Emotion encoding and decoding from human upper-body movement: The role of acceleration, dimensionless jerk, head...

What kinematic features drive emotion encoding (production or expression of emotion by an expressor) and decoding (perception, interpretation, or recognition of that emotional information by a receiver) in human full-body movement? We used upper-body sensor data from the EMOKINE full-body dataset [Christensen, et al. (2024). EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets. Behavior Research Methods, 56(7), 7498-7542.], to perform a pre-registered, secondary data-analysis, examining this question. Previous work using EMOKINE showed that human observers (N = 132) identify the dancer's intended emotions in full-body movements above chance-level. Yet, no analyses have been presented with regards to which kinematic features contributed to the dancer's encoding of emotional intention, and observers' correct decoding of encoded emotions. The present study examined the role of four of the 12 kinematic features provided with the EMOKINE dataset (acceleration, dimensionless jerk, head tilt, limb contraction), specifically for the upper-body. Finally, in an exploratory analysis, the effect of intended emotional expressivity and kinematic features on aesthetic judgment was examined. Upper-body acceleration, head tilt, and dimensionless jerk were the strongest predictors of emotion encoding, while decoding relied on the dancer's intended emotion, on dimensionless jerk and limb contraction. Aesthetic judgments were only weakly related to upper-body kinematics and depended largely on the dancer's intended emotion at encoding. Results illustrate a possible dissociation between expressive and aesthetic impression, and suggests that recognition and aesthetic responses may draw on partially distinct cognitive mechanisms.