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Artificial neural network models are able to achieve great performance at numerous computationally challenging tasks like face recognition. It is of significant importance to explore the difference between neural network models and human brains in terms of computational mechanism. This issue has become an experimental focus for some researchers in recent studies, and it is believed that using human behavior to understand neural network models can address this issue. This paper compares the neural network model performance with human performance on a classic yet important task: judging the ethnicity of a given face. This study uses Caucasian and East Asian faces to train 4 neural networks including AlexNet, VGG11, VGG13, and VGG16. Then, the ethnicity judgments of the neural networks are compared with human data using classical psychophysical methods by fitting psychometric curves. The results suggest that VGG11, followed by VGG16, shows a similar response pattern as humans, while simpler AlexNet and more complex VGG13 do not resemble human performance. Thus, this paper explores a new paradigm to compare neural networks and human brains. • Neural networks are able to provide cognitive approaches from face perception. • Human perception is used as a benchmark to evaluate the performance of neural networks. • Neural networks utilizing eye regions are confirmed to be better to perceive faces. • Attentional region analysis would unveil the processing details of a neural network. [ABSTRACT FROM AUTHOR]

Affective computing is a rising interdisciplinary field of research spanning the areas from artificial intelligence, natural language processing to cognitive and social sciences. Potential applications comprise of man–machine interaction, healthcare, entertainment, teaching, marketing and many more. Despite the increasing number of papers published in the domains of affective computing, emotion recognition, and human–computer interaction (HCI), there are still gaps in the comprehensive literature review that covers all relevant studies in a single study, which this review attempts to address. As a result, this study provides a systematic literature review (SLR) on existing modalities (unimodals) for emotion recognition, emotion models, and trends in relevant studies by selecting articles published from January 2010 to June 2021. To ensure the retrieval of all relevant studies, a review protocol is used that includes both automatic and manual searches. Based on the research questions, the final 129 papers are reviewed and relevant information is extracted. This SLR provides future research directions to assist novice researchers and practitioners in more efficiently utilizing affective computing techniques. [ABSTRACT FROM AUTHOR]

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information. In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics. [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]

• Provides a framework combining deep learning with brain-like innate structures. • Introduces time-dependent interactions into Transformer to model emotional coherence. • Embeds a gating mechanism to identify the distinctions of different modalities. Multimodal sentiment analysis and emotion recognition has become an increasingly popular research area, where the biggest challenge is to efficiently fuse the input information from different modality. The recent success is largely credited to the attention-based models, e.g., transformer and its variants. However, the attention-based mechanism often neglects the coherency of human emotion due to its parallel structure. Inspired by the emotional arousal model in cognitive science, a Deep Emotional Arousal Network (DEAN) that is capable of simulating the emotional coherence is proposed in this paper, which incorporates the time dependence into the parallel structure of transformer. The proposed DEAN model consists of three components, i.e., a cross-modal transformer is devised to simulate the functions of perception analysis system of humans; a multimodal BiLSTM system is developed to imitate the cognitive comparator, and a multimodal gating block is introduced to mimic the activation mechanism in human emotional arousal model. We perform extensive comparison and ablation studies on three benchmarks for multimodal sentiment analysis and emotion recognition. The empirical results indicate that DEAN achieves state-of-the-art performance, and useful insights are derived from the results. [ABSTRACT FROM AUTHOR]

In the work setting of deep learning, most of the neural networks employed for visual object detection in recent years are based on bounding box regression. The performance of active detectors through multi-step decision-making is limited by the rough model design. However, from the perspective of cognitive science, the recognition in the human visual system is a decision process from coarse to fine. Based on the theory of "see the forest first, then the trees", this paper proposes a dynamic coarse-to-fine gaze strategy for active object detection, named AHDet, which takes the key points as the realization carrier of the coarse-to-fine concept. The detection process is divided into two steps, AIM and HIT. In the step of AIM, the positioning and prior bounding boxes for objects are given by detecting the center points, referring to the first glance. In the step of HIT, bounding boxes are dynamically adjusted to obtain compact bounding boxes with the help of the corner points, referring to the careful observation. With the design of the two-step coarse-to-fine gaze process, AHDet outperforms traditional approaches. A series of experiments performed on MS-COCO and PASCAL VOC dataset demonstrate the advantages of AHDet. [ABSTRACT FROM AUTHOR]

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception–action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world. [ABSTRACT FROM AUTHOR]

We offer a framework for the design and use of Ambient Smart Environments (ASEs) for preventive mental health care support. Drawing from Complex Systems Theory (CST) and 'E' Cognitive Science (ECS), we claim that ASEs have the potential to act in a preventive capacity in support of good mental health, i.e. supporting dynamics that avoid so-called "struck states" (which are, according to CST, thought generally to underpin forms of psychopathology). Here, we frame our discussion with what has recently been termed the "mind-technology problem". We define and characterise ASE systems, present some examples, and briefly survey some existing theoretical work. After introducing the essential CST terminology, the paper goes on to apply CST to explain developmental adaptation to continuously changing (smart) environments. Understanding the ASE's navigation in terms of a dynamic geometry between attracting and repelling points (or local minima/local maxima), allows us to develop neurotechnology that can augment clinical interventions by predicting upcoming shifts for good symptomatic outcomes, i.e. when a preventive intervention (i.e. destabilisation) should take place. We further offer clear directions for the development and design of such neurotechnology. [ABSTRACT FROM AUTHOR]

Upper limb amputation is a widespread problem worldwide, leading to massive loss of functionality for the victims. While a few solutions exist, these are often very expensive and involve expensive and dangerous surgical procedures. In this paper, we propose a low-cost, highly functional upper limb prosthesis controlled via Electroencephalogram (EEG) signals captured in real-time, classified into upper limb motion intention using a novel Genetic Algorithm (GA) optimized Long Short-Term Memory (LSTM) deep learning model to rehabilitate amputees. The proposed 3D-printed prosthetic arm has 3 Degrees-of-Freedom (DOF), which allows it to perform complex movements that can accurately emulate an actual human arm. The results discussed later in this paper conclude that our approach, while being low-cost, is highly accurate and functional. [ABSTRACT FROM AUTHOR]

Abstract There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a 'good' explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence. [ABSTRACT FROM AUTHOR]

Abstract In this paper I assess whether the recently proposed "No De-Coupling" (NDC) theory of constitutive relevance in mechanisms is a useful tool to reconstruct constitutive relevance investigations in scientific practice. The NDC theory has been advanced as a framework theoretically superior to the mutual manipulability (MM) account of constitutive relevance in mechanisms but, in contrast to the MM account, has not yet been applied to detailed case studies. I argue that the NDC account is also applicable to empirical practice and that it fares better than the MM account on both theoretical and empirical grounds. I elaborate these claims in terms of applications of the NDC theory to two case studies of cognitive science research on the role of eye movements in mechanisms for cognitive capacities. Highlights • This paper applies the NDC theory of constitutive relevance in mechanisms to cognitive science research. • The NDC theory can be used profitably to reconstruct constitutive relevance assessments in scientific practice. • The NDC theory is both theoretically and empirically superior to the MM account. [ABSTRACT FROM AUTHOR]

Abstract "Learning once, remembering forever", this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon "one-trial learning". The traditional artificial neural networks can simulate the psychological phenomenon of "implicit learning", but can't simulate the cognitive phenomenon of "one-trial learning". Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of "one-trial learning" for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of "one-trial learning", therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is one of the most attractive interdisciplinary research topics in artificial intelligence (AI). Different from other classification issues, multimodal sentiment analysis of human is a much finer classification problem. However, most current work accept all multimodalities as the input together and then output final results at one time after fusion and decision processes. Rare models try to divide their models into more than one fusion modules with different fusion strategies for better adaption of different tasks. Additionally, most recent multimodal sentiment analysis methods pay great focuses on binary classification, but the accuracy of multi-classification still remains difficult to improve. Inspired by the emotional processing procedure in cognitive science, both binary and multi-classification abilities are improved in our method by dividing the complicated problem into smaller issues which are easier to be handled. In this paper, we propose a Hierarchal Attention-BiLSTM (Bidirectional Long-Short Term Memory) model based on Cognitive Brain limbic system (HALCB). HALCB splits the multimodal sentiment analysis into two modules responsible for two tasks, the binary classification and the multi-classification. The former module divides the input items into two categories by recognizing their polarity and then sends them to the latter module separately. In this module, Hash algorithm is utilized to improve the retrieve accuracy and speed. Correspondingly, the latter module contains a positive sub-net dedicated for positive inputs and a negative sub-nets dedicated for negative inputs. Each of these binary module and two sub-nets in multi-classification module possesses different fusion strategy and decision layer for matching its respective function. We also add a random forest at the final link to collect outputs from all modules and fuse them at the decision-level at last. Experiments are conducted on three datasets and compare the results with baselines on both binary classification and multi-classification tasks. Our experimental results surpass the state-of-the-art multimodal sentiment analysis methods on both binary and multi-classification by a big margin. [ABSTRACT FROM AUTHOR]

The rapid development and widespread adoption of Artificial Intelligence (AI) technologies have made the development of AI-specific design methods an important topic to advance. In recent decades, the centre of gravity in AI has shifted away from cognitive science and related fields like psychology. However, there is a clear need and potential for added value in returning to stronger interaction. One potential challenge for this interaction may be the lack of common conceptual grounds and design languages. In this article, we aim to contribute to the development of conceptual interfaces for human-based AI-specific design methods through the idea of cognitive mimetics. We begin by introducing basic concepts from mimetic design and interpret them in the context of this thematic area. These provide some of the basic building blocks for a design language and bring to the surface key questions. These in turn provide a ground for explicating cognitive mimetics. In the second part of this paper, we focus on specifying a key aspect in cognitive mimetics: the contents of information processes. Others engaged in this field can derive value from using or developing the basic conceptual machinery to specify their own approaches in this interdisciplinary field that is still shaping itself. Furthermore, those who resonate with the idea of cognitive mimetics, as specified here, can join in taking this particular approach further. [ABSTRACT FROM AUTHOR]

One of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky's classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces. • A new method for generating multidimensional subspaces in a Semantic-VSM was created. • We exemplify the violation of symmetry by order effect in a Semantic-VSM. • We exemplify the violation of triangular equality in a Semantic-VSM. • Two new models are proposed to evaluate the diagnosticity effect in Semantic-VSMs. [ABSTRACT FROM AUTHOR]

This paper will discuss whether there are chances for a paradigm shift, i.e., a shift away from the (almost without much thought) taken-for-granted sequential, from piece-to-whole mainstream understanding of a cognitivist, now almost 70-year old information processing perspective to a perspective that takes as its starting point the whole, and hence meaning. The whole may stand for an object as embedded in its immediately salient as well as inconspicuous environment, where parts cannot be made sense of without knowing and understanding their roles in the larger configurations. It may stand for an organism or an organismic collectivity as embedded in its environment, a person or a collectivity of people in their embedded immediate and phenomenal field, which we will also not understand unless we understand their larger environmental, societal and cultural embeddedness. In today's scientific climate where the mainstream information-processing perspective is serving as an unchallenged, often hidden assumption within neuroscience and computer science, I will look for recent, promising developments which might nonetheless be paving the road to a perspective so long ago proposed by the Gestaltists yet somehow "lost in translation". In that sense, Gestalt theory, which till today has been widely distorted as a theory that talks about 'a bunch of grouping principles in (static) vision', is the very first dynamical theory within the psychological sciences that meticulously proposed this with a firm philosophical grounding, something that is ever more missing within theory building in psychology. [ABSTRACT FROM AUTHOR]

This paper discusses situated problem solving accomplished in the three constituent domains of Kashmiri carpet weaving practice viz. designing, coding and weaving. While, the designers’ problem relates to carving spatial regions on graph-sheets, the coders’ problem relates to cognitively demarcating relevant coding area in those graphs while generating code. Likewise, the weavers’ problem lies in demarcating weaving-territory on the loom, while the manufacturers’ problem relates to determining a particular spatial area for quality assessment. The paper discusses how the actors create fixed, transient and negotiable boundaries on the graph, the loom and the carpet to solve this problem of area determination and demarcation. Section 1 introduces this practice, its task domains and the problem of area determination and demarcation, Section 2 discusses the methodology, Section 3 discusses in detail the problem of area-determination in each task domain and the problem solving strategies employed by the actors using material available in their task-environments itself. In addition, the problem and resolution of area determination as encountered by the manufacturers is discussed. The paper concludes with a discussion on findings of this study with larger findings in cognitive science and accentuates the role played by situated and distributed cognition in unearthing the findings reported in this paper. [ABSTRACT FROM AUTHOR]

The problem of finding the similarity between natural language sentences is crucial for many applications in Natural Language Processing (NLP). An accurate calculation of similarity between sentences is highly needed. Many approaches depend on word-to-word similarity to measure sentence similarity. This paper proposes a new approach to improve the accuracy of the sentence similarity calculation. The proposed approach combines different similarity measures in the calculation of sentence similarity. In addition to traditional word-to-word similarity measure, the proposed approach exploits sentence semantic structure. Discourse representation structure (DRS) which is a semantic representation for natural sentences is generated and used to calculated structure similarity. Furthermore, word order similarity is measured to consider the order of words in sentences. Experiments show that exploiting structural information achieves good results. Moreover, the proposed method outperforms the current approaches on a standard benchmark dataset achieving 0.8813 Pearson correlation with human similarity. [ABSTRACT FROM AUTHOR]

• Electroencephalographic spatial features are used for emotion recognition. • Design automatic eigenvalue selection to improve the Common Spatial Pattern method. • A channel optimization strategy is proposed to decrease the computation load. • The proposed method achieves superior results than the traditional method. Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-based emotion recognition. Specifically, we first use a common spatial pattern algorithm to design a spatial domain filter according to three different emotions (positive, neutral, and negative). The traditional joint approximation diagonalization method using the criterion of the "highest score eigenvalue" may be unable to solve multiple classifications of emotion representation. Therefore, we design three different eigenvalue selection methods in terms of the positions of the eigenvalues with the highest scores. Finally, to make the installation easier and reduce the computational load, we also develop a channel-selection strategy based on the weight values that individually reflect the degrees of influence of all the channels on emotion recognition. Experiments are conducted on a self-collected dataset and on the MAHNOB-HCI dataset. The average recognition accuracies for the three emotion tasks are found to be 85.85% and 94.13% on the self-collected and MAHNOB-HCI datasets, respectively, thus proving the effectiveness of the proposed ICSPCS method for emotion recognition. [ABSTRACT FROM AUTHOR]

Nowadays, the economy of our society is in a period of transition, and there are many family businesses that also face the problem of successors' handover; moreover, whether the succession process is carried out smoothly will affect the success of the future development of the family business. In the real society, the wrong selection of the next successor in the family business and the failure of the successor in receiving enough preparations all can lead to the final collapse of the family business, which is common. This is no reason for people to think seriously about the successors. In this paper, based on the related theory of cognitive science, the psychological model of the successor of the family business and its influence on the internationalization of family business were studied and analyzed. [ABSTRACT FROM AUTHOR]

The notion of domain specificity plays a central role in some of the most important debates in cognitive science. Yet, despite the widespread reliance on domain specificity in recent theorizing in cognitive science, this notion remains elusive. Critics have claimed that the notion of domain specificity can't bear the theoretical weight that has been put on it and that it should be abandoned. Even its most steadfast proponents have highlighted puzzles and tensions that arise once one tries to go beyond an initial intuitive sketch of what domain specificity involves. In this paper, we address these concerns head on by developing an account of what it means for a cognitive mechanism to be domain specific that overcomes the obstacles that have made domain specificity seem so problematic. We then apply this understanding of domain specificity to one of the key debates that it has figured prominently in—the rationalism-empiricism debate concerning the origins of cognitive traits—and introduce several related theoretical notions that work alongside domain specificity in helping to clarify what makes a view more (or less) rationalist. This example illustrates how the notion of domain specificity can, and should, continue to play a central role in ongoing debates in cognitive science. [ABSTRACT FROM AUTHOR]

According to the Language of Thought Hypothesis (LoTH), an influential account in philosophy and cognitive science, human cognition is underlain by symbolic reasoning in a formal language. In this account, concepts are expressions in a Language of Thought, deduction is syntactic manipulation in this language, and learning is an inference of expressions in this language from data. This picture raises the question of what LoT humans have, and how to infer it from behavior. In this paper, we pave the way towards answering this question, by approaching a more fundamental question: to what extent is it possible in principle to recover the human LoT from experimental data? To answer this question, we focus on the fragment of LoT that is concerned with representing Boolean categories and simulate the recovery of the Boolean LoT from category learning experiments. Our findings show that in principle the vast majority of Boolean LoTs can be accurately recovered from experimental data. However, we find that this crucially depends on the employed experimental design. Moreover, we find evidence that LoTs with fewer operators can be recovered from category learning data faster. [ABSTRACT FROM AUTHOR]

The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages. However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG3 with cognitive modelling, with the aim of further validating the HsMM-EEG3 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG3 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task. Applying HsMM-EEG3 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general. • HsMM-EEG emerges as a powerful method to explore cognitive structure in EEG data by extracting clear-cut processing stages. • Cognitive modelling with ACT-R allows functional assumptions of intra-trial processes. • Both methods were applied to a mental rotation task well-suited for exploring spatial reasoning and strategy choice. • Model predictions correspond well to HsMM-EEG stages and allow insights not possible by either method alone. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested. [ABSTRACT FROM AUTHOR]

In this paper, we analyse how GPS-based navigation systems are transforming some of our intellectual virtues and then suggest two strategies to improve our practices regarding the use of such epistemic tools. We start by outlining the two main approaches in virtue epistemology, namely virtue reliabilism and virtue responsibilism. We then discuss how navigation systems can undermine five epistemic virtues, namely memory, perception, attention, intellectual autonomy, and intellectual carefulness. We end by considering two possible interlinked ways of trying to remedy this situation: [i] redesigning the epistemic tool to improve the epistemic virtues of memory, perception, and attention; and [ii] the cultivation of cognitive diligence for wayfinding tasks scaffolding intellectual autonomy and carefulness. [ABSTRACT FROM AUTHOR]

Abstract Recent interest in oral poetic traditions has centred on the possibility of applying cognitive linguistic categories in order to shed light on the nature of poetic improvisation across a range of cultures and historical periods (Boas, 2016). Frames, or topics or scenes, can be seen to be associated with particular scripts, consisting at least partly of formulaic language, which facilitates the process of poetic creation in real time. This paper focuses on the ayyu , a short improvised oral poetic genre common among the Jbala people of northern Morocco. In the first section, I examine the habitual repertoire of two singers, and show how they indeed associate particular frames (love, pilgrimage, etc.) with certain organizational sequences and formulaic phrases that are also found in the context of these frames in the wider corpus of Jebli poetry. In the second part, I focus on the klam , or utterances, inserted in the middle or placed in the end of ayyus that are always ignored in dictated or published versions of these poems. Klam does not strictly belong within the four-line verse, but is consistently delivered during its performance. These utterances seem to have an important role in live performances, operating as a confirmation to the audience that they have picked up the right frame from the rather sketchy information in the quatrain, and from the use of particular formulaic expressions. Interestingly, although the singers generally use the predictable last line, occasionally they come up with a different option, perhaps with a view to surprising their audience or provoking a response. Highlights • In oral poetry frames, or topics or scenes, can be seen to be associated with particular scripts, consisting at least partly of formulaic language. • This research is based on a living tradition (material collected in 2018). • I examine the habitual repertoire of two singers from the Jbala region (northern Morocco), and show how they associate particular frames with certain organizational sequences and formulaic phrases. • I analyse klam , or utterances, inserted in the middle or placed in the end of ayyus, that are ignored in dictated or published versions of these poems. However, they seem to have an important role. • This study has implications for our understanding of the evidence concerning the oral genres of the past. [ABSTRACT FROM AUTHOR]

Article 12 of the UNCRPD on equal recognition before the law, places an obligation on member states to 'provide access by persons with disabilities to the support they may require in exercising their legal capacity'. This has resulted in an increased focus on the concept and practice of supported decision-making, as opposed to substitute decision-making, for those with cognitive disabilities. To date, translation of this concept into law has been limited. However, Law Reform Agencies, tasked with reviewing legal decision-making schemes are increasingly recommending incorporation of legally recognised supported decision-making measures. This paper identifies the contribution of Law Reform Agencies' reports and recommendations to the evolving body of knowledge in relation to supported decision-making. In particular, it analyses the rationales for recommendations favouring the introduction of forms of legally recognised supported decision-making and the types of legal models of supported decision-making being recommended by Law Reform Agencies. [ABSTRACT FROM AUTHOR]

The last fifteen years have seen a sea change in cognitive science where issues of embodiment, situatedness and dynamics have become central to the explanatory resources in use. 
This paper evaluates the suggestion that representation should be eliminated from the explanative vocabulary of cognitive science. We trace the history of the issue by examining the usefulness of action-oriented representation (AOR), and we reassess if there is still a good explanatory role for the notion of representation in contemporary cognitive science by looking at contexts of re-use, contexts of informational fusion and elaboration, contexts of virtualist perception, and contexts of representational extension, restructuring and substitution. We claim that in these contexts the notion of representation continues to fulfill a valuable function in linking the inner informational economy of cognitive systems to how they interact and couple with the world, and that the role of representation in explanation has not been superseded by enactive and radical embodied theories of cognition. The final section of the paper suggests that we might be better off adopting a more pluralist research perspective, accepting that certain branches of cognitive science seem to require the positing of representations in order to develop, whereas others (e.g. research into minimal cognitive systems), do not appear to require it. We conclude that trying to suppress the notion of representation in all areas of cognitive science is seriously misguided. [ABSTRACT FROM AUTHOR]

Summary The aim of this paper is to present a short historical perspective on the neurophysiological approach to hysteria and conversion disorders. The body of this paper will be constituted of three main parts. In the first part, we will present the significant progress due to some pioneers of neurology/psychiatry during the XIXth century. As we shall see, this period was particularly rich in personalities whose work gradually laid the foundations to a true medical approach to hysteria. In the first half of the XXth century, different factors have led to a long eclipse of the neurological approach to hysteria. In the second part, we will show how, by the 1960's–1970's, the conceptual and methodological advances in neurophysiology, as well as the turning point of cognitive sciences (and cognitive psychology in particular) allowed a gradual reinstatement of hysteria within the fields of neurology and clinical neurophysiology. Finally, and this is the third part of this paper, we will show how over the past three decades, an entirely new neurophysiological approach to hysteria and conversion disorders has emerged. [ABSTRACT FROM AUTHOR]

Urban systems are highly complex and non-linear in nature, defined by the behaviours and interactions of many individuals. Building on a wealth of new data and advanced simulation methods, conventional research into urban systems seeks to embrace this complexity, measuring and modelling cities with increasingly greater detail and reliability. The practice of transportation modelling, despite recent developments, lags behind these advances. This paper addresses the implications resulting from variations in model design, with a focus on the behaviour and cognition of drivers, demonstrating how different models of choice and experience significantly influence the distribution of traffic. It is demonstrated how conventional models of urban traffic have not fully incorporated many of the important findings from the cognitive science domain, instead often describing actions in terms of individual optimisation. We introduce exploratory agent-based modelling that incorporates representations of behaviour from a more cognitively rich perspective. Specifically, through these simulations, we identify how spatial cognition in respect to route selection and the inclusion of heterogeneity in spatial knowledge significantly impact the spatial extent and volume of traffic flow within a real-world setting. These initial results indicate that individual-level models of spatial cognition can potentially play an important role in predicting urban traffic flow, and that greater heed should be paid to these approaches going forward. The findings from this work hold important lessons in the development of models of transport systems and hold potential implications for policy. [ABSTRACT FROM AUTHOR]

In this paper, it is argued that Jakob von Uexküll’s “search image” notion, the original version of this notion within ethology, is still of use. A search image, in Uexküll’s sense, is an imagined object that an organism has in mind when it searches for something. Uexküll’s conception of the search image is useful both for understanding the theoretical context of contemporary notions of search images, and with an eye to envisioning possible future developments of the idea. Uexküll’s classical notion differs from contemporary versions in that it has a wider application, and is therefore of greater relevance to theoretical biology and cognitive science. It constituted an integral part of his ground-breaking Umwelt theory, stressing the fundamental plasticity of the Umwelt, the subjective lifeworld of an animal or human subject. In a contemporary development of Umwelt theory, expressed by the tripartite Umwelt model , the search image notion represents a key connection between the directly experienced core Umwelt and the mediated Umwelt. However, the key function that schemata have in cognitive processes is also the starting point for mistakes in perception. This article details both the constructive function search images have in animal and human perception, and the mismatches in perception they can lead to. It also explains how the existence of search images can help explain puzzles concerning subjective and neural time in contemporary cognitive science. [ABSTRACT FROM AUTHOR]

In an attempt to improve process safety, today’s plants deploy sophisticated automation and control strategies. Despite these, accidents continue to occur. Statistics indicate that human error is the predominant contributor to accidents today. Traditionally, human error is only considered during process hazard analysis. However, this discounts the role of operators in abnormal situation management. Recently, with the goal to develop proactive strategies to prevent human error, we utilized eye tracking to understand the situation awareness of control room operators. Our previous studies reveal the existence of specific eye gaze patterns that reveal operators’ cognitive processes. This paper further develops this cognitive engineering based approach and proposes novel quantitative measures of operators’ situation awareness. The proposed measures are based on eye gaze dynamics and have been evaluated using experimental studies. Results demonstrate that the proposed measures reliably identify the situation awareness of the participants during various phases of abnormal situation management. [ABSTRACT FROM AUTHOR]

Holonic multi-agent systems (HMASs) have recently attracted many researches in multi-agent systems community. Inspired from the multi-level and self-similar structures of social and biological system, holonic multi-agent systems have been widely used to model and solve complex real-world problems. The main concern in deploying HMASs is the problem of building the hierarchical holonic structure, called holarchy, and dynamically managing it during its lifetime. The way an HMAS is organized has a great impact on its applicability and performance. This paper proposes a self-organizing algorithm to build and manage the holoic structures in multi-agent systems. This algorithm is based on the local information of the agents about other agents they can communicate with. Using common social concepts, like skills, diversity, social exchange theory, and norms in definition of the algorithm, the outcomes of this research can be used in wide ranges of distributed applications. The proposed model is extensively tested in a task allocation problem; and its performance based on various design parameters is studied. Empirical results show that the proposed model properly increases the performance of the system in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

For effective human-robot interaction, we argue that robots must gain social-cognitive mechanisms that allow them to function naturally and intuitively during social interactions with humans. However, a lack of consensus on social cognitive processes poses a challenge for how to design such mechanisms for artificial cognitive systems. We discuss a recent integrative perspective of social cognition to provide a systematic theoretical underpinning for computational instantiations of these mechanisms. We highlight several commitments of our approach that we refer to as Engineering Human Social Cognition. We then provide a series of recommendations to facilitate the development of the perceptual, motor, and cognitive architecture for this proposed artificial cognitive system in future work. For each recommendation, we highlight their relation to the discussed social-cognitive mechanisms, provide the rationale for these recommendations and potential benefits, and detail examples of associated computational formalisms that could be leveraged to instantiate our recommendations. Overall, the goal of this paper is to outline an interdisciplinary and multi-theoretic approach to facilitate the design of robots that will one day function, and be perceived, as socially interactive and effective teammates. [ABSTRACT FROM AUTHOR]

Science that needs logical demonstration has failed to eliminate religious concepts. It is as if they have own validity that cannot be broken by scientific knowledge we trust the most at present. In this paper, I will attempt to establish a new cognitive theory to help explain the basis of belief in religious concepts. This form of cognition will be named simply unifying-induction or unifying-inductive cognition. As illustrations, I will consider some typical religious discourses involving concepts such as “all-in-one” or “one is everything.” It is these typically religious discourses that science has not been able to easily sweep away by its logical scientific proofs. In the end, although we perhaps cannot know if the religious beings such as gods really exist or not, we may understand these concepts are very the creation of human cognition. It also has important implications for other disciplines such as robotics, developmental psychology, cognitive archaeology, the history of science, the study of religion and so on. [ABSTRACT FROM AUTHOR]

Conservation scientists increasingly seek to find ways to implement their research for improved policy and practice. However, such efforts may be ineffective, or even counterproductive, if they are based on outdated models of science communication and behavioral change. Insights from fields that study how information is processed in the brain, how and why humans make decisions and take action, and how change spreads across social networks can support and improve existing efforts to translate conservation research into practice and policy. However, little of this research has made its way into the conservation science literature, thus limiting the power of these ideas to influence how research is communicated and how impact is understood. This paper seeks to address this gap by discussing four common myths about how to best communicate science for decision-making, namely, that facts change minds, scientific literacy will lead to enhanced research uptake, individual attitude change will shift collective behaviors, and broad dissemination is best. The article provides four alternative insights that can support effective science communication and impact: engaging the social mind for optimal decision-making, understanding the power of values, emotions, and experience in swaying minds, changing collective behavior, and thinking strategically for biggest impact. If we can understand how people process information, we can design interventions based on the best possible evidence of how humans make decisions for conservation management and policy. [ABSTRACT FROM AUTHOR]

The measurement of the semantic relatedness between words has gained increasing interest in several research fields, including cognitive science, artificial intelligence, biology, and linguistics. The development of efficient measures is based on knowledge resources, such as Wikipedia, a huge and living encyclopedia supplied by net surfers. In this paper, we propose a novel approach based on multi-Layered Wikipedia representation for Computing word Relatedness (LWCR) exploiting a weighting scheme based on Wikipedia Category Graph (WCG): Term Frequency-Inverse Category Frequency ( tf x icf ). Our proposal provides for each category pertaining to the WCG a Category Description Vector (CDV) including the weights of stems extracted from articles assigned to a category. The semantic relatedness degree is computed using the cosine measure between the CDVs assigned to the target words couple. The basic idea is followed by enhancement modules exploiting other Wikipedia features, such as article titles, redirection mechanism, and neighborhood category enrichment, to exploit semantic features and better quantify the semantic relatedness between words. To the best of our knowledge, this is the first attempt to incorporate the WCG-based term-weighting scheme ( tf x icf ) into computing model of semantic relatedness. It is also the first work that exploits 17 datasets in the assessment process, which are divided into two sets. The first set includes the ones designed for semantic similarity purposes: RG65, MC30, AG203, WP300, SimLexNoun666 and GeReSiD50Sim; the second includes datasets for semantic relatedness evaluation: WordSim353, GM30, Zeigler25, Zeigler30, MTurk287, MTurk771, MEN3000, Rel122, ReWord26, GeReSiD50 and SCWS1229. The found results are compared to WordNet-based measures and distributional measures cosine and PMI performed on Wikipedia articles. Experiments show that our approach provides consistent improvements over the state of the art results on multiple benchmarks. [ABSTRACT FROM AUTHOR]

This article deals with artificial intelligence models inspired from cognitive science. The scope of this paper is the simulation of the decision-making process for virtual entities. The theoretical framework consists of concepts from the use of internal behavioral simulation for human decision-making. Inspired from such cognitive concepts, the contribution consists in a computational framework that enables a virtual entity to possess an autonomous world of simulation within the simulation. It can simulate itself (using its own model of behavior) and simulate its environment (using its representation of other entities). The entity has the ability to anticipate using internal simulations, in complex environments where it would be extremely difficult to use formal proof methods. Comparing the prediction and the original simulation, its predictive models are improved through a learning process. Illustrations of this model are provided through two implementations. First illustration is an example showing a shepherd, his herd and dogs. The dog simulates the sheep’s behavior in order to make predictions testing different strategies. Second, an artificial 3D juggler plays in interaction with virtual jugglers, humans and robots. For this application, the juggler predicts the behavior of balls in the air and uses prediction to coordinate its behavior in order to juggle. [ABSTRACT FROM AUTHOR]

In contemporaneous cognitive psychology and cognitive science, literacy is not a central concept. In this paper, on the contrary, it is argued that both cognition and cognitive science are deeply influenced by literacy. Before addressing the experimental work on the structure of speech and spoken language, one of the major themes of Jacques Mehler's work, I recall his links with the Brussels Laboratory of Experimental Psychology and the important influence he had on the discipline and on our work. Most of the present paper is a discussion of the concept of the phoneme, its dependence on literacy, its role in language, and of the constant interactions between orthography and phonology in spoken language recognition. I argue that the phoneme is not a unit of perception, but a literate cognitive concept. In this way, I hope that cognitive science will overtly recognize the literacy heritage, the building of which it greatly contributes to. [ABSTRACT FROM AUTHOR]

Contours and skeletons are well-known shape representations that embody visual information by using a limited set of object points. Both representations have been applied in various pattern recognition applications, while studies in cognitive science have investigated their roles in human perception. Despite their importance has been shown in the above-mentioned fields, to our knowledge no existing studies have been conducted to compare their performances. Filling this gap, this paper is an empirical study of these two shape representations by comparing their performances over different binary image categories and variations. The image categories include thick, elongated, and nearly thin images. Image variations include addition of noise to the contours, blurring, and size reduction. The comparative evaluation is achieved by resorting to object classification (OC) and content-based image retrieval (CBIR) algorithms and evaluation metrics. The main findings highlight the superiority of contours but the improvements observed when skeletons are used for images with noisy contours. [ABSTRACT FROM AUTHOR]

Over the last decades, science has grown increasingly collaborative and interdisciplinary and has come to depart in important ways from the classical analyses of the development of science that were developed by historically inclined philosophers of science half a century ago. In this paper, I shall provide a new account of the structure and development of contemporary science based on analyses of, first, cognitive resources and their relations to domains, and second of the distribution of cognitive resources among collaborators and the epistemic dependence that this distribution implies. On this background I shall describe different ideal types of research activities and analyze how they differ. Finally, analyzing values that drive science towards different kinds of research activities, I shall sketch the main mechanisms underlying the perceived tension between disciplines and interdisciplinarity and argue for a redefinition of accountability and quality control for interdisciplinary and collaborative science. [ABSTRACT FROM AUTHOR]

This paper proposes to rethink the study of oral performativity in the context of modern cognitive science. To that end, we list a number of so-far unrecognized parallels between the Parry-Lord theory of composition in performance and what has come to be known as “usage-based” approaches to grammar and language acquisition in the field of Cognitive Linguistics. We develop these connections into an integrated whole, opening up the way for a research program in the new field of “cognitive oral poetics”, and relating it to a number of very topical questions in present-day cognitive science (creativity, language acquisition, multimodality). The conclusion vouches for a closer collaboration of literary theorists, linguists, and cognitive scientists in the establishment of cognitive oral poetics. [ABSTRACT FROM AUTHOR]

Creative thinking is central to the arts, sciences, and everyday life. How does the brain produce creative thought? A series of recently published papers has begun to provide insight into this question, reporting a strikingly similar pattern of brain activity and connectivity across a range of creative tasks and domains, from divergent thinking to poetry composition to musical improvisation. This research suggests that creative thought involves dynamic interactions of large-scale brain systems, with the most compelling finding being that the default and executive control networks, which can show an antagonistic relation, tend to cooperate during creative cognition and artistic performance. These findings have implications for understanding how brain networks interact to support complex cognitive processes, particularly those involving goal-directed, self-generated thought. [ABSTRACT FROM AUTHOR]

While some computational models of intelligence test problems were proposed throughout the second half of the XXth century, in the first years of the XXIst century we have seen an increasing number of computer systems being able to score well on particular intelligence test tasks. However, despite this increasing trend there has been no general account of all these works in terms of how they relate to each other and what their real achievements are. Also, there is poor understanding about what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. In this paper, we provide some insight on these issues, in the form of nine specific questions, by giving a comprehensive account of about thirty computer models, from the 1960s to nowadays, and their relationships, focussing on the range of intelligence test tasks they address, the purpose of the models, how general or specialised these models are, the AI techniques they use in each case, their comparison with human performance, and their evaluation of item difficulty. As a conclusion, these tests and the computer models attempting them show that AI is still lacking general techniques to deal with a variety of problems at the same time. Nonetheless, a renewed attention on these problems and a more careful understanding of what intelligence tests offer for AI may help build new bridges between psychometrics, cognitive science, and AI; and may motivate new kinds of problem repositories. [ABSTRACT FROM AUTHOR]

In a number of publications, Alan Leslie and colleagues have developed a theory of the psychological mechanisms underlying pretense. This theory maintains that pretense is an early manifestation of “theory of mind” or “mindreading” – the capacity to attribute mental states to oneself and others. Nichols and Stich proposed an alternative theory of pretense on which pretense in young children does not require mindreading. Rather, they argued, young children have a behavioral understanding of pretense. In a lengthy critique, Friedman and Leslie made a persuasive case that the Nichols and Stich theory cannot account for the early emergence of children’s capacity to engage in joint pretense and recognize pretense in others. In this paper, we set out a new “pretense game” theory of pretense that avoids the problems raised by Friedman and Leslie, and does not require that children who engage in joint pretense must have a theory of mind. We go on to argue that our pretense game theory can explain many of the facts about pretense that go unexplained in Leslie’s theory. The central shortcoming of Leslie’s theory is that it attempts to explain the production and recognition of pretense behavior by positing the existence of an innate concept, without explaining how this concept enables those who have it to recognize or produce pretense behavior. [ABSTRACT FROM AUTHOR]

Recent advances in the applications of quantum models into various disciplines such as cognitive science, social sciences, economics, and biology witnessed enormous achievements and possible future progress. In this paper, we propose one of the most promising directions in the applications of quantum models: the combination of quantum and mechanical models in social biophysics. The possible resulting discipline may be called as experimental quantum social biophysics and could foster our understandings of the relationships between the society and individuals. [ABSTRACT FROM AUTHOR]

Background: Arithmetic knowledge has long been known as an essential factor for educational and vocational success.Aims: This study aims to explore the effectiveness of a process-based Executive Function (EF) intervention program on the improvement of components of arithmetic.Methods and Procedure: A goal-directed sampling method was applied in this study. Participants were assigned into active control and experimental groups. Semi-experimental design with pre-test, post-test and follow-up was utilized in this research. Participants were included in this study based on their WISC-IV and Key-Math test performance. 30 male students aged 8-10 years with a formal diagnosis of Developmental Dyscalculia (DD), selected from a learning disability center in Ahvaz, Iran, participated in the study. The pre-test took 1 month, the intervention including 17 sessions, took two months and the post-test took 1 month. All the students' arithmetic knowledge were tested in pre-test, post-test, and 3 months after post-test to test the longevity of the intervention effects. Repeated measure Univariate Analysis of Variance was conducted in this study.Outcomes and Results: The results indicate that the students who attended the intervention, outperformed control group in the components of factual and procedural arithmetic in post-test and follow-up, however; the performance of two groups in conceptual knowledge was not different. This study contributes to the emerging evidence that EF intervention may improve factual and procedural arithmetic knowledge in children with DD.Conclusions and Implications: Process-based EF interventions can improve arithmetic knowledge of students with DD, which can contribute to the literature of this area WHAT THIS PAPER ADDS?: The current research helps cognitive science to present a more meticulous theoretical and conceptual pattern for EF components and math, using process-based EF intervention programs with arithmetic content. Furthermore, this research allows for specification of cognitive fundamentals of arithmetic development and understanding the mechanisms underlying the transfer effect of EF intervention to math. The findings of this research can contribute to evidence-based EF intervention studies and help educational psychologists in preparation of appropriate curricula based on the fundamental components of arithmetic development in preschool and primary school. [ABSTRACT FROM AUTHOR]

The interaction between people and computers takes place everywhere in everyday life through information processing. As the degree of contact between humans and computers continues to increase, the cognitive burden of interaction between humans and computers increases as the operating errors rate increases. HCI scientists examine and create particular technology user interfaces. They research and enhance technology development processes and develop and assess new technological applications. HCI has gradually merged its scientific interests to improve the usability and the technical understanding and technique of computer systems. Artificial intelligence (HCI-AI) based problems in human-computer interactions are suggested to identify the relationship between cognitive knowledge and the comprehension of natural and artificial intelligence forms. The models describe the relationship between HCI research and the conception of interactions between humans and computers. A model of the planning and controlling numerous tasks at medical reception and their hypothesis illustrates the relationships defined thus. The proposal proposes to evaluate links between other studies and the design of human-make-computer interactions by using the relations described for HCI and cognitive science. The paper recommends that any discipline such as cognitive science, which has a relationship between its research and the design of the human-computer interactions, should perform this assessment. Such an evaluation would determine whether or not the relationship is specified. The article suggests that better relationships are necessary to provide more effective research support to develop interaction between humans and computers. [Display omitted] [ABSTRACT FROM AUTHOR]