Suchergebnisse

Deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, there has been no definite finding of a univocally "deceptive" signal. This work proposes an approach to deception detection combining cognitive load manipulation and T-pattern methodology with the objective of: (a) testing the efficacy of dual task-procedure in enhancing differences between truth tellers and liars in a low-stakes situation; (b) exploring the efficacy of T-pattern methodology in discriminating truthful reports from deceitful ones in a low-stakes situation; (c) setting the experimental design and procedure for following research. We manipulated cognitive load to enhance differences between truth tellers and liars, because of the low-stakes lies involved in our experiment. We conducted an experimental study with a convenience sample of 40 students. We carried out a first analysis on the behaviors' frequencies coded through the observation software, using SPSS (22). The aim was to describe shape and characteristics of behavior's distributions and explore differences between groups. Datasets were then analyzed with Theme 6.0 software which detects repeated patterns (T-patterns) of coded events (non-verbal behaviors) that regularly or irregularly occur within a period of observation. A descriptive analysis on T-pattern frequencies was carried out to explore differences between groups. An in-depth analysis on more complex patterns was performed to get qualitative information on the behavior structure expressed by the participants. Results show that the dual-task procedure enhances differences observed between liars and truth tellers with T-pattern methodology; moreover, T-pattern detection reveals a higher variety and complexity of behavior in truth tellers than in liars. These findings support the combination of cognitive load manipulation and T-pattern methodology for deception detection in low-stakes situations, suggesting the testing of directional hypothesis on a larger probabilistic sample of population ; The authors are gratefully acknowledge the support of two Spanish government projects (Ministerio de Economía y Competitividad): (1) La Actividad Física y el Deporte Como Potenciadores del Estilo de Vida Saludable: Evaluación del Comportamiento Deportivo Desde Metodologías No Intrusivas [Grant No. DEP2015-66069-P, MINECO/FEDER, UE]; (2) Avances Metodológicos y Tecnológicos en el Estudio Observacional del Comportamiento Deportivo [Grant No. PSI2015-71947-REDP, MINECO/FEDER, UE]. In addition, they thank the support of the Generalitat de Catalunya Research Group, GRUP DE RECERCA I INNOVACIÓ EN DISSENYS (GRID). Tecnología i Aplicació Multimedia i Digital als Dissenys Observacionals [Grant No. 2017 SGR 1405]. Lastly, MTA also acknowledge the support of University of Barcelona (Vice-Chancellorship of Doctorate and Research Promotion). ; Peer Reviewed

AbstractEconomic analysis assumes that consumer behaviour can be rationalised by a utility function. Previous research has shown that some consistency of choices with economic rationality can be captured by permanent cognitive ability. No other known study however has examined how a temporary load in subjects' working memory can affect economic rationality. Using two controlled laboratory experiments, we exogenously vary cognitive load by asking subjects to memorise a number while they undertake an induced budget allocation task (Choi et al., 2007a, b). Using a number of manipulation checks, we verify that cognitive load has adverse effects on subjects' performance in reasoning tasks. However, we find no effect in any of the goodness-of-fit measures that measure consistency of subjects' choices with the Generalized Axiom of Revealed Preference (GARP), despite having a sample size large enough to detect even small differences between treatments with 80% power. We also find no effect on first-order stochastic dominance and risk preferences. Our finding suggests that economic rationality can be attained even when subjects are placed under temporary working memory load, despite the fact that the load has adverse effects in reasoning tasks.

AbstractDoes exposure to cognitive load affect key properties of economic behavior? In this experiment, subjects face a series of simple binary decision tasks between prospects, testing for monotonicity in monetary payments, consistency with (first-order) stochastic dominance, reduction of compound lotteries, risk attitudes, and ambiguity attitudes. Cognitive load is manipulated via simultaneous memory tasks. Our data show treatment differences resulting from cognitive load for decision tasks with risky outcomes. However, cognitive load has no impact on monotonicity and ambiguity attitudes. Under a dual-process view of human decision-making, our findings suggest that ambiguity attitudes and preferences for "more certain money" are intuitive, not reasoned.

In a tachistoscopic dual-task study, the foveal load of a primary task was varied, and subjects were required to report the orientation of a simultaneously presented peripheral line that could occur at anyone of six retinal locations (3, 6, or 9 deg from central fixation in either the left or right visual field). Overall peripheral (secondary) task accuracy was significantly superior for subjects given a low foveal load that required naming a letter drawn from a set of two alternatives as compared with the accuracy of subjects who had to choose a foveal response from among six alternatives. Foveal load interacted strongly with retinal eccentricity. Accuracy of peripheral line-orientation discrimination deteriorated much more rapidly with increases in retinal eccentricity for the high-foveal-load group than for the lowfoveal-load group. These results are discussed in terms of a tunnel-vision model.

Cognitive manufacturing utilizes cognitive computing, the industrial Internet of things (IoT), and advanced analytics to upgrade manufacturing processes in manners that were not previously conceivable. It enables associations to improve major business measurements, for example, productivity, product reliability, quality, and safety, while decreasing downtime and lowering costs. Considering all the facts that can prejudice the manufacturing performance in Industry 4.0, the cognitive load has received more attention, since it was previously neglected with respect to manufacturing industries. This paper aims to investigate what causes cognitive load reduction in manufacturing environments, i.e., human–computer interaction technologies that reduce the identified causes and the applications of cognitive manufacturing that use the referred technologies. Thus, a conceptual framework that links cognitive manufacturing to a reduction of the cognitive load was developed. ; This research was funded by the project 026653|POCI-01-0247-FEDER-026653—INDTECH—New technologies for smart manufacturing, co-financed by the Portugal 2020 Program (PT 2020), Compete 2020 Program, and the European Union through the European Regional Development Fund (ERDF). The authors wish to thank the relevant bodies for the opportunity and financial support that permitted carrying out this project: Fundação para a Ciência e Tecnologia (FCT) and C-MAST (Center for Mechanical and Aerospace Science and Technologies), under project UIDB/00151/2020. ; info:eu-repo/semantics/publishedVersion

Introduction -- Elements of difficulty in introductory accounting -- Human cognitive architecture -- Cognitive load theory and instructional application -- Self-management of cognitive load: background and research hypotheses -- Experimental design and task description -- Experiments and results -- Implications of the findings, limitations and suggestions for future research

Abstract
All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.

One of the key findings concerning how groups process information is the 'common knowledge effect'—information shared by many group members plays a larger role in group process and performance than unshared information. However, in an information rich environment, assigning all information to all members may overload each member's cognitive capabilities. Thus, the question addressed here is how to optimally assign information to group members. Following a model proposed by Zajonc and Smoke (1959), we assessed the effects of information assignment redundancy and group interaction on group memory performance. Participants in five-person groups either received a full list of consonant-verb-consonant non-word trigrams to memorize, or a partial list with each trigram distributed to two group members. Groups recalled the trigrams as either coacting or interacting groups. In terms of correct recall, coacting groups outperformed interacting groups, and partial redundancy produced better recall than total redundancy. However, intrusion errors were greatly reduced by group interaction and/or a reduction in the cognitive load on the individual group members (i.e. partial redundancy). Groups in the partial redundancy condition tended to perform near optimal levels. Implications for future research on information distribution in groups are discussed.

Over the last decade, text messages have become one of most popular forms of communication. A diverse array of software to enable the exchange of text messages has been developed and deployed on computers and smart phones which is one of reasons for the increased use of text messages for communication purposes. However, there are several disadvantages for communicators when using text messages. For example, in the text-chat environment, there is a lack of trust between communicators (Bos et al., 2002) and communicators may face a high cognitive load (Thirunarayanan et al., 2002). Therefore, we conducted several studies to improve communication between people in the text-chat environment where important elements for communication, such as facial expressions, do not exist. We examined various data collected from people with different levels of interpersonal trust and cognitive load, including chat contents (e.g., the number of assent words used), mouse movements (e.g., distance travelled by the mouse) and physiological signals (skin response). These data proved to be useful indicators for measuring the level of trust of the communicators and the cognitive load to which the communicators were exposed. These findings have implications for enhancing the communication and relationships between communicators in a text-chat environment and will assist system developers to design applications to measure the conditions the communicators are under when they use the text-chat environment to complete tasks in a business or government context and provide them with suitable assistance.In addition to using the previously mentioned data to measure trust and cognitive load, we further examined the text-chat environment by allowing communicators to see the actions of their partners in real time when they were trying to solve tasks. Our results show that when communicators are able to see the behaviors of their partners when they chat in the text-chat environment while completing tasks, it improves the level of interpersonal trust between them. Finally, in this thesis, we examined the effects of cooperation and cognitive load on interpersonal trust. The findings show that trust is significantly improved between communicators in the text-chat environment when the communicators act in a cooperative way and when they are under a low cognitive load.