How context influences the interpretation of facial expressions: a source localization high-density EEG study on the “Kuleshov effect”

Abstract

Few studies have explored the specificities of contextual modulations of the processing of facial expressions at a neuronal level. This study fills this gap by employing an original paradigm, based on a version of the filmic “Kuleshov effect”. High-density EEG was recorded while participants watched film sequences consisting of three shots: the close-up of a target person’s neutral face (Face_1), the scene that the target person was looking at (happy, fearful, or neutral), and another close-up of the same target person’s neutral face (Face_2). The participants’ task was to rate both valence and arousal, and subsequently to categorize the target person’s emotional state. The results indicate that despite a significant behavioural ‘context’ effect, the electrophysiological indexes still indicate that the face is evaluated as neutral. Specifically, Face_2 elicited a high amplitude N170 when preceded by neutral contexts, and a high amplitude Late Positive Potential (LPP) when preceded by emotional contexts, thus showing sensitivity to the evaluative congruence (N170) and incongruence (LPP) between context and Face_2. The LPP activity was mainly underpinned by brain regions involved in facial expressions and emotion recognition processing. Our results shed new light on temporal and neural correlates of context-sensitivity in the interpretation of facial expressions.

Time to Face Language: Embodied Mechanisms Underpin the Inception of Face-Related Meanings in the Human Brain

“…2014 ; Berchio et al. 2017 ; Calbi et al. 2019 ), effects were reported only if significant differences lasted at least 20 ms.…”

Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

“…As such, taking into account that cognitive face processing is evolving during the early stages of life, the employment of LORETA has been able to provide insight into the underlying process [84]. In a similar way, using an MNE algorithm, ESI was able to further advance the brain state decoding by deciphering the neural operations of facial recognition [85], while the utilization of the LAURA method and harnessing of the "Kuleshov effect" has effectively highlighted the facial expression cognitive functions [86]. Furthermore, combining ESI with a self-organizing feature map (SOMF) resulted in accurate categorization of emotions perceived from physiognomic components, (up to 91% of classification accuracy) [14].…”

Diferencias de género en la valoración de la intensidad emocional de las expresiones faciales de alegría y tristeza

“…Sin embargo, en este trabajo se valoraron las expresiones faciales respecto a la intensidad mostrada, pero no se midió la capacidad de los participantes para determinar con precisión las diferencias en intensidad entre dos expresiones faciales. Quizá esta sea la clave para contrastar adecuadamente la hipótesis de la sensibilidad de género, en tanto que el reconocimiento de la expresión emocional viene determinado en alto grado por el contexto en el que se produce la percepción de la expresión facial (Calbi et al, 2019).…”