The male advantage in child facial resemblance detection: Behavioral and ERP evidence

Abstract

Males have been suggested to have advantages over females in reactions to child facial resemblance, which reflects the evolutionary pressure on males to solve the adaptive paternal uncertainty problem and to identify biological offspring. However, previous studies showed inconsistent results and the male advantage in child facial resemblance perception, as a kin detection mechanism, is still unclear. Here we investigated the behavioral and brain mechanisms underlying the self-resembling faces processing and how it interacts with sex and age using ERP technique. The results showed a stable male advantage in self-resembling child faces processing, such that males have higher detectability to self-resembling child faces than females. For ERP results, males showed smaller N2 and larger LPC amplitudes for self-resembling child faces, which may reflect face-matching and self-referential processing in kin detection, respectively. Further source analysis showed that the N2 and LPC components were originated from the anterior cingulate cortex and medial frontal gyrus, respectively. Our results support the male advantage in self-resembling child detection and further indicate that such distinctions can be found in both early and late processing stages in the brain at different regions.

Emotional conflict in facial expression processing during scene viewing: An ERP study

“…The beloved faces as non-novel stimuli decreased the face identity conflict (Langeslag et al, 2007). Wu et al (2013) found males showed smaller N2 amplitudes for self-resembling child faces, which may reflect the conflict monitoring of the ACC during kin detection. In previous studies regarding facial expressions, the N2 was found to be larger for neutral faces as compared to faces with emotional expressions.…”

“…Therefore, the most negative peak was defined as being between 200 and 300 ms for the fronto-central N2, and the peak amplitudes and latencies were measured at this time window for the N2 component. Consistent with the analyzing method in previous studies, the peak amplitudes were analyzed for N2 component (e.g., Chen et al, 2012;Wu et al, 2013). Additionally, regarding the late positive potential (LPP), since in many participants, the LPP peak was not easily discernible, the analysis was based on the mean amplitude calculated between 450 and 600 ms (based on the grand-average waveforms).…”

“…While in previous studies the focus has been on investigating the emotional congruency effects on early visual ERPs components, especially on N170 responses which reflect facial structural encoding, it is not yet clear whether the integration of facial expressions and emotional information from visual scene is reflected in the longer latency ERPs components, especially N2 which is related to conflict monitoring and LPP (Late Positive Potential) which reflects motivated attention. Previous studies have shown that the fronto-central N2 (emerging around 220-350 ms after stimulus onset) originating from the anterior cingulated cortex (ACC) was associated with conflict monitoring in face processing (Wu et al, 2013). The previous studies found that strangers' faces elicited increased N2 than famous faces (Nessler et al, 2005), selffaces (Sui et al, 2009) or beloved faces (Langeslag et al, 2007), which revealed face identity conflict.…”

Sensitivity to Kinship: From Electrophysiological Perspective

“…Previous findings indicated that LPC or P3 was highly sensitive to the process of stimulus evaluation, response execution [33] and motivational significance [16]. Wu and Yang et al [28] suggested that LPC may reflect self-referential processing in kin detection.…”

On the usefulness of color for kinship verification from face images

“…Kinship verification has been traditionally studied in psychology and neuroscience aiming at understanding the resemblance of faces of the same family [1,2,3]. To the best of our knowledge, the first attempt to conduct automatic kinship verification based on face images is described in the work of Fang et al [4].…”