Facial Motion Engages Predictive Visual Mechanisms

Abstract

We employed a novel cuing paradigm to assess whether dynamically versus statically presented facial expressions differentially engaged predictive visual mechanisms. Participants were presented with a cueing stimulus that was either the static depiction of a low intensity expressed emotion; or a dynamic sequence evolving from a neutral expression to the low intensity expressed emotion. Following this cue and a backwards mask, participants were presented with a probe face that displayed either the same emotion (congruent) or a different emotion (incongruent) with respect to that displayed by the cue although expressed at a high intensity. The probe face had either the same or different identity from the cued face. The participants' task was to indicate whether or not the probe face showed the same emotion as the cue. Dynamic cues and same identity cues both led to a greater tendency towards congruent responding, although these factors did not interact. Facial motion also led to faster responding when the probe face was emotionally congruent to the cue. We interpret these results as indicating that dynamic facial displays preferentially invoke predictive visual mechanisms, and suggest that motoric simulation may provide an important basis for the generation of predictions in the visual system.

The neuroimaging and genetics of emotion perception in schizophrenia

“…For example, Kilts et al (2003) found that dynamic emotion perception recruited neural correlates implicated in the analysis of emotional information in the animated facial expressions, whereas static emotion perception recruited neural correlates implicated in motor imagery. In addition, compared to static emotional expressions, the animated and changing cues in naturalistic emotional expressions have been found to stimulate greater predictive processing (Kaufman and Johnston, 2014;Palumbo and Jellema, 2013). This is due to the greater uncertainty involved in perceiving dynamic emotions, compared to static emotions, as there is a larger influx of sensory information that is continually evolving and changing over time.…”

“…Static images only represent a single facial pattern within a single modality and fail to capture the dynamic complexity of emotion perception relevant in everyday life. In addition, it is the dynamics of emotion that preferentially incite predictive mechanisms, leading to a greater reliance on prior expectations (Kaufman and Johnston, 2014;Palumbo and Jellema, 2013). Thus, it is of paramount importance that functional Magnetic Resonance Imaging (fMRI) studies, which investigate the influence of prior expectations on emotion perception, incorporate tasks that rely on multimodal sensory information and dynamic emotional presentations.…”

“…For example, Kilts et al (2003) found that dynamic emotion perception recruited neural correlates implicated in the evaluation of emotional messages in the constant changing of facial patterns. More importantly, a number of studies have demonstrated that, compared to static emotional expressions, the changing dynamics in naturalistic emotional expressions stimulate greater predictive processes (Kaufman and Johnston, 2014;Palumbo and Jellema, 2013). An explanation for this phenomenon is that dynamic emotions evolve over time and involve greater input of sensory information.…”

“…Notwithstanding the valuable findings reported by Barbalat et al (2013) and DieguezRisco et al (2015), only static displays of emotion were explored, despite the fact that facial motion preferentially invokes predictive mechanisms leading to prior expectations (Kaufman and Johnston, 2014). As such, utilization of static stimuli in experimental paradigms limits the scope of these results and, more importantly, may confound the predictive processes that are naturally involved in dynamic emotion perception.…”

“…Despite the crucial findings provided by the abovementioned work, the threat stimuli were presented in a form of static images of faces. Evidence shows that the greater complexity of real-life dynamic emotion requires predictive and evaluative processes, which are generated by the greater uncertainty in the constantly moving and changing features (Kaufman and Johnston, 2014;Palumbo and Jellema, 2013).…”