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Psychophysiological modeling: Current state and future directions


Bach, Dominik R; Castegnetti, Giuseppe; Korn, Christoph W; Gerster, Samuel; Melinscak, Filip; Moser, Tobias (2018). Psychophysiological modeling: Current state and future directions. Psychophysiology, 55(11):e13214.

Abstract

Psychologists often use peripheral physiological measures to infer a psychological variable. It is desirable to make this inverse inference in the most precise way, ideally standardized across research laboratories. In recent years, psychophysiological modeling has emerged as a method that rests on statistical techniques to invert mathematically formulated forward models (psychophysiological models, PsPMs). These PsPMs are based on psychophysiological knowledge and optimized with respect to the precision of the inference. Building on established experimental manipulations, known to create different values of a psychological variable, they can be benchmarked in terms of their sensitivity (e.g., effect size) to recover these values-we have termed this predictive validity. In this review, we introduce the problem of inverse inference and psychophysiological modeling as a solution. We present background and application for all peripheral measures for which PsPMs have been developed: skin conductance, heart period, respiratory measures, pupil size, and startle eyeblink. Many of these PsPMs are task invariant, implemented in open-source software, and can be used off the shelf for a wide range of experiments. Psychophysiological modeling thus appears as a potentially powerful method to infer psychological variables.

Abstract

Psychologists often use peripheral physiological measures to infer a psychological variable. It is desirable to make this inverse inference in the most precise way, ideally standardized across research laboratories. In recent years, psychophysiological modeling has emerged as a method that rests on statistical techniques to invert mathematically formulated forward models (psychophysiological models, PsPMs). These PsPMs are based on psychophysiological knowledge and optimized with respect to the precision of the inference. Building on established experimental manipulations, known to create different values of a psychological variable, they can be benchmarked in terms of their sensitivity (e.g., effect size) to recover these values-we have termed this predictive validity. In this review, we introduce the problem of inverse inference and psychophysiological modeling as a solution. We present background and application for all peripheral measures for which PsPMs have been developed: skin conductance, heart period, respiratory measures, pupil size, and startle eyeblink. Many of these PsPMs are task invariant, implemented in open-source software, and can be used off the shelf for a wide range of experiments. Psychophysiological modeling thus appears as a potentially powerful method to infer psychological variables.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > General Neuroscience
Social Sciences & Humanities > Neuropsychology and Physiological Psychology
Social Sciences & Humanities > Experimental and Cognitive Psychology
Life Sciences > Neurology
Life Sciences > Endocrine and Autonomic Systems
Life Sciences > Developmental Neuroscience
Life Sciences > Cognitive Neuroscience
Life Sciences > Biological Psychiatry
Language:English
Date:November 2018
Deposited On:22 Feb 2019 09:17
Last Modified:29 Jul 2020 09:37
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0048-5772
OA Status:Closed
Publisher DOI:https://doi.org/10.1111/psyp.13209
PubMed ID:30175471

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