Influence of User Personality Traits and Attitudes on Interactions With Social Robots: Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
Social robots are robots that can interact and communicate with people in accordance with social norms. They are increasingly implemented in various environments including healthcare, education and the service industry. Individual differences, such as personality traits and attitudes are drivers of human social behaviours and interactions. As robots are increasingly developed as social agents, the drive to develop more socially acceptable, user-centered robots calls for a synthesis of existing findings to improve our understanding how user traits and attitudes influence human-robot interactions (HRI). Understanding the role of individual differences, and their impact on lived experience, is crucial for designing interactions that are better tailored to users. Currently, it is unclear whether or how personality traits and user attitudes affect HRI, which interaction modalities are being investigated and what is the quality of existing evidence. To address these questions, we conducted a systematic search of the literature, yielding 56 articles, from which we extracted relevant findings. As some of the studies included qualitative outcomes, we used a mixed methods meta-aggregation, in which findings were grouped into categories to form more general synthesized findings. We found evidence that user personality traits and attitudes are indeed correlated with social HRI outcomes, including extraversion being associated with preferred distance from the robot, preference for similar robot personality traits, users’ impressions of robots and behavior towards robots. Our analysis also revealed that existing evidence has limitations which prevent us from drawing unambiguous conclusions, such as disparate interaction outcome measures, lack of comparison between different robots and small sample sizes. We provide a comprehensive summary of the existing evidence and propose that these findings can guide the development of research hypotheses to extend knowledge and to provide clarification where the existing literature is ambiguous or contradictory. Findings that warrant future investigation include different preferred robot behaviours based on extroversion and introversion, the impact of user traits on perceived robot anthropomorphism and social presence of the robot.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it