Mental State Attribution to Robots: A Systematic Review of Conceptions, Methods, and Findings

Abstract

The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far exhibit considerable diversity in terms of how the phenomenon is described and how it is approached from a theoretical and methodological standpoint. This literature review addresses the need for a shared scientific understanding of mental state attribution to robots by systematically and comprehensively collating conceptions, methods, and findings from 155 empirical studies across multiple disciplines. The findings of the review include that: (1) the terminology used to describe mental state attribution to robots is diverse but largely homogenous in usage; (2) the tendency to attribute mental states to robots is determined by factors such as the age and motivation of the human as well as the behavior, appearance, and identity of the robot; (3) there is a computer < robot < human pattern in the tendency to attribute mental states that appears to be moderated by the presence of socially interactive behavior; (4) there are conflicting findings in the empirical literature that stem from different sources of evidence, including self-report and non-verbal behavioral or neurological data. The review contributes toward more cumulative research on the topic and opens up for a transdisciplinary discussion about the nature of the phenomenon and what types of research methods are appropriate for investigation.

Development and validation of the Attribution of Mental States Questionnaire (AMS-Q): A reference tool for assessing anthropomorphism

“…Overall, these results are in line with previous studies ( Hackel et al, 2014 ; Martini et al, 2016 ; Di Dio et al, 2018 , 2020a , b ; Manzi et al, 2020b ) reporting that different agents, or even the same agent with different characteristics (e.g., different types of robots; Manzi et al, 2020b ), can evoke different – although diminished – attributions of human mental traits. Importantly, the tendency to attribute mental states to robots is also determined by factors such as people’s age, motivation, cultural background, and attitude toward robots, as well as the behavior, appearance, and identity of the robot ( Marchesi et al, 2019 ; Thellman et al, 2022 ). Likewise, in a recent study, Manzi et al (2021c) showed that humans are particularly sensitive to the design of robots in terms of attribution of mental qualities; in fact, even when robots differ slightly in their physical appearance, the dissimilar design evokes different mental properties.…”

“…In addition, regardless of religious background, preschoolers attribute qualities such as omniscience to the mind of God, thus perceiving God’s mind at a higher epistemic level than humans’ minds ( Nyhof and Johnson, 2017 ; Di Dio et al, 2018 ). Several studies have focused also on the attribution of minds to robotic agents (for a review, see Thellman et al, 2022 ) and observed that adults are more inclined to ascribe greater mental states to robots characterized by human-like physical features ( Dario et al, 2001 ; MacDorman et al, 2005 ; Kiesler et al, 2008 ; Krach et al, 2008 ; Bartneck et al, 2009 ; Fink et al, 2012 ; Gray and Wegner, 2012 ; Airenti, 2015 ; Złotowski et al, 2015 ; Thellman et al, 2017 ; Wiese and Weis, 2020 ; Manzi et al, 2020b , 2021a , b , c ). This tendency has also been found in children over the age of five, who are likely to attribute more mental states to robots with more human-like features; in contrast, younger children tend to anthropomorphize by giving less importance to the human aspect of the robotic agent ( Di Dio et al, 2020a , b ; Manzi et al, 2020b ).…”

Tools and methods to study and replicate experiments addressing human social cognition in interactive scenarios.

“…Recently, several authors explored how humans interpret the behaviors of a robot and whether they refer to mental states in their explanations and predictions of robot behaviors (Thellman & Ziemke, 2020; for a review see Thellman et al, 2022). In a similar vein, Marchesi and colleagues (Marchesi et al, 2019) have developed the InStance Test (IST) to probe adoption of the intentional stance towards robots.…”

Of children and social robots

“…), and biological (e.g., Is the robot alive?). A recent meta-analysis revealed that age is a factor in whether children attribute mental states to robots (Thellman, de Graaf, & Ziemke, 2022). Although the findings were mixed, most studies reported that people of all ages attribute mental states to robots.…”