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Abstract Many of our cognitive capacities are shaped by enculturation. Enculturation is the acquisition of cognitive practices such as symbol-based mathematical practices, reading, and writing during ontogeny. Enculturation is associated with significant changes to the organization and connectivity of the brain and to the functional profiles of embodied actions and motor programs. Furthermore, it relies on scaffolded cultural learning in the cognitive niche. The purpose of this paper is to explore the components of symbol-based mathematical practices. Phylogenetically, these practices are the result of concerted organism-niche interactions that have led from approximate number estimations to the emergence of discrete, symbol-based mathematical operations. Ontogenetically, symbol-based mathematical practices are associated with plastic changes to neural circuitry, action schemata, and motor programs. It will be suggested that these practices rely on previously acquired capacities such as subitizing and counting. With these considerations in place, I will argue that computations, understood in the sense of Turing (1936), are a specific kind of symbol-based mathematical practices that can be realized by human organisms, machines, or by hybrid organism-machine systems. In sum, this paper suggests a new way to think about mathematical cognition and computation. [ABSTRACT FROM AUTHOR]

In this paper, an overview of the wide variety of occurrences of mental models in the literature is discussed. They are classified according to two dimensions obtaining four categories of them: static-dynamic and world-mental, where static refers to mental models for static world states or for static mental states and dynamic refers to mental models for world processes or for mental processes. In addition, distinctions are made for what can be done by mental models: they can, for example, be (1) used for internal simulation, they can be (2) adapted, and these processes can be (3) controlled. This leads to a global three-level cognitive architecture covering these three ways of handling mental models. It is discussed that in this cognitive architecture reflection principles play an important role to define the interactions between the different levels. [ABSTRACT FROM AUTHOR]

In the long run the cognitive algorithms intend to make super-intelligent machines and super-intelligent humans. This paper presents a technical process to reach specific aspects of super-intelligence that are out of the current human cognitive abilities. These aspects are inabilities to discover patterns in large numeric multidimensional data with a naked eye. This is a long-standing problem in Data Science and Modeling in general. The major obstacle is in human inability to see n-D data by a naked eye and our needs in visualization means to represent n-D data in 2-D losslessly. While these means exist their number and abilities are limited. This paper expands the class of such lossless visual methods, by further developing a new concept of Generalized Shifted Paired Coordinates. It shows the advantages of proposed reversible lossless technique by representing real data and by proving mathematical properties. [ABSTRACT FROM AUTHOR]

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study. [ABSTRACT FROM AUTHOR]

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need. [ABSTRACT FROM AUTHOR]

Assisting aged population or a population with disabilities is a critical problem in today's world. To compensate for their declined motor function, power-assist wearable robots have been proposed. In some cases, however, the cognitive function of these populations may have declined as well, and it may be insufficient to compensate only for their motor function deficiency. Perception-assist wearable robots, which can perceive environmental information using visual sensors attached to them, have been proposed to address this problem. This study addresses the problem of identifying motion intentions of the user of an upper-limb power-assist wearable robot, while the user engages in desired interactions with others. It is important to consider both the interacting parties in order to accurately predict proper interaction. Therefore, this paper presents an interaction recognition methodology by combining both the user's motion intention and the other party's motion intention with environmental information. A fuzzy reasoning model is proposed to semantically combine the motion intentions of both parties and environmental information. In this method, the motion intentions of both the user and the other party are simultaneously estimated using kinematic information and visual information, respectively, and they are employed for predicting the interactions between both parties. The effectiveness of the proposed approach is experimentally evaluated. [ABSTRACT FROM AUTHOR]

Crowdsourcing is the process of obtaining input or enlisting the services of a crowd of people with the aim of finding the best solution to a proposed problem. The method with which the collected input is aggregated affects the outcome of the crowdsourcing process. In this paper, we introduce a modelling framework through which we compare several aggregation methods for crowdsourcing systems. The work reveals a surprising result where confidence-related approaches lack in performance when compared to other approaches such as simple plurality voting or approaches which consider respondent competence. This inadequacy stems from a psychological phenomenon brought forth by David Dunning and Justin Kruger related to people's bias in assessing their own cognitive abilities. [ABSTRACT FROM AUTHOR]

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved. [ABSTRACT FROM AUTHOR]

As an advanced function of the cognitive neural mechanism of human brain, inductive reasoning is an important skill in language communication. Under the background of the development of information intelligence, it is a new research field to effectively display the cognitive neural function of inductive reasoning with the advantage of the logic operation of artificial intelligence algorithm. Therefore, in this paper, based on the neurolinguistics, the translation and introduction of Mo Yan's works were studied. And on the basis of the analysis of the characteristics of the cognitive neural mechanism of sentence inductive reasoning, by using fMR and ERP techniques, the narrowing characteristics of the semantic integrated components in the induction were investigated, and the dual processing model of inductive reasoning was discussed. After that, artificial intelligence particle swarm optimization (PSO) algorithm was introduced, and the problem of alignment in the translation of English and Chinese sentences in Mo Yan's works was transformed into the problem of finding the optimal solution for the corresponding fitness function in Chinese and English sentences in bilingual space. Thus, a scientific mathematical model was used to improve the accuracy of translation. The simulation experiments show that this study can effectively improve the accuracy of the translation and introduction of Mo Yan's works. [ABSTRACT FROM AUTHOR]

Abstract As a key industry promoting the transformation of the labour force and proving labour cognitive level, education and training has strong economic externality with positive external effects on socio-economic development according to many economists. However, the research on the economic evaluation of education and training industry is very limited. From the Western economics perspective of externality, this paper analyses education and training industry on the basis of the important role of human capital in China's economic transformation. The difference between education training's spillover effect and the relative marginal production factor's productivity is calculated by using the fidelity model. In this way, labour cognitive level can be quantified. The more people who receive education training, the more they can promote the economic efficiency of enterprises, and the more they can promote the harmonious development of the entire society and economy. Education and training industry has a positive externality effect that can improve labour cognitive level. [ABSTRACT FROM AUTHOR]

Abstract Taijiquan is a traditional Chinese martial arts which is popular in China and even Southeast Asia. Now, many university students are Practicing it. Previous studies focused on the cognitive effects of aerobic exercise with different intensities on the human brain. This paper will present an experiment on Whether Taijiquan can develop cognitive function and what is the effective frequency of cognition developed by Taijiquan. In experiment 1: seventeen male university students were recruited as EG who had been exercising Taijiquan more than one year, another thirty male university students who were healthy but whose major were not sports were as CG. The differences of P 300 were compared; In experiment 2: thirty female healthy university students were recruited for six weeks, randomly divided into 3 groups, 10 in each group. EG1 and EG2 for six weeks Taijiquan exercise. EG1 did Taijiquan exercise 3 times a week (Monday, Wednesday, Friday). EG2 did Taijiquan exercise 5 times a week (from Monday to Friday). CG did not do any sports. The P 300 of each group were recorded before the experiment, and in the second, fourth, sixth and eighth week. Through scientific reasoning and strict analysis, this study found: the long-term Taijiquan exercise can improve the cognitive function of the practitioners; For short-term Taijiquan exercise, the positive effects of Taijiquan exercise 5 times a week is better than that of 3 times a week. [ABSTRACT FROM AUTHOR]

Abstract There are a large number of old residential communities and their management state is related to thousands of families. As a typical representative country of the developing countries, China has a wider public influence because of its own special housing reform. However, the government's policies such as renovation and marketization management have had little effect. Why it is so hard to improve the management of old communities? In the current research, gradually transforming from urban renewal to the sustainable management improvement, but there are few to find the solution of the problem from owners' perspective. Therefore, from the perspective of social cognition, we established a cognitive theoretical model of owners' participation in community management according to a framework of interaction among individuals, environment and behavior. In addition, we studied the relationship between influencing factors of desire for owners' participation and actual management effects, and discussed the management improving strategies of old residential community. Taking empirical analysis through 1038 questionnaires from owners of 61 old residential communities, results show that the owners efficiency, outcome expectancy, hardware quality, soft resource quality, and neighborhood relationship of old residential community all have positive impacts on owners' participation in management. Specifically, outcome expectancy has the greatest impact, followed by hardware facilities quality, then soft resources quality and owners efficiency, while neighborhood relationship affect least. This study explains why the government's urban renewal policy failing to change the old residential management difficulties. What's more we gives solutions to establish a long-term development model which owners participate actively by improving law and disclosing old communities planning to reverse their pessimistic expectations. This paper has great significance on old residential community management theory construction and management practices in many countries with similar problems. [ABSTRACT FROM AUTHOR]

Abstract A total of 584 thematic journals were included in the "Chinese Journal of Psychology" from 1996 to 2017 as the basic data set for this study, using bibliometrics, co-occurrence analysis, word frequency analysis, etc., and using CiteSpace III. Software mapping of knowledge. Visual analysis of published papers, research hotspots, research topic time zones, core authors, author co-occurrences, and research institutions of sports psychology research in China. It aims to fully grasp the dynamics of research on the development of sports psychology in China. The process provides a valuable reference for the theoretical research and practice of sports psychology in China. The results show that: Taking time as the axis, the amount of published documents can be divided into three stages: slow growth stage, rapid growth stage, and stable development stage; research topic time zone is also divided into three stages, and the basis for division of each stage is For the introduction of national sports reforms or new policies; 1996–2017, the focus of sports psychology disciplines is the application of sports psychology in physical education and sports training; the core authors in the field are representatives of Yan Jun, Wang Jin, and Yan. Gang Yan, etc.; The core organization of the volume of documents issued is based on professional sports colleges. The representative agency is Beijing Sport University. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the application of computer technology in higher education and management mode based on cognitive anthropology was studied and a teaching recommendation method was constructed. Firstly, on the basis of the existing teaching recommendation research results, teaching recommendation was carried out by adopting the methods of linear weighting and cascade combination and combining with human social factors, geographical factors and time factors, and the validity of the results was improved. Then, the expansion was carried out in the Apache cluster environment, and the efficiency and scalability of teaching recommendation were improved, so that an innovative mode of higher education research and management was created. Experiments show that the implementation efficiency of comprehensive teaching recommendation method achieved in an Apache cluster environment is high. As the amount of data increases, the efficiency advantages become more pronounced. [ABSTRACT FROM AUTHOR]

Abstract Categorising is arguably one of the first steps in cognition, because it enables high-level cognitive processing. For a similar reason, categorising is a first step—a preprocessing step—in artificial intelligence, specifically in decision-making, reasoning, and natural language processing. In this paper we categorise the motion of entities. Such categorisations, also known as qualitative representations, represent the preprocessing step for navigation problems with dynamical obstacles. As a central result, we present a general method to generate categorisations of motion based on categorisations of space. We assess its general validity by generating two categorisations of motion from two different spatial categorisations. We show examples of how the categorisations of motion describe and control trajectories. We also establish its soundness in cognitive and mathematical principles. [ABSTRACT FROM AUTHOR]

Abstract Categorization is a fundamental function of minds, with wide ranging implications for the rest of the cognitive system. In humans, categories are shared and communicated between minds, thus requiring explanations at the population level. In this paper, we discuss the current state of research on the cultural evolution of categorization. We begin by delineating key properties of categories in need of evolutionary explanation. We then review computational modeling and laboratory studies of category evolution, including their major insights and limitations. Finally, we discuss remaining challenges for understanding the cultural evolution of categorization. [ABSTRACT FROM AUTHOR]

For people with mobility impairment, powered wheelchairs are able to extend independent transportation. However, most users find it difficult to operate the powered wheelchairs effectively. This can be due to variety of physical or cognitive impairments. Herein lies the motivation of developing intelligent wheelchairs that provide the user, assistance in control and navigation as and when required, supporting retention of residual skills. For better Human-Robot Interaction, such intelligent wheelchairs need to be cognitively enhanced. This paper propose a Cognitive Collaborative Architecture (CCA) for an intelligent wheelchair which is designed to assist the user as a team member. Whenever user finds it difficult to drive or control the wheelchair, CCA transfers control to the robotic agent which continues the human navigation strategy providing ‘assistance-as-required’. [ABSTRACT FROM AUTHOR]

Like all human actors, politicians possess limited cognitive capacity. In ordinary interactions, this limitation discourages political decision-makers from addressing high-dimensional policy problems unless incentivized to do so by exogenous “focusing events.” Public policy researchers have documented this pattern extensively, and have argued that cognitive constraints help explain the “stick–slip” dynamics that characterize macro-level policymaking. However, data and measurement limitations have prevented these studies from examining individual-level information processing patterns. In this paper, I develop a text-based approach designed to measure diversity of attention at an individual level, which I apply to an original dataset of Congressional hearing transcripts surrounding the 2008–2009 Financial Crisis. I find that individual speakers engaged with a more diverse set of topics during the crisis than before its onset, and became more focused as the crisis subsided. [ABSTRACT FROM AUTHOR]

In this paper, we study the connections between working memory capacity (WMC) and learning in the context of economic guessing games. We apply a generalized version of reinforcement learning, popularly known as the experience-weighted attraction (EWA) learning model, which has a connection to specific cognitive constructs, such as memory decay, the depreciation of past experience, counterfactual thinking, and choice intensity. Through the estimates of the model, we examine behavioral differences among individuals due to different levels of WMC. In accordance with ‘Miller’s magic number’, which is the constraint of working memory capacity, we consider two different sizes (granularities) of strategy space: one is larger (finer) and one is smaller (coarser). We find that constraining the EWA models by using levels (granules) within the limits of working memory allows for a better characterization of the data based on individual differences in WMC. Using this level-reinforcement version of EWA learning, also referred to as the EWA rule learning model, we find that working memory capacity can significantly affect learning behavior. Our likelihood ratio test rejects the null that subjects with high WMC and subjects with low WMC follow the same EWA learning model. In addition, the parameter corresponding to ‘counterfactual thinking ability’ is found to be reduced when working memory capacity is low. [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]

This paper presents a formal definition for a number of distinct cases where the preposition “ em ” (in) can be applied in Brazilian Portuguese. The aim of this work is to establish a mathematical model using qualitative spatial reasoning formalisms within the idea of precisification from supervaluation semantics. Our long term goal is to implement this model into artificial intelligent systems to allow for seamless communication with humans in a common speech. [ABSTRACT FROM AUTHOR]

In creative problem solving, humans perform object replacement and object composition to improvise tools in order to carry out tasks in everyday situations. In this paper, an approach to perform Object Replacement and Object Composition ( OROC ) inside a Creative Cognitive framework ( CreaCogs ) is proposed. Multi-feature correspondence is used to define similarity between objects in an everyday object domain. This enables the cognitive system OROC to perform creative replacement of objects and creative object composition. The generative properties of OROC are analysed and proof-of-concept experiments with OROC are reported. An evaluation of the results is carried out by human judges and compared to human performance in the Alternative Uses Test. [ABSTRACT FROM AUTHOR]

Neural computation has an influential role in the study of human capacities and behaviors. It has been the dominant approach in the vision science of the last half century, and it is currently one of the fundamental methods of investigation for most higher cognitive functions. Yet, neurocomputational approaches to moral behavior are lacking. Computational modeling in general has been scarcely pursued in morality, and existent non-neural attempts have failed to account for the mental processes involved in morality. In this paper we argue that recently the situation has evolved in a way that subverted the insufficient knowledge on the basic organization of moral cognition in brain circuits, making the project of modeling morality in neurocomputational terms feasible. We will present an original architecture that combines reinforcement learning and Hebbian learning, aimed at simulating forms of moral behavior in a simple artificial context. The relationship between language and morality is controversial. In the analytic tradition of philosophy, morality is essentially the language of morals. On the other side, current cognitive ethology has shown how non human species display behaviors that are surprisingly similar to those prescribed by human ethics. Nevertheless, morality in humans is deeply entrenched with language, and the semantics of words like ‘wrong’ resists consensual explanations. The model here proposed includes an auditory processing pathway, with the purpose of showing how the coding of “wrong”, even if highly simplified with respect to its rich content in natural language, can emerge in the course of moral learning. [ABSTRACT FROM AUTHOR]

This paper describes a computational model named Dev E-R ( Developmental Engagement-Reflection ) that, inspired by Piaget’s theory, simulates the assimilation-accommodation adaptation process. It is implemented with a new extended version of the computational model of creativity known as Engagement-Reflection . That is, this model simulates adaptation as a creative activity. We introduce here the implementation of our model on an agent that is initialized with basic reflex conducts and that through the interaction with a 3D virtual world, it is able to build new behaviors autonomously. The new acquired skills, according to Piaget’s theory, are typically observed in children that have reached the second substage of the sensorimotor period. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to interpret and formalize Herbert Simon’s cognitive notions of bounded rationality, satisficing and heuristics in terms of computability theory and computational complexity theory. Simon’s theory of human problem solving is analyzed in the light of Turing’s work on Solvable and Unsolvable Problems. It is suggested here that bounded rationality results from the fact that the deliberations required for searching computationally complex spaces exceed the actual complexity that human beings can handle. The immediate consequence is that satisficing becomes the general criterion of decision makers and heuristics are the procedures used for achieving their goals. In such decision problems, it is demonstrated that bounded rationality and satisficing are more general than orthodox, non-cognitive, Olympian rationality and optimization, respectively, and not the other way about. [Copyright &y& Elsevier]

Abstract: Computational cognitive modeling has been established as a useful methodology for exploring and validating quantitative theories about human cognitive processing and behavior. In some cases, however, complex models can create challenges for parameter exploration and estimation due to extended execution times and limited computing capacity. To address this challenge, some modelers have turned to intelligent search algorithms and/or large-scale computational resources. For an emerging class of models, epitomized by attempts to predict the time course effects of cognitive moderators, even these techniques may not be sufficient. In this paper, we present a new methodology and associated software that allows modelers to instantiate a model proxy that can quickly interpolate predictions of model performance anywhere within a defined parameter space. The software integrates with the R statistics environment and is compatible with many of the fitting algorithms therein. To illustrate the utility of these capabilities, we describe a case study where we are using the methodology in our own research. [Copyright &y& Elsevier]

Abstract: This paper describes how personality may be explained by a generic, comprehensive computational cognitive architecture. We show that a cognitive architecture by itself can serve as a generic model of personality, without any significant addition or modification. A cognitive architecture can capture the fundamental invariance within an individual in terms of behavioral inclinations as well as the inevitable variability of behaviors. Various tests and simulations have been conducted within the cognitive architecture that show that such a model is reasonably stable, is relatively flexible (in terms of person–situation interactions), captures some major personality traits (e.g., the Five-Factor Model), and accounts for a variety of empirical data. The work shows the feasibility and usefulness of integrating personality modeling with generic computational cognitive modeling (i.e., cognitive architectures). [Copyright &y& Elsevier]

Abstract: In the classical music tradition, knowing how to read music is an essential skill and is seen as a fundamental component to develop when learning to play the piano. This research’s focus is to study the possible impact of two different teaching approaches to the acquisition of initial reading skills. By using cognitive modeling, we are hoping to observe through computer simulation the problem solving and decision-making tasks involved in decoding a simple musical score. Our model intends to capture a novice initial coordination of music reading and motor operations on a piano keyboard. As such, it does not aim at modelling advanced sight-reading skills. The paper introduces the Middle-C and Intervallic methods followed by a description of an ACT-R cognitive model and simulation results upon learning with each of the reading methods. Inspection of the simulation results reveals differences in terms of declarative memory and cognitive processing demands. In particular, the Intervallic method requires a larger number of declarative knowledge related to notes, and more execution planning than the Middle-C method. [Copyright &y& Elsevier]

Abstract: This paper introduces a new vision module, called PAAV, developed for the cognitive architecture ACT-R. Unlike ACT-R’s default vision module that was originally developed for top-down perception only, PAAV was designed to model a wide range of tasks, such as visual search and scene viewing, where pre-attentive bottom-up processes are essential for the validity of a model. PAAV builds on attentive components of the default vision module and incorporates greater support for modeling pre-attentive components of human vision. The module design incorporates the best practices from existing models of vision. The validity of the module was tested on four different tasks. [Copyright &y& Elsevier]

Abstract: Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science”, which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems. [Copyright &y& Elsevier]

Abstract: In a cognitive architecture, intelligent behavior is contingent upon the use of an appropriate abstract representation of the task. When designing a general-purpose cognitive architecture, two basic challenges related to abstraction arise, which are introduced and examined in this article. The perceptual abstraction problem results from the difficulty of creating a single perception system able to induce appropriate abstract representations in any task the agent might encounter, and the irreducibility problem arises because some tasks are resistant to being abstracted at all. The first contribution of this paper is identifying these problems, and the second contribution is showing a means to address them. This is accomplished through the use of mental imagery. To support imagery, a concrete (highly detailed) representation of the spatial state of the problem is maintained as an intermediate between the external world and an abstract representation. Actions can be simulated (imagined) in terms of this concrete representation, and the agent can derive abstract information by applying perceptual processes to the resulting concrete state. Imagery works to mitigate the perceptual abstraction problem by allowing a given perception system to work in a wider variety of tasks, since perception can be dynamically combined with imagery, and works to mitigate the irreducibility problem by allowing internal simulation of low-level control processes. To demonstrate these benefits, an implementation is described, which is an extension of the Soar architecture. An agent in this architecture that uses reinforcement learning and imagery to play an arcade game and an agent that performs sampling-based motion planning for a car-like vehicle are described, demonstrating the perceptual abstraction and irreducibility problems and the associated use of imagery to mitigate those problems in complex AI tasks. Previous AI systems have incorporated imagery-like processes, however, the functional benefit of imagery in those systems has typically been characterized as the ability to perform more efficient inference through the use of a specialized representation. The use of imagery here shows further benefits related to the perceptual abstraction and irreducibility problems, enriching the broader understanding of the role of imagery in cognitive systems. [Copyright &y& Elsevier]

Abstract: Inadequate sleep affects cognitive functioning, with often subtle and occasionally catastrophic personal and societal consequences. Unfortunately, this topic has received little attention in the cognitive modeling literature, despite the potential payoff. In this paper, we provide evidence regarding the impact of sleep deprivation on a particular component of cognitive performance, the ability to access and use declarative knowledge. Every 2h throughout an extended period of sleep deprivation, participants completed 50 trials of a serial addition/subtraction task requiring knowledge of single-digit arithmetic facts. Over the course of 88h awake, response times increased while accuracy declined. A computational model accounts for the degradation in performance through a reduction in the activation of declarative knowledge. This knowledge is required for successful completion of the serial addition/subtraction task, but access to the declarative knowledge is impaired as sleep deprivation increases and alertness declines. Importantly, the mechanism provides a generalizable quantitative account relevant to other tasks and contexts. It also provides a process-level understanding of how cognitive performance declines with increasing levels of sleep loss. [Copyright &y& Elsevier]

Abstract: Cognitive appraisal theories (CATs) explain human emotions as a result of the subjective evaluation of events that occur in the environment. Recently, arguments have been put forward that discuss the need for formal descriptions in order to further advance the field of cognitive appraisal theory. Formal descriptions can provide detailed predictions and help to integrate different CATs by providing clear identification of the differences and similarities between theories. A computational model of emotion that is based on a CAT also needs formal descriptions specifying the theory on which it is based. In this paper we propose a formal notation for the declarative semantics of the structure of appraisal. We claim that this formalism facilitates both integration of appraisal theories as well as the design and evaluation of computational models of emotion based on an appraisal theory. To support these claims we show how our formalism can be used in both ways: first we integrate two appraisal theories; second, we use this formal integrated model as basis for a computational model after identifying what declarative information is missing in the formal model. Finally, we embed the computational model in an emotional agent, and show how the formal specification helps to evaluate the computational model. [Copyright &y& Elsevier]

Abstract: The data learning problem is a phenomenon that arises when an agent employing a cognitive architecture faces the task of acquiring declarative information from an external source, such as the “answer” to a “question”. Because the agent has to pay attention to both question and answer in order to learn the association between them, it is problematic for the agent to learn to produce the answer in response to the question alone. This observation helps shape the basic characteristics of human memory. The problem was first reported with the Soar architecture, but it arises also in ACT-R, and this paper argues that it will occur in any cognitive architecture, connectionist as well as symbolic, which is specified in a sufficiently explicit manner to avoid having the theorist act as an implicit homunculus for the agent. [Copyright &y& Elsevier]

An increasing body of evidence has shown that attention is a multi-type and multilevel cognitive faculty. The dominant computational modeling approaches to attention have often focused on one specific type of attention at one specific level. In particular, various connectionist modeling techniques at the subsymbolic level have been widely adopted. In this paper, we report a symbolic computational model of the Attentional Network Test, which simultaneously involves different types of attention (alerting, orienting, and executive control), each subserved by distinctive attentional networks in the brain. The model was developed in ACT-R, a rule-based cognitive architecture. The results show that the model, by sequentially firing rules at a rate of about one every 40 ms, was able to capture the effect of each attentional network. The model implies that while the attentional networks can be distinguished at both neuroanatomical and behavioral levels, different attentional networks may adopt similar computational operations at least at a symbolic rule level. [Copyright &y& Elsevier]