Simulating the Emergence of Task Rotation

In work groups, task rotation may decrease the negative consequences of boredom and lead to a better task performance. In this paper we use multi agent simulation to study several organisation types in which task rotation may or may not emerge. By looking at the development of expertise and motivation of the different agents and their performance as a function of self-organisation, boredom, and task rotation frequency, we describe the dynamics of task rotation. The results show that systems in which task rotation emerges perform better than systems in which the agents merely specialise in one skill. Furthermore, we found that under certain circumstances, a task that leads to a high degree of boredom was performed better than a task causing a low level of boredom.Organisation, Task Rotation, Work Groups, Psychological Theory, Multi Agent Simulation

Aerospace Medicine and Biology: A continuing bibliography (supplement 160)

This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976

Cholinergic Modulation of Attention.

Rodent studies indicate that cholinergic inputs to frontoparietal cortex play an important role in signal detection, especially in challenging conditions. fMRI studies have likewise shown frontoparietal activity in humans under task conditions parallel to those used in the rodent studies. While these parallels are suggestive, the degree to which the fMRI activation patterns seen in humans reflect cholinergic activity remains unknown. The studies in this dissertation provide stronger evidence for cholinergic influences on the brain systems supporting attention in humans, and begin to delineate how those influences may differ by brain region and interact with other (e.g. dopaminergic) influences to shape cognition and behavior. First, an electroencephalography study showed that gamma synchronization, which previous studies have linked to cholinergic activity and attentional control, increases in response to a distractor challenge. Furthermore, across participants, greater increases in gamma synchronization in parietal cortex were associated with better distractor resistance, whereas greater increases in gamma dispersion in right prefrontal cortex were associated with greater response time variations thought to reflect difficulty in maintaining consistent control. Another series of experiments leveraged variability in cholinergic integrity (measured using PET) in Parkinson’s patients as a natural experiment to determine cholinergic contributions to different aspects of attention and cognitive control. Thalamic cholinergic integrity made the strongest independent contribution to variation in the ability to detect signals under perceptual challenge, whereas cortical cholinergic integrity was the best independent predictor of the ability to resist content-rich distractors likely to draw attention away from the target signal. Exploratory analyses suggested that parietal cholinergic integrity might play an especially important role in resisting these distractors, consistent with the electroencephalography study results. Finally, a secondary data analysis of a larger sample suggested that in conditions making strong demands on executive control, there may be mutual compensation between cholinergic and dopaminergic systems. To summarize, the present findings provide further evidence for cholinergic contributions to frontoparietal brain systems supporting signal detection, attention, and cognitive control, more precisely define the contributions of thalamic, prefrontal, and parietal inputs, and suggest the possibility of mutual compensation with the dopaminergic system in situations of high executive demand.PhDPsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111387/1/kaminkim_1.pd

Risk Starvation Contributes to Dementias and Depressions: Whiffs of Danger Are the Antidote

This paper’s objective is to use SKAT, the author’s Stages of Knowledge Ahead Theory of risk, to shed fresh light on the treatment and prevention of mental disorders. SKAT employs a broad definition of risk that allows for nice – not merely nasty – possibilities. SKAT is here shown to solve eight epidemiological puzzles left unexplained by our current theories and associated treatments for the demented and depressed. SKAT does so by enabling a decision model of mental health that puts centre stage why people (and other soft-wired animals) have brains – to make decisions under risk. To make good decisions (be healthy), brains need exercise. Brains get beneficial exercise from what the paper terms “whiffs of danger”, namely sets of risks with the characteristics that the risks are 1) tiny, 2) varied, and 3) frequent. Brains deteriorate when there are shortfalls in such risk exercise. The paper terms such shortfalls “risk starvation”. Those lacking a history of whiffs find normal mishaps too stressful and frequently become depressed. A lot of time with an inadequate amount of whiffs generates the endemic co-morbidity of becoming demented as well as depressed. Socio-economic cultural changes such as the introduction of unemployment benefits and old age pensions and increasing protection of women and children have had the beneficial effects of removing big challenges and big dangers and thus of prolonging physical longevity. But these changes also removed the tiny challenges and tiny dangers formerly faced by those sub-groups in the population identified as more prone to depressions and dementias. Unintentionally, these sub-groups thus were deprived of whiffs of danger, and suffered from risk starvation. In both drug and psychotherapeutic stress research and treatments of the depressed and demented, there should be injections of whiffs of danger to enhance the likelihood of enduring improvements. It is unkind and dangerous for people’s brains to be treated with drugs while maintaining the modern socioeconomic culture of coddling parents and coddling college / university student counsellors, coddling unemployment benefits and coddling old age pensions. These coddles need to be complemented with whiffs of danger, tiny varied chances and challenges. These whiffs of danger need to be introduced in three forms: eliciting social security recipients’ whiffs of danger in the form of little obligations to help the community; educating the poor and other sub-groups that believe closeting females at home endangers their mental health; and educating parents on the damage from overprotection. Overprotection prevents children from becoming inoculated against depression with sensible hope developed over a childhood in which they were allowed to experience numerous failures, not merely numerous successes from parents too closely engineering their environment. Research is required on the likely role of risk starvation in mental disorders other than dementias and depressions and in some physical illnesses.stress; whiffs of danger; decision; dementia; depression; risk starvation; risk; learning; hope; fear; risk-based emotions

Cognitive and Affective Mechanisms of Immersive Virtual Reality Learning Environments

Students and educators value the potential use of immersive virtual reality (IVR) in the classroom to teach academic content as it may increase interest and motivation to learn, which in turn may increase learning outcomes. However, one criticism is that features of IVR, such as extraneous sounds, animations, and interactions, that are not relevant to the content of the lesson, as well as the affective arousal associated with the use of IVR, may be distracting to learning processes. To examine this distraction hypothesis, two experiments were conducted in which students viewed a biology (Experiment 1) or history (Experiment 2) lesson either in IVR or in a desktop lesson containing the same content, with or without practice questions. In both experiments, students who viewed the lesson on the desktop in a PowerPoint (Experiment 1) or an interactive video (Experiment 2) outperformed those who viewed the IVR lessons on transfer tests. The desktop lessons led to higher cognitive engagement based on EEG measures in both experiments, and less self-reported extraneous cognitive load in Experiment 1. Participants also reported more high-arousal positive emotions after the IVR lessons in both experiments, and experienced higher physiological arousal based on heart-rate measures after the IVR lessons compared to the desktop lessons in Experiment 2. The same pattern of results was found when adjunct practice questions were or were not included in the lessons. Across both experiments, mediation analyses suggest that the negative relationship between instructional media and learning outcomes can be explained by self-reported cognitive processing or emotional arousal, particularly for retention test performance. Overall, immersive environments may create high emotional arousal and cognitive distraction during learning, which leads to poorer learning outcomes than desktop environments

Validation of fNIRS System as a Technique to Monitor Cognitive Workload

CognitiveWorkload (CW) is a key factor in the human learning context. Knowing the optimal amount of CW is essential to maximise cognitive performance, emerging as an important variable in e-learning systems and Brain-Computer Interfaces (BCI) applications. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising avenue of brain discovery because of its easy setup and robust results. It is, in fact, along with Electroencephalography (EEG), an encouraging technique in the context of BCI. Brain- Computer Interfaces, by tracking the user’s cognitive state, are suitable for educational systems. Thus, this work sought to validate the fNIRS technique for monitoring different CW stages. For this purpose, we acquired the fNIRS and EEG signals when performing cognitive tasks, which included a progressive increase of difficulty and simulation of the learning process. We also used the breathing sensor and the participants’ facial expressions to assess their cognitive status. We found that both visual inspections of fNIRS signals and power spectral analysis of EEG bands are not sufficient for discriminating cognitive states, nor quantify CW. However, by applying machine learning (ML) algorithms, we were able to distinguish these states with mean accuracies of 79.8%, reaching a value of 100% in one specific case. Our findings provide evidence that fNIRS technique has the potential to monitor different levels of CW. Furthermore, our results suggest that this technique allied with the EEG and combined via ML algorithms is a promising tool to be used in the e-learning and BCI fields for its skill to discriminate and characterize cognitive states.O esforço cognitivo (CW) é um factor relevante no contexto da aprendizagem humana. Conhecer a quantidade óptima de CW é essencial para maximizar o desempenho cognitivo, surgindo como uma variável importante em sistemas de e-learning e aplicações de Interfaces Cérebro-Computador (BCI). A Espectroscopia Funcional de Infravermelho Próximo (fNIRS) emergiu como uma via de descoberta do cérebro devido à sua fácil configuração e resultados robustos. É, de facto, juntamente com a Electroencefalografia (EEG), uma técnica encorajadora no contexto de BCI. As interfaces cérebro-computador, ao monitorizar o estado cognitivo do utilizador, são adequadas para sistemas educativos. Assim, este trabalho procurou validar o sistema de fNIRS como uma técnica de monitorização de CW. Para este efeito, adquirimos os sinais fNIRS e EEG aquando da execução de tarefas cognitivas, que incluiram um aumento progressivo de dificuldade e simulação do processo de aprendizagem. Utilizámos, ainda, o sensor de respiração e as expressões faciais dos participantes para avaliar o seu estado cognitivo. Verificámos que tanto a inspeção visual dos sinais de fNIRS como a análise espectral dos sinais de EEG não são suficientes para discriminar estados cognitivos, nem para quantificar o CW. No entanto, aplicando algoritmos de machine learning (ML), fomos capazes de distinguir estes estados com exatidões médias de 79.8%, chegando a atingir o valor de 100% num caso específico. Os nossos resultados fornecem provas da prospecção da técnica fNIRS para supervisionar diferentes níveis de CW. Além disso, os nossos resultados sugerem que esta técnica aliada à de EEG e combinada via algoritmos ML é uma ferramenta promissora a ser utilizada nos campos do e-learning e de BCI, pela sua capacidade de discriminar e caracterizar estados cognitivos

General dynamics of the physical-chemical systems in mammals

Biodynamic regulator chain models for physical chemical systems in mammal