Taking the perspectives of many people: Humanization matters

Embodied Cross-Modal Interactions Based on an Altercentric Reference Frame

Accurate comprehension of others' thoughts and intentions is crucial for smooth social interactions, wherein understanding their perceptual experiences serves as a fundamental basis for this high-level social cognition. However, previous research has predominantly focused on the visual modality when investigating perceptual processing from others' perspectives, leaving the exploration of multisensory inputs during this process largely unexplored. By incorporating auditory stimuli into visual perspective-taking (VPT) tasks, we have designed a novel experimental paradigm in which the spatial correspondence between visual and auditory stimuli was limited to the altercentric rather than the egocentric reference frame. Overall, we found that when individuals engaged in explicit or implicit VPT to process visual stimuli from an avatar's viewpoint, the concomitantly presented auditory stimuli were also processed within this avatar-centered reference frame, revealing altercentric cross-modal interactions.

Human-like interactions prompt people to take a robot's perspective

The increasing need for human-robot interaction requires not only robots to understand how humans think, but also humans to understand robots. Interestingly, little attention has been given to how humans interpret robots' behaviors. In this study, we adopted a social mental rotation task and investigated whether socially engaging behaviors could influence how people take a robot's perspectives. In a real lab, two android robots with neutral appearance sat opposite each other by a table with conflicting perspectives. Before the participant started the experiment, one of the robots behaved more interactively than the other by showing more socially engaging behaviors. Then the participant was required to identify rotated normal or mirrored digits presented inbetween the two robots. Results revealed a significant interactive effect between the digits type (normal; mirrored) and robot type (interactive; noninteractive). When digits were oriented to the interactive robot, we found a larger RT difference between normal and mirrored digits. In general, these findings suggested that robots' interactive behaviors could influence how people spontaneously consider the robot's perspective. Future studies may further consider how interactive behaviors can shape human-robot relationships and facilitate human-robot interaction.

Shared social groups or shared experiences? The effect of shared knowledge on children's perspective-taking

Although the ability to consider others' visual perspectives to interpret ambiguous communication emerges during childhood, people sometimes fail to attend to their partner's perspective. Two studies investigated whether 4- to 6-year-olds show a "closeness-communication bias" in their consideration of a partner's perspective in a communication task. Participants played a game that required them to take their partner's visual perspective in order to interpret an ambiguous instruction. If children, like adults, perform worse when they overestimate the extent to which their perspective is aligned with that of a partner, then they should make more perspective-taking errors when interacting with a socially close partner compared with a more socially distant partner. In Study 1, social closeness was based on belonging to the same social group. In Study 2, social closeness was based on caregiving, a long-standing social relationship with a close kinship bond. Although social group membership did not affect children's consideration of their partner's perspective, children did make more perspective-taking errors when interacting with a close caregiver compared with a novel experimenter. These findings suggest that close personal relationships may be more likely to lead children to overestimate perspective alignment and hinder children's perspective-taking than shared social group membership, and they highlight important questions about the mechanisms underlying the effects of partner characteristics in perspective-taking tasks.

Manipulating avatar age and gender in level-2 visual perspective taking

Visual perspective taking (VPT) represents how the world appears from another person's position. The age, group status and emotional displays of the other person have been shown to affect task performance, but tasks often confound social and spatial outcome measures by embedding perspective taking in explicitly social contexts or theory-of-mind reasoning. Furthermore, while previous research has suggested that visual perspective taking may be impacted by avatar characteristics, it is unknown whether this is driven by general group processing or a specific deficit in mentalizing about outgroups, for example, children. Therefore, using a minimally social task (i.e., the task was not communicative, and acknowledging the "mind" of the avatar was not necessitated), we examined whether avatar age and avatar gender affect performance on simpler (low angular disparity) and more effortful, embodied (high angular disparity) perspective judgments. Ninety-two participants represented the visuospatial perspectives of a boy, girl, man, or woman who were presented at various angular disparities. A target object was placed in front of the avatar and participants responded to the orientation of the object from the avatar's position. The findings suggest that social features of visuospatial perspective taking (VSPT) are processed separately from the fundamental spatial computations. Further, Level-2 VSPT appears to be affected by general group categorization (e.g., age and gender) rather than a deficit in mentalizing about a specific outgroup (e.g., children).

Human but not robotic gaze facilitates action prediction

Do people ascribe intentions to humanoid robots as they would to humans or non-human-like animated objects? In six experiments, we compared people’s ability to extract non-mentalistic (i.e., where an agent is looking) and mentalistic (i.e., what an agent is looking at; what an agent is going to do) information from gaze and directional cues performed by humans, human-like robots, and a non-human-like object. People were faster to infer the mental content of human agents compared to robotic agents. Furthermore, although the absence of differences in control conditions rules out the use of non-mentalizing strategies, the human-like appearance of non-human agents may engage mentalizing processes to solve the task. Overall, results suggest that human-like robotic actions may be processed differently from humans’ and objects’ behavior. These findings inform our understanding of the relevance of an object’s physical features in triggering mentalizing abilities and its relevance for human–robot interaction.

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People differently ascribe mental content to human-like and non-human-like agents

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A human-like shape may automatically engage mentalizing processes

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Human actions are interpreted faster than non-human actions