Abstract
Different affective states have previously been shown to have specific kinematic signatures, for example, sad movements tend to be slow, and angry movements are fast and accelerated. We likely use these kinematic signatures to help us to perceive affective states in others. If so, our models of the kinematic-affective state correspondence may be based upon experience of our own actions. We therefore predicted that first, altering the kinematic properties of affective movements should reduce the perceived intensity of the correct affective state, and second, perceived intensity would be influenced by participants' own action kinematics. To test these hypotheses, affect perception was measured by asking typical adult participants to rate on a visual analogue scale the extent to which happy, angry, sad and neutral point-light walkers expressed happiness, anger and sadness. The affective animations were either un-manipulated (100%), or were altered to bring the velocity closer to that of the neutral walker (66%, 33% and 0%, with 0% being equivalent to neutral velocity). The kinematics of the participant's leg were also measured during a walking task. In confirmation of our first hypothesis, participants' intensity ratings of the correct affective state decreased linearly as the velocity information pertaining to affective states was removed. Second, there was a linear correlation between participants' own walking kinematics and their perceptual judgements. Faster walkers gave the slower affective states (sadness) higher intensity ratings than the faster affective states (anger), and the slower walkers showed the opposite pattern. Therefore, we may rate the intensity of an affective state according to its kinematic difference from our own neutral movement kinematics. These results suggest that kinematics communicate important information about our affective states, and that individuals calibrate their perception of others' affective states relative to their own reference kinematics.
Meeting abstract presented at VSS 2016