Integrating a cognitive computational model of planning and decision-making considering affective information

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.     

Team situation awareness within the context of human-autonomy teaming

Effective team communication, a fundamental part of team coordination, is crucial for both effective Team Situation Awareness (TSA) and team performance. In this study, we looked at the role that team interaction (i.e., more specifically team verbal behaviors) played in TSA and team performance in order to better understand Human-Autonomy Teaming (HAT). We first analyzed team verbal behaviors (i.e., pushing and pulling information) across conditions of human-autonomy teams and human-human teams, and then analyzed their relationship with TSA and team performance via Growth Curve Modelling (GCM). Good teamwork involves anticipating the needs of teammates and that means pushing information before it is requested. Therefore, if things are going well, there should be little need for pulling information. In this study’s task, participants were instructed to push information to others, and over time master the specific timing of information sharing to the intended recipient. Findings indicate that pushing information was positively associated with TSA and team performance, and human-autonomy teams had lower levels of both pushing and pulling information than all-human teams. Through this study, we have learned that anticipation of other team member behaviors and information requirements in human-autonomy teams are important for effective TSA and team performance. In order to make HAT more effective in terms of teamwork, we need to develop mechanisms to enhance pushing information within HAT.     

Virtual personalities: Using computational modeling to understand within-person variability

How can the same underlying psychological/neurobiological system result in both stable between-individual differences and high levels of within-individual variability in personality states over time and situations? We argue that both types of variability result from a psychological system based on structured, chronic motivations, where behavior at a specific point in time is a joint function of the current availability of motive affordances in the situation, current motivationally relevant bodily or interoceptive states, and the result of the competition among alternative active motives. Here we present a biologically-based theoretical framework, embodied in two different computational models, that shows how individuals with stable personality characteristics, can nevertheless exhibit considerable within-person variability in personality states across time and situations.     

小麦中烟曲霉生长温度与水分活度预测模型的建立

为快速预测和监控引起小麦霉变的烟曲霉的生长,在15～40℃温度及0.70～0.96水分活度下进行小麦储藏模拟实验,建立和验证烟曲霉D6生长的一级和二级预测模型。结果表明,一级生长模型中Bo-ltsmann模型优于Logistic模型。以Boltsmann模型拟合数据建立二级模型,采用平方根模型拟合,得烟曲霉D6的最低生长温度和水分活度分别为5.784℃和0.721。经验证,平方根模型能较好描述烟曲霉D6最大比生长速率与温度、水分活度之间的对应关系。     

Quelles méthodes et quelle modélisation d’analyse de l’activité de travail de l’encadrement ? L’exemple de l’encadrement des chantiers de la construction

By referring to the ergonomic analysis of work and to the management sciences, the authors propose an analysis model of the managers’ activity. Until then, few researchs were realized in ergonomics science with this population which has to prepare and to organize the works of other operators. According to the authors, it is possible to apply to this population the model of “the centre de décision”. It allows to understand how their activity is determined by “upstream” centres and then how it determines the activity of centres “approval”. Applied to the project and site managers, this model brings solutions to transform their work and the work of the workmen.     

视觉表象生成系统及其影响因素

视觉表象是在记忆基础上产生的类似知觉的信息表征, 也是人们在日常生活中不可或缺的心理活动之一。本综述介绍了该领域中比较有影响力的表象计算理论模型, 并进一步归纳了视觉表象生成系统的成分及其对应的神经基础。分析结果强调了初级视觉皮层作为“视觉缓冲器”是生成表象的主要机能结构, 以“视觉缓冲器”为基础的表象生成系统的机能受多重因素影响。本综述有助于对视觉表象生成系统形成全面的认识, 促进视觉表象实质的进一步研究。     

Impulsivity and reward sensitivity within the pen model: A test of discriminant hypotheses

Impulsivity and reward sensitivity are often treated as interchangeable constructs. This conflation may be traced back to Gray’s interpretation of impulsivity as the trait level manifestation of the Behavioral Activation System (BAS), that mediates sensitivity to rewards. In this study (N = 696) we evaluated discriminant hypotheses regarding the relationships between BAS-mediated reward sensitivity, impulsivity and Eysenck’s PEN superfactors. We hypothesized that reward sensitivity would be mainly linked with Extraversion, and that impulsivity would be primarily connected with Psychoticism. Hypotheses were tested using structural equation modelling and equality constraints tests. Results mostly supported our expectations: reward sensitivity was more strongly related to Extraversion than to the other PEN superfactors, while impulsivity was more strongly related to Psychoticism and Neuroticism. Implications for BAS and impulsivity research are discussed.     

BEAUTY IN THE LIVING WORLD

Almost all admit that there is beauty in the natural world. Many suspect that such beauty is more than an adornment of nature. Few in our contemporary world suggest that this beauty is an empirical principle of the natural world itself and instead relegate beauty to the eye and mind of the beholder. Guided by theological and scientific insight, the authors propose that such exclusion is no longer tenable, at least in the data of modern biology and in our view of the natural world in general. More important, we believe an empirical aesthetics exists that can help guide experimental design and development of computational models in biology. Moreover, because theology and science can both contribute toward and equally profit from such an aesthetics, we propose that this empirical aesthetics provides the foundation for a living synergy between theology and science.     

Bayesian journey-to-crime modelling of single and multiple crime-type series in Baltimore County,MD

This study tested different Bayesian Journey-to-Crime (JTC) models on a sample group of 850 serial offenders apprehended in Baltimore County, MD from 1993 to 1997. In this research, Bayesian JTC models were being used to predict the home locations of the offenders. The sample group data included 133 assaults, 90 burglaries, 497 larcenies, 81 robberies, and 49 vehicle thefts. The main question this research aimed to answer was whether the addition of crimes of a different type to an existing crime series of a single type would result in more accurate and/or precise Bayesian JTC models. The standard practice by law enforcement has been to consider the same-type crime series only when modelling the anchor point of the offender. Similarly, in research, geographic profiles have been constructed exclusively with the same-type crime series. The results of this study clearly indicated that the inclusion of crimes of a different type into a single crime-type series will result in significantly more accurate and more precise Bayesian JTC models. In contrast, crime series with predominantly assault and burglary showed results that were inconclusive or indicated no significant differences. These results should encourage law enforcement agencies to re-evaluate their standard practice of constructing geographic profiles with only the same-type crime series. Copyright © 2009 John Wiley & Sons, Ltd.