented innovation, this paper offers a research agenda that combines the following five fields of study: sufficiency-driven business models, inclusive capitalism, sustainability-oriented innovation capability, mindfulness in organizations and interdisciplinary and qualitative mindfulness. [ABSTRACT FROM AUTHOR]

The prime focus of scientists and the researchers in the field of intelligent systems is oriented to understand and replicate information processing functionalities of the human brain network. There have been continuous efforts to develop an intelligent knowledge system that incorporates the neuronal processes involved in cognition. In this paper the authors give some details of their unique work on the development of a knowledge system with information processing functionalities moving toward cognitive processing of the human brain, using intelligent links and nodes with processing capabilities. The existing knowledge systems connect only the pieces of information represented by nodes of a network and connect nodes using connectors, referred as edges. The edge is an attribute defining a relationship (e.g. isA, hasA) between the nodes. These edges lack cognitive properties, and the nodes lack functional processing to support efficient information transfer between nodes. The main objective of this paper is to provide an overview of the characteristics of a neurocognitive knowledge network model (NCKM) developed by the authors. NCKM is a knowledge network with nodes and links developed to provide methods that deal with the cognitive processes of the human brain, useful for efficient information processing. These cognitive processes provide self-directivity and learning within the network for intelligent knowledge retrieval. This work opens up a pathway to ingrain cognitive and neuronal characteristics for information processing into knowledge networks. [ABSTRACT FROM AUTHOR]

This review concentrates on the issue of acquisition of abstract words in a cognitive robot with the grounding principle, from relevant theories to practical models of agents and robots. Most cognitive robotics models developed for grounding of language take inspiration from the findings of neuroscience and psychology to get the theoretical skeleton of these models. To better understand these modelling approaches, it is indispensable to work from the base (theoretical accounts) to the top (computational models). Therefore in this paper, succinct definition of abstract words is presented first, and then the symbol grounding issue and accounts of grounded cognition for abstract words are given. The next section discusses the computational modelling approaches for abstract words grounding phenomenon. Finally, important cognitive robotics models are reviewed. This paper also points out the strengths and weaknesses of relevant hypotheses and models for the representation of abstract words in the grounded cognition framework and helps the understanding of issues such as where and why modelling efforts stand to address this problem in comparison with theoretical findings. [ABSTRACT FROM AUTHOR]

This paper tries to analyze the relationship between external knowledge acquisition strategies and the relational social capital generated by firms in their relationships networks. The study is based on an empirical research applied to a sample of Spanish companies from biotechnology and pharmaceutical industries. The paper mainly focuses on the effect of trust, as the basic component of relational social capital, on two types of knowledge acquisition strategies: alliances and direct purchases of external knowledge. Moreover, this paper proposes an interaction effect of a company's cognitive social capital on the relationships between relational social capital and alliances as a method of knowledge acquisition. The argument is that shared visions and common values could moderate the relationship between trust and the acquisition based on narrower relationships between agents. The results of the study show that, although relational social capital has a significant influence on both types of strategies (alliances, direct purchases), such influence is more important for the case of alliances, although it is not finally affected by the interaction between cognitive and relational social capital. [ABSTRACT FROM AUTHOR]

Do metaphors express a single and determinate proposition (or even a small set), or do they convey a loose impression? Even ordinary metaphors such as A: " Her new boyfriend is a mouse ". B: " Well, it's better to date a mouse than a rat ", are interspersed with images contingent on individual memories and associations (Wilson and Carston, 2019). Given the vagueness of what may be communicated by metaphors, my aim in this paper is firstly, to acknowledge the existence of images and emotions in their interpretation with evidence from L2. Secondly, to argue that non-propositional effects should be allowed a prominent role in the analysis of metaphors by according effects which ensue from the interplay between perception and cognition. • Language is not emotionally dry. • The maximum benefit from new information is not exclusively cognitive. • Episodes recalled are quick, impulsive, easy to re-experience, more rewarding. • Emotions and images may motivate non-demonstrative inference. • Emotions and images constitute interpretive routes in creative and common metaphors. [ABSTRACT FROM AUTHOR]

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable. [ABSTRACT FROM AUTHOR]

The paper considers a purpose and features of ontologies use in describing of subject area, both from a theoretical and an applied point of view. Criteria for identifying ontologies types are indicated. Based on cognition schematism principles, a knowledge representation ontologies system has been developed, that combines language, forms of knowledge representation and process schemes. It is shown that it is exactly such a system of ontologies makes it possible for the practical use of ontologies in computing environments. [ABSTRACT FROM AUTHOR]

Simultaneously extracting junctions and their corresponding line segments from images presents a promising approach to structural environment cognition. However, conventional methods employ square convolution for line feature extraction, resulting in the exclusion of long-range dependencies and the generation of suboptimal wireframe predictions. In this paper, we introduce an efficient and concise parsing method named Structural Asymmetric Convolution-based Wireframe Parser (SACWP). Taking advantage of the inherent similarities between structural asymmetric convolution and the predominant distribution of line segments in man-made environments, we propose a Structural Asymmetric Convolution module (SAC) that captures long-range contextual features while efficiently filtering out irrelevant information from neighboring pixels. Additionally, we introduce a feature aggregation module based on dilated convolution (DCFA) to seamlessly integrate contextual information from multiple receptive fields. We thoroughly evaluate our approach on the Wireframe and YorkUrban datasets, achieving preferable results of 69.3% and 29.7% msAP respectively. On the other hand, the promising results adequately demonstrate the effectiveness of SACWP to Wireframe Parsing task. [ABSTRACT FROM AUTHOR]

Concept-cognitive learning (CCL) is an emerging field for studying the representation and processing of knowledge embedded in data. Many efforts are focused on this field due to the interpretability and effectiveness of the formal concept (not pseudo concept). However, the standard CCL methods cannot tackle continuous data directly. Although the current fuzzy-based CCL (FCCL) is a straightforward approach to discovering the knowledge embedded in continuous data, it does not sufficiently utilize the native advantage of concepts in simulating the cognitive mechanism. Then it causes it to be incomplete and complex cognition. Inspired by the memory mechanism, this paper combines the recalling and forgetting mechanisms with CCL, called memory-based concept-cognitive learning (M-FCCL). Specifically, a cosine measure is introduced to describe the relationship of samples and construct cosine-similar granules to learn the concept. Subsequently, a fuzzy three-way concept based on the cosine similar granules is defined to represent and discover knowledge. Furthermore, two memory mechanisms are borrowed for the process of concept cognition for dynamic data classification and knowledge fusion: concept-recalling can enhance the effectiveness of concept learning, and concept-forgetting can effectively reduce the complexity of concept cognition. Finally, some experiments are compared with other methods on 16 benchmark datasets to show that M-FCCL achieves superior performance. Specifically, on these datasets, the proposed M-FCCL method achieves 17.02% and 18.54% classification accuracy gain compared with some advanced CCL mechanisms and popular classification methods. • A novel memory-based concept-cognitive learning model is established. • The fuzzy three-way concept based on cosine similarity granule is defined to represent knowledge. • A concept-recalling mechanism enhances the performance of concept learning. • A concept-forgetting mechanism is utilized to reduce the complexity of concept cognitive. • Extensive experiments show that the proposed approach is effective. [ABSTRACT FROM AUTHOR]