Effect of brain stimulation on mechanisms of social cognition is modulated by individual preferences for human versus robot agents. (November 2019)

• Record Type: Journal Article
• Authors: Abubshait, Abdulaziz; Wiese, Eva
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 63:Part 1(2019)
• Page Start: 858
• Page End: 862
• Publication Date: 2019-11
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181319631359
• Languages: English
• ISSNs: 1541-9312

Abstract

When we interact with others, we use nonverbal behavior such as changes in gaze direction to make inferences about what people think or what they want to do next – a process called mentalizing . Previous studies have shown that how we react to others' gaze signals depends on how much "mind" we ascribe to the gazer, and that this process of mind perception is related to activation in brain areas that process social information (i.e., social brain ). Although brain stimulation studies have identified prefrontal structures like the ventromedial prefrontal cortex (vmPFC) as the potential neural substrate through which mind perception modulates social-cognitive processes like attentional orienting to gaze cues (i.e., gaze following), little is known about whether and how individual differences in preferences for human versus robot agents modulate this relationship. To address this question, the current study examines how transcranial direct current stimulation (tDCS) of left prefrontal versus left temporo-parietal areas affects attentional orienting to gaze signals as a function of the participants' preferences for human ( Human Gaze Followers, HGF) versus robot ( Robot Gaze Followers ; RGF) agents at baseline (prior to brain stimulation). Results show that prefrontal (but not temporo-parietal) stimulation positively affected attentional orienting to gaze signals for HGFs for the human but not the robot gazer; RGFs showed no effect of brain stimulation in neither of the stimulation conditions. These findings inform how preferences for human versus nonhuman agent types can influence subsequent interactions and communications in human-robot interaction.