Facilitatory effects of visual perspective taking level 2 for human and robotic avatars

Visual perspective taking (VPT) is a crucial component of social cognition: when engaging in activities such as preparing meals together, moving a couch together, playing tennis, and discussing our surroundings, the ability to keep track of what others see is essential. In recent years, studies investigated the cognitive mechanisms of VPT not only of other humans but also of artificial agents, such as robots. Thus far, researchers have primarily investigated interference tasks with regard to artificial agents (i.e., how the perspective of an agent interferes with one's own perspective). In the present study, we investigated a task where VPT has a facilitatory effect (i.e., taking the perspective of an agent results in a performance boost) across four experiments, in which we varied the type of the avatar: a human (Exp. 1), a humanoid robot with a human-like head (Exp. 2), a humanoid robot with a camera-like head (Exp. 3), and a camera (Exp. 4). We found significant VPT effects for the human and robot avatar but failed to do so for the robot with a camera-like head and the camera. These findings suggest that participants adopted the perspective of avatars that are either human or resemble a human-like appearance. However, this is not the case for avatars which deviate from a human-appearance or which imply a social presence.

Authors

Institutions

Publication Details

Journal
Acta Psychologica
Published
2026-05-21
DOI
https://doi.org/10.1016/j.actpsy.2026.107085
Primary Topic
Visual perception and processing mechanisms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Facilitatory effects of visual perspective taking level 2 for human and robotic avatars

Basil Wahn, Leda Berio
Acta Psychologica
Visual perception and processing mechanisms
article

Facilitatory effects of visual perspective taking level 2 for human and robotic avatars

Basil Wahn, Leda Berio
article en

Abstract

Visual perspective taking (VPT) is a crucial component of social cognition: when engaging in activities such as preparing meals together, moving a couch together, playing tennis, and discussing our surroundings, the ability to keep track of what others see is essential. In recent years, studies investigated the cognitive mechanisms of VPT not only of other humans but also of artificial agents, such as robots. Thus far, researchers have primarily investigated interference tasks with regard to artificial agents (i.e., how the perspective of an agent interferes with one's own perspective). In the present study, we investigated a task where VPT has a facilitatory effect (i.e., taking the perspective of an agent results in a performance boost) across four experiments, in which we varied the type of the avatar: a human (Exp. 1), a humanoid robot with a human-like head (Exp. 2), a humanoid robot with a camera-like head (Exp. 3), and a camera (Exp. 4). We found significant VPT effects for the human and robot avatar but failed to do so for the robot with a camera-like head and the camera. These findings suggest that participants adopted the perspective of avatars that are either human or resemble a human-like appearance. However, this is not the case for avatars which deviate from a human-appearance or which imply a social presence.

Acta PsychologicaVol. 267
University College Dublin (IE), University Hospitals of the Ruhr-University of Bochum (DE), Bochum University of Applied Sciences (DE), Ruhr University Bochum (DE)
Openalex Percentile: Top 5%
Visual perception and processing mechanisms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.