Exploiting Coarse Grained Parallelism in Conceptual Data Mining

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Neurophysiological responses during cooking food associated with different emotions

Neurophysiological correlates of affective experience could potentially provide continuous information about a person’s experience when cooking and tasting food, without explicitly verbalizing this. Such measures would be helpful to understand people’s implicit food preferences and choices. This study examined for the first time the relation between neurophysiological variables and affective experiences under real cooking and tasting circumstances, using ingredients that were a priori expected to evoke different affective reactions. 41 participants cooked and tasted two stir-fry dishes in random order following an identical, strictly timed protocol. Once the main ingredient was chicken and the other time mealworms. EEG, ECG and skin potential were recorded continuously. Participants scored subjective valence and arousal after each cooking and tasting session. Frontal EEG alpha asymmetry showed the expected effect throughout the whole cooking and tasting session, consistent with ‘approach’ motivation for chicken and ‘avoidance’ for mealworms. Skin potential effects differed between cooking intervals but were in the expected direction. ECG variables showed an interaction with order of cooking the different dishes. Based on EEG alpha asymmetry, ECG and skin potential variables, we can estimate with 82% accuracy whether a single participant is preparing a dish with mealworms or with chicken. Our study provides evidence that it is possible to estimate experienced emotion during real-life cooking and tasting. We argue that it is important to consider that different neurophysiological and subjective measures reflect different underlying affective processes, to map them out more precisely, and to take advantage of these differences

Modeling the Cognitive Task Load and Performance of Naval Operators

Abstract. Operators on naval ships have to act in dynamic, critical and highdemand task environments. For these environments, a cognitive task load (CTL) model has been proposed as foundation of three operator support functions: adaptive task allocation, cognitive aids and resource feedback. This paper presents the construction of such a model as a Bayesian network with probability relationships between CTL and performance. The network is trained and tested with two datasets: operator performance with an adaptive user interface in a lab-setting and operator performance on a high-tech sailing ship. The “Naïve Bayesian network ” tuned out to be the best choice, providing performance estimations with 86 % and 74 % accuracy for respectively the lab and ship data. Overall, the resulting model nicely generalizes over the two datasets. It will be used to estimate operator performance under momentary CTL-conditions, and to set the thresholds of the load-mitigation strategies for the three support functions

Longitudinal changes in acylated versus unacylated ghrelin levels may be involved in the underlying mechanisms of the switch in nutritional phases in Prader-Willi syndrome

Introduction: Prader-Willi syndrome (PWS) is characterized by a switch from failure to thrive to excessive weight gain and hyperphagia in early childhood. An elevated, more unfavorable ratio between acylated and unacylated ghrelin (AG/UAG ratio) might play a role in the underlying mechanisms of this switch. We aimed to assess the evolution of the appetite-regulating hormones acylated ghrelin (AG) and unacylated ghrelin (UAG) and the AG/UAG ratio and their association with the change in eating behavior in children with PWS, compared to healthy age-matched controls. Methods: A longitudinal study was conducted in 134 children with PWS and 157 healthy controls, from the Netherlands, France, and Belgium. Levels of AG and UAG and the AG/UAG ratio were measured and nutritional phases as reported for PWS were scored. Results: The AG/UAG ratio was lower in the first years of life in PWS than in controls and started to increase from the age of 3 years, resulting in a high-normal AG/UAG ratio compared to controls. The AG levels remained stable during the different nutritional phases (p = 0.114), while the UAG levels decreased from 290 pg/mL in phase 1a to 137 pg/mL in phase 2b (p < 0.001). The AG/UAG ratio increased significantly from 0.81 in phase 2a to 1.24 in phase 2b (p = 0.012). Conclusions: The change from failure to thrive to excessive weight gain and hyperphagia in infants and children with PWS coincides with an increase in AG/UAG ratio. The increase in AG/UAG ratio occurred during phase 2a, thus before the onset of hyperphagia

Cognitive task load in a naval ship control centre : from identification to prediction

Deployment of information and communication technology will lead to further automation of control centre tasks and an increasing amount of information to be processed. A method for establishing adequate levels of cognitive task load for the operators in such complex environments has been developed. It is based on a model distinguishing three load factors: time occupied, task-set switching, and level of information processing. Application of the method resulted in eight scenarios for eight extremes of task load (i.e. low and high values for each load factor). These scenarios were performed by 13 teams in a high-fidelity control centre simulator of the Royal Netherlands Navy. The results show that the method provides good prediction of the task load that will actually appear in the simulator. The model allowed identification of under- and overload situations showing negative effects on operator performance corresponding to controlled experiments in a less realistic task environment. Tools proposed to keep the operator at an optimum task load are (adaptive) task allocation and interface support. Keywords: Mental load; Task analysis; Human-computer interaction; Cognitive engineering; Task allocation; Ship control centr

Task based interpretation of operator state information for adaptive support

In the last 2 decades, the application of new technologies in process control caused a radical change in the role of the operator. To cope with these changes, the operator needs personalized support that can vary over time. To realize this support, we present the design and first testing of a framework for adaptive support. First an analysis was done which data is necessary for the framework. After selection, data was collected during an experiment. Based on this data a software tool was designed to visualize task load of operators over time. Evaluation of the tool shows that combination of cognitive task load, effort and performance gives insight into the task load of an operator over time. Furthermore, it enables generation of critical and optimal task load work areas. Further implementation of the framework needs to be done to generate a real time adaptation plan