Psychophysiological modeling
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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.
in: PSYCHOPHYSIOLOGY, Jahrgang 55, Nr. 11, 11.2018, S. e13214.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Review › Forschung
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TY - JOUR
T1 - Psychophysiological modeling
T2 - Current state and future directions
AU - Bach, Dominik R
AU - Castegnetti, Giuseppe
AU - Korn, Christoph W
AU - Gerster, Samuel
AU - Melinscak, Filip
AU - Moser, Tobias
N1 - © 2018 Society for Psychophysiological Research.
PY - 2018/11
Y1 - 2018/11
N2 - 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.
AB - 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.
KW - Autonomic Nervous System
KW - Humans
KW - Models, Theoretical
KW - Psychophysiology/methods
U2 - 10.1111/psyp.13209
DO - 10.1111/psyp.13209
M3 - SCORING: Review article
C2 - 30175471
VL - 55
SP - e13214
JO - PSYCHOPHYSIOLOGY
JF - PSYCHOPHYSIOLOGY
SN - 0048-5772
IS - 11
ER -
