Stability of value perception: minimal influence of framing on moral attributions to a humanoid robot

As robots are becoming collaborators in various domains, understanding if humans perceive them as moral and intentional agents is pivotal. This study investigated the malleability of human attributions of value alignment and intentionality to a humanoid robot (iCub). We designed an experiment to test whether brief experimental framing strategies could influence these perceptions. Three experimental groups were exposed to different strategies regarding background information concerning the robot’s moral-decision making abilities: a no Information group; a verbal description group; and a social interaction (or video) group, where the robot reacted to moral decision-making movie scenes. Our results showed that our manipulations did not produce the expected differences in perceived robot–human value alignment or intentionality, suggesting that participants’ initial impressions of the robot are robust and not easily shifted by brief experimental framing. Furthermore, attributions of intentionality remained generally high regardless of the informational strategy employed. On the contrary, our exploratory findings indicate a small, significant correlation between participants’ own pre-existing moral orientations and their perceptions of the robot’s values. These findings suggest that moral impressions of complex artificial agents are less malleable than anticipated. They highlight that pre-existing moral frameworks shape how humans interpret artificial agents.

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Publication Details

Journal
Scientific Reports
Published
2026-01-09
DOI
https://doi.org/10.1038/s41598-025-28831-8
Primary Topic
Psychology of Moral and Emotional Judgment
Type
article
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article

Stability of value perception: minimal influence of framing on moral attributions to a humanoid robot

Serena Marchesi, Kinga Ciupińska, Davide De Tommaso, Agnieszka Wykowska
Scientific Reports
Psychology of Moral and Emotional Judgment
article

Stability of value perception: minimal influence of framing on moral attributions to a humanoid robot

Serena Marchesi, Kinga Ciupińska, Davide De Tommaso, Agnieszka Wykowska
article en

Abstract

As robots are becoming collaborators in various domains, understanding if humans perceive them as moral and intentional agents is pivotal. This study investigated the malleability of human attributions of value alignment and intentionality to a humanoid robot (iCub). We designed an experiment to test whether brief experimental framing strategies could influence these perceptions. Three experimental groups were exposed to different strategies regarding background information concerning the robot’s moral-decision making abilities: a no Information group; a verbal description group; and a social interaction (or video) group, where the robot reacted to moral decision-making movie scenes. Our results showed that our manipulations did not produce the expected differences in perceived robot–human value alignment or intentionality, suggesting that participants’ initial impressions of the robot are robust and not easily shifted by brief experimental framing. Furthermore, attributions of intentionality remained generally high regardless of the informational strategy employed. On the contrary, our exploratory findings indicate a small, significant correlation between participants’ own pre-existing moral orientations and their perceptions of the robot’s values. These findings suggest that moral impressions of complex artificial agents are less malleable than anticipated. They highlight that pre-existing moral frameworks shape how humans interpret artificial agents.

Scientific ReportsVol. 16(1)
Jagiellonian University (PL), Italian Institute of Technology (IT), Institute of Psychology (PL)
Peace, Justice and strong institutions
Openalex Percentile: Top 92%
Psychology of Moral and Emotional Judgment
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Stability of value perception: minimal influence of framing on moral attributions to a humanoid robot — Serena Marchesi, Kinga Ciupińska, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS