Design and implementation of domain-specific cognitive system based on question similarity algorithm

The cognitive system is an artificial intelligence product that integrates natural language processing techniques. The biggest d on logical reasoning, which can help users improve exploration and accelerate discovery. On the basis of natural language processing and logical reasoning, the paper adopts similarity algorithm bas difference between big data analysis systems and cognitive systems is that cognitive systems can directly deal with the problems raised by natural language forms and provide predictions baseded on question sentences, designs a cognitive system for specific domains, and finally realizes it on the NAO robot platform.     

Memory systems within a cognitive architecture

This article addresses the division of memory systems in relation to an overall cognitive architecture. As understanding the architecture is essential to understanding the mind, developing computational cognitive architectures is an important enterprise in computational psychology (computational cognitive modeling). The article proposes a set of hypotheses concerning memory systems from the standpoint of a cognitive architecture, in particular, the four-way division of memory (including explicit and implicit procedural memory and explicit and implicit declarative memory). It then discusses in detail how these hypotheses may be validated through examining qualitatively the literature on memory. A quick review follows of computational simulations of a variety of quantitative data (which are not limited to narrowly conceived “memory tasks”). Results of accounting for both qualitative and quantitative data point to the promise of this approach.     

Classifying and profiling Social Networking Site users: a latent segmentation approach

Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.     

The relation between verbal and visuospatial memory and autobiographical memory

The basic-systems approach (Rubin, 2005, 2006) states that autobiographical memory is supported by other cognitive systems and argues that autobiographical memories are constructed from interactions between cognitive systems, such as language, vision and emotion. Although deficiencies in one or more of the basic systems influence the properties of autobiographical memories, little is known about how these cognitive abilities and autobiographical memory are related. To assert whether participants with stronger cognitive abilities also perform better on autobiographical memory tests, participants who completed verbal and visuospatial memory tests also recorded one personal event, which they recalled after a certain interval. Participants who performed well on the verbal memory tests also had better retention for the personal event, providing support for the basic-systems approach to autobiographical memory and preliminary support for the view that people have more memories from adolescence and early adulthood because the memory system works optimally in these lifetime periods.     

Cognitive predictors of single-digit and procedural calculation skills and their covariation with reading skill

This study examined the extent to which children's cognitive abilities in kindergarten and their mothers' education predict their single-digit and procedural calculation skills and the covariance of these with reading skill in Grade 4. In kindergarten, we assessed children's (N=178) basic number skills, linguistic skills, and visual attention. In Grade 4, we assessed their calculation and reading skills. Data on children's cognitive ability at 5 years of age and their mothers' level of education were also collected. The results showed that both of the core components of calculation, single-digit and procedural calculation, as well as their covariance with reading, were predicted by unique cognitive factors. Fluency in single-digit calculation and text reading shared an underlying cognitive process, that is, the ability to fluently retrieve verbal or visual-verbal associations from long-term memory. In contrast, procedural calculation was predicted not only by single-digit calculation but also by mother's education and conceptual knowledge of numbers. Overall, the results suggest that a multicomponential approach, including a hierarchical relation among various components, is fruitful when trying to understand the development of mathematical skill and its covariation with reading.     

Toward integrating cognitive components with computational models of emotion using software design patterns

Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges such as (i) sharing information among independently developed cognitive and affective components, and (ii) interconnecting complex cognitive and affective components that must interact with one another in order to generate realistic emotions, which may even affect agents’ decision making. This paper proposes an architectural pattern aimed at cataloging and describing fundamental components of CMEs and their interrelationships with cognitive components. In this architectural pattern, external cognitive components and internal affective components of CMEs do not interact directly but are extended by including message exchange methods in order to use a publish-subscribe channel, which enables their intercommunication, thus attenuating issues such as software heterogeneity. This structural approach centralizes communication management and separates the inherent complexity of the cognitive and affective processes from the complexity of their interaction mechanisms. In so doing, it enables the design of CMEs’ architectures composed of independently developed affective and cognitive components. The proposed architectural pattern attempts to make progress in capturing the complex process of human emotions in a software system that adheres to software engineering best practices and that incorporates quality attributes such as flexibility and interoperability.     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.     

注意觉察系统：实现多任务环境下的人机交互

注意觉察系统是以认知心理学和认知工效学为理论基础设计的、能在多任务环境下帮助用户管理任务中断及转移的新型人机交互系统。本文概述了注意觉察系统的三大功能体系,研究现状,并对这种系统的未来发展趋势作了展望。     

Delusions and theories of belief

Cognitive neuropsychiatry is a branch of cognitive psychology that seeks to explain neuropsychiatric symptoms in terms of disruptions or damage to normal cognitive processes. A key objective of this approach is to use insights derived from the study of pathological symptoms to inform accounts of premorbid cognitive systems. Delusions, in particular, can be considered to represent dysfunction of the cognitive processes underlying belief formation, so studying delusions may provide unique insights into nonpathological belief. While this approach has provided compelling accounts for a range of delusions in terms of putative cognitive dysfunctions, it is less clear that it has achieved progress in its reciprocal goal of informing understanding of belief more generally. In this review, we trace the origins of the cognitive neuropsychiatric approach and consider the reasons for the lack of progress. We propose a tentative framework to overcome these challenges and suggest directions for future research.     

How to Exploit Diversity for Scientific Gain: Using Individual Differences to Constrain Cognitive Theory

ABSTRACT— People often show considerable systematic variability in their ability to perform many different cognitive tasks. In this article, we argue that by combining an individual-differences approach with an experimental-cognitive-neuroscience approach one can often further constrain potential theories of the underlying cognitive mechanisms. In support of this proposal, we outline three basic benefits of using an individual-differences approach: validating neurophysiological measures, demonstrating associations among constructs, and demonstrating dissociations among apparently similar constructs. To illustrate these points, we describe recent work by us and other researchers that utilizes each of these techniques to address specific questions within the domain of visual working memory. It is our hope that some of these techniques for utilizing individual variability may be applied to other domains within cognitive neuroscience.     

Application of Cognitive Load Theory to Develop a Measure of Team Cognitive Efficiency

Improving human systems integration through technologically advanced training and performance aids has become increasingly important to military transformation. Measures of improved cognitive and coordination processes arising from the employment of transformational tools are necessary to guide the refinement and future development of such technologies. In this article, we describe a cognitive load theory approach to developing a combinatory measure of individual workload and team performance following an experimental intervention involving training and a decision support system. We discuss how indicators of what we term team cognitive efficiency can improve assessing the effectiveness of transformational processes and technologies.     

Modelling Cognitive Bias in Crowdsourcing Systems

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.     

Approach motivation and cognitive resources combine to influence memory for positive emotional stimuli

Inspired by the elaborated intrusion theory of desire, the current research tested the hypothesis that persons higher in trait approach motivation process positive stimuli deeply, which enhances memory for them. Ninety-four undergraduates completed a measure of trait approach motivation, viewed positive or negative image slideshows in the presence or absence of a cognitive load, and one week later completed an image memory test. Higher trait approach motivation predicted better memory for the positive slideshow, but this memory boost disappeared under cognitive load. Approach motivation did not influence memory for the negative slideshow. The current findings support the idea that individuals higher in approach motivation spontaneously devote limited resources to processing positive stimuli.     

Promotions Spontaneously Induce a Positive Evaluative Response

Research has suggested that conscious cognitive processes mediate positive responses to promotions. In contrast, we find that exposure to promotional stimuli evokes a positive evaluative reaction spontaneously and that this reaction generalizes to products that are evaluated subsequently. Three experiments support our predictions and rule out alternative possibilities. Specifically, we find that promotions spontaneously evoke positive evaluative responses and that these responses mediate liking for a target product. Our findings also identify conditions under which promotion‐generated evaluative responses are transferred to products unrelated to the promotion and indicate the type of product categories that are likely to produce this effect. The theoretical and substantive implications of this research are discussed.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

How to (and how not to) think about top-down influences on visual perception

The question of whether cognition can influence perception has a long history in neuroscience and philosophy. Here, we outline a novel approach to this issue, arguing that it should be viewed within the framework of top-down information-processing. This approach leads to a reversal of the standard explanatory order of the cognitive penetration debate: we suggest studying top-down processing at various levels without preconceptions of perception or cognition. Once a clear picture has emerged about which processes have influences on those at lower levels, we can re-address the extent to which they should be considered perceptual or cognitive. Using top-down processing within the visual system as a model for higher-level influences, we argue that the current evidence indicates clear constraints on top-down influences at all stages of information processing; it does, however, not support the notion of a boundary between specific types of information-processing as proposed by the cognitive impenetrability hypothesis.     

The role of robotic modelling in cognitive science

From the perspective of cognitive robotics, this paper presents a modern interpretation of Newell’s (1973) reasoning and suggestions for why and how cognitive psychologists should develop models of cognitive phenomena. We argue that the shortcomings of current cognitive modelling approaches are due in significant part to a lack of exactly the kind of integration required for the development of embodied autonomous robotics. Moreover we suggest that considerations of embodiment, situatedness, and autonomy, intrinsic to cognitive robotics, provide an appropriate basis for the integration and theoretic cumulation that Newell argued was necessary for psychology to mature. From this perspective we analyse the role of embodiment and modes of situatedness in terms of integration, cognition, emotion, and autonomy. Four complementary perspectives on embodied and situated cognitive science are considered in terms of their potential to contribute to cognitive robotics, cognitive science, and psychological theorizing: minimal cognition and organization, enactive perception and sensorimotor contingency, homeostasis and emotion, and social embedding. In combination these perspectives provide a framework for cognitive robotics, not only wholly compatible with the original aims of cognitive modelling, but as a more appropriate methodology than those currently in common use within psychology.     

Can Implicit Theory Influence Construal Level?

Beliefs about stability and change are captured by individuals’ implicit theories. Incremental‐theorists believe that human traits and world‐dispositions are malleable and can change through effort, whereas entity‐theorists believe that human traits and world‐dispositions are fixed. In this research we find that the implicit theory an individual holds influences an important aspect of the cognitive process, namely, the level of construal at which information is processed. In three studies we demonstrate that, compared to entity‐theorists, incremental‐theorists adopt a more abstract level of information construal, and the increased cognitive flexibility afforded by an incremental‐theory mindset explains this effect.     

Mechanisms for Robust Cognition

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.     

Scaffolding the design of accessible eLearning content: a user-centered approach and cognitive perspective

There exist various guidelines for facilitating the design, preparation, and deployment of accessible eLearning applications and contents. However, such guidelines prevalently address accessibility in a rather technical sense, without giving sufficient consideration to the cognitive aspects and issues related to the use of eLearning materials by learners with disabilities. In this paper we describe how a user-centered design process was applied to develop a method and set of guidelines for didactical experts to scaffold their creation of accessible eLearning content, based on a more sound approach to accessibility. The paper also discusses possible design solutions for tools supporting eLearning content authors in the adoption and application of the proposed approach.