Steady State Visually Evoked Potential Correlates of Static and Dynamic Emotional Face Processing

Abstract

While the neural regions associated with facial identity recognition are considered to be well defined, the neural correlates of non-moving and moving images of facial emotion processing are less clear. This study examined the brain electrical activity changes in 26 participants (14 males M = 21.64, SD = 3.99; 12 females M = 24.42, SD = 4.36), during a passive face viewing task, a scrambled face task and separate emotion and gender face discrimination tasks. The steady state visual evoked potential (SSVEP) was recorded from 64-electrode sites. Consistent with previous research, face related activity was evidenced at scalp regions over the parieto-temporal region approximately 170 ms after stimulus presentation. Results also identified different SSVEP spatio-temporal changes associated with the processing of static and dynamic facial emotions with respect to gender, with static stimuli predominately associated with an increase in inhibitory processing within the frontal region. Dynamic facial emotions were associated with changes in SSVEP response within the temporal region, which are proposed to index inhibitory processing. It is suggested that static images represent non-canonical stimuli which are processed via different mechanisms to their more ecologically valid dynamic counterparts.

Brain synchronization during perception of facial emotional expressions with natural and unnatural dynamics

“…By considering them together, these results show that static stimuli were processed more strongly at early latencies, whereas natural dynamic stimuli entailed more prolonged processing towards later latencies as expected. (Note that the delta and lower theta PLI response to the static -unlike the natural —FEEs is back to baseline after 700 ms–see Figure A panel (a) of the S1 File) This conclusion agrees with previous studies [22,28] and supports the hypothesis of [19] that prolonged rather than stronger activations could explain the stronger fMRI and PET responses to dynamic FEEs over their static counterparts [17–20]. Indeed, PLI and, even more, WP at low frequencies are far from showing stronger activations for natural dynamic than for static stimuli in our results, especially before 800 ms.…”

“…We anticipated that in the comparison of natural dynamic versus static FEEs, we would find differences mainly in the timing of the perceptual process, in line with the previous findings of [28], as static FEEs depict the “full-blown” expression at the very first video frame, whereas the natural dynamic FEEs develop gradually over time. We therefore expected stimulus-driven activity and task-relevant activations, as captured by PLI and WP respectively, to be higher for static FEEs at early latencies, but more prolonged towards later latencies for natural dynamic FEEs.…”

“…From a different perspective, converging evidence from a study on a brain lesion patient [24], a PET study [25], a Macaque fMRI study [26], a Transcranial Magnetic Stimulation (TMS) study [27] and a Steady State Visually Evoked Potential (SSVEP) study [28] suggests that perception of static and dynamic FEEs does not differ only in a quantitative manner, but also relies on qualitatively different processes. In particular, the recognition of dynamic FEEs is thought to involve the perception of overt motion, whereas static FEE processing may involve motor imagery to compensate for the missing motion.…”

“…In particular, the recognition of dynamic FEEs is thought to involve the perception of overt motion, whereas static FEE processing may involve motor imagery to compensate for the missing motion. Moreover, the SSVEP study [28] also found that static FEEs are processed at earlier latencies than dynamic FEEs, i.e. the associated processes differ in their timing as well.…”

Robust sensitivity to facial identity in the right human occipito-temporal cortex as revealed by steady-state visual-evoked potentials

“…SSVEP has been primarily used to study the brain's sensitivity to low-level properties of visual stimuli (contrast, phase, line orientation, spatial frequencies, motion, e.g., Ales & Norcia, 2009;Braddick, Wattam-Bell, & Atkinson, 1986;Campbell & Maffei, 1970;Heinrich & Bach, 2003;Tyler & Kaitz, 1977;see Regan, 1989), spatial and selective attention (e.g., Andersen, Müller, & Hillyard, 2009;Morgan, Hansen, & Hillyard, 1996), and figure-ground segregation (e.g., Appelbaum, Wade, Pettet, Vildavski, & Norcia, 2008;Appelbaum, Wade, Vildavski, Pettet, & Norcia, 2006). A few recent studies have also used SSVEPs with high-level visual stimuli and showed modulation of the SSVEP amplitude with the affective content of pictures (Keil et al, 2003), object familiarity (Kaspar, Hassler, Martens, Trujillo-Barreto, & Gruber, 2010), as well as to static and dynamic facial expressions (Mayes, Pipingas, Silberstein, & Johnston, 2009). However, to the best of our knowledge, none of these studies or other studies have attempted to use this method to address the issue of how (individual) faces are coded in the human brain.…”