Journal
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 1, Issue 2, Pages 109-118Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/T-AFFC.2010.12
Keywords
Affective computing; arousal classification; affect recognition; virtual reality; Stroop task; Yerkes-Dodson Law
Funding
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0757414] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [911009] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1029373] Funding Source: National Science Foundation
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A closed-loop system that offers real-time assessment and manipulation of a user's affective and cognitive states is very useful in developing adaptive environments which respond in a rational and strategic fashion to real-time changes in user affect, cognition, and motivation. The goal is to progress the user from suboptimal cognitive and affective states toward an optimal state that enhances user performance. In order to achieve this, there is need for assessment of both 1) the optimal affective/cognitive state and 2) the observed user state. This paper presents approaches for assessing these two states. Arousal, an important dimension of affect, is focused upon because of its close relation to a user's cognitive performance, as indicated by the Yerkes-Dodson Law. Herein, we make use of a Virtual Reality Stroop Task (VRST) from the Virtual Reality Cognitive Performance Assessment Test (VRCPAT) to identify the optimal arousal level that can serve as the affective/cognitive state goal. Three stimuli presentations (with distinct arousal levels) in the VRST are selected. We demonstrate that when reaction time is used as the performance measure, one of the three stimuli presentations can elicit the optimal level of arousal for most subjects. Further, results suggest that high classification rates can be achieved when a support vector machine is used to classify the psychophysiological responses (skin conductance level, respiration, ECG, and EEG) in these three stimuli presentations into three arousal levels. This research reflects progress toward the implementation of a closed-loop affective computing system.
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