Your body gives away what you think
Is it possible to predict what people think by looking at their body signals? In a new study Fairclough and colleagues used a range of psychophysiological measures (such as EEG, ECG, and skin conductance) while asking their subjects to perform a demanding task. The researchers were able to use the psychophysiological measures to predict how […]
Is it possible to predict what people think by looking at their body signals? In a new study Fairclough and colleagues used a range of psychophysiological measures (such as EEG, ECG, and skin conductance) while asking their subjects to perform a demanding task. The researchers were able to use the psychophysiological measures to predict how much people felt engaged in the task and how much they were stressed by it. On the other side, they were not able to predict other subjective states such as worry.
Prediction of subjective states from psychophysiology: A multivariate approach
Fairclough et al. in Biological Psychology Volume 71, Issue 1 , January 2006, Pages 100-110
Abstract
Biocybernetic systems utilise real-time changes in psychophysiology in order to adapt aspects of computer control and functionality, e.g. adaptive automation. This approach to system design is based upon an assumption that psychophysiological variations represent implicit fluctuations in the subjective state of the operator, e.g. mood, motivation, cognitions. A study was performed to investigate the convergent validity between psychophysiological measurement and changes in the subjective status of the individual. Thirty-five participants performed a demanding version of the Multi-Attribute Task Battery (MATB) over four consecutive 20-min blocks. A range of psychophysiological data were collected (EEG, ECG, skin conductance level (SCL), EOG, respiratory rate) and correlated with changes in subjective state as measured by the Dundee Stress State Questionnaire (DSSQ). MATB performance was stable across time-on-task; psychophysiological activity exhibited expected changes due to sustained performance. The DSSQ was analysed in terms of three subjective meta-factors: Task Engagement, Distress and Worry. Multiple regression analyses revealed that psychophysiology predicted a substantial proportion of the variance for both Task Engagement and Distress but not for the Worry meta-factor. The consequences for the development of biocybernetic systems are discussed.
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