# Do We View Robots as We Do Ourselves? Examining Robotic Face Processing Using EEG

## TL;DR

This study uses brain activity to explore how humans process robotic faces compared to human faces, revealing differences in neural responses.

## Contribution

The study provides new insights into the neural mechanisms of robotic face processing using ERP components.

## Key findings

- Robotic faces elicited increased early and mid-latency ERP amplitudes compared to human faces.
- Emotional valence differences were observed in P300 and P600 components, supporting dual-stage face processing models.

## Abstract

Background/Objectives: The ability to perceive and process emotional faces quickly and efficiently is essential for human social interactions. In recent years, humans have started to interact more regularly with robotic faces in the form of virtual or real-world robots. Neurophysiological research regarding how the brain decodes robotic faces relative to human ones is scarce and, as such, warrants further research to explore these mechanisms and their social implications. Methods: This study uses event-related potentials (ERPs) to examine the neural correlates during an emotional face categorization task involving human and robotic stimuli. We examined differences in brain activity elicited by viewing robotic and human faces expressing both happy and neutral emotions. ERP waveforms’ amplitudes for the P100, N170, P300, and P600 components were calculated and compared. Furthermore, mass univariate analysis of ERP waveforms was carried out to explore effects not limited to brain regions previously reported in the literature. Results: Results showed robotic faces evoked increased waveform amplitudes at early components (P100 and N170) as well as at the later P300 component. Further, only mid-latency and late cortical components (P300 and P600) showed amplitude differences resulting from emotional valences, aligning with dual-stage models of face processing. Conclusions: These results advance our understanding of face processing during human–robot interaction and contribute to our understanding of brain mechanisms underlying interactions when viewing social robots, setting new considerations for their use in brain health settings and broader cognitive impact.