Analysis of Multimodal Physiological Signals Within and Across Individuals to Predict Psychological Threat vs. Challenge

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Khalaf, A., Nabian, M., Fan, M., Yin, Y., Wormwood, J., Siegel, E., PhD, … Ostadabbas, S. (2017, November 19). Analysis of Multimodal Physiological Signals Within and Across Individuals to Predict Psychological Threat vs. Challenge. https://doi.org/10.31234/osf.io/96djs

Challenge and threat are biopsychological responses following an individual's evaluation of task demands relative to his or her available resources to cope with these demands. In this study, we aimed to investigate individual and group variation in physiological responding across a series of motivated performance tasks of varying difficulty. We specifically tested three hypotheses: (H1) individuals will express different sets of physiological patterns (features) across tasks of varying difficulty; (H2) there will be groups of individuals who share common salient physiological features that dominate within-individual differentiation in physiological responding across tasks of varying difficulty; and (H3) the accuracy of predicting self-reported judgments of challenge and threat across individuals will be higher within each group with shared salient physiological features than across all groups or the entire sample. To test these hypotheses, we developed an integrated analytic framework for multimodal physiological data analysis. We employed data from an existing experiment in which participants completed three mental arithmetic tasks of increasing difficulty during which we collected different modalities of physiological data. Analyses revealed three groups of participants who shared common features that best differentiated their within-individual physiological response patterns across tasks. Support vector machine (SVM) classifiers were then trained using both shared features within each group and all computed features to predict challenge vs. threat states. Our results showed that, within-group classification model using person-specific features achieved higher self-report prediction accuracy comparing to the alternative model trained on data from all participants without feature selection.


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