It’s a Match: Task Assignment in Human–Robot Collaboration Depends on Mind Perception

Eva Wiese, Patrick P. Weis*, Yochanan Bigman, Kyra Kapsaskis, Kurt Gray

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Robots are becoming more available for workplace collaboration, but many questions remain. Are people actually willing to assign collaborative tasks to robots? And if so, exactly which tasks will they assign to what kinds of robots? Here we leverage psychological theories on person-job fit and mind perception to investigate task assignment in human–robot collaborative work. We propose that people will assign robots to jobs based on their “perceived mind,” and also that people will show predictable social biases in their collaboration decisions. In this study, participants performed an arithmetic (i.e., calculating differences) and a social (i.e., judging emotional states) task, either alone or by collaborating with one of two robots: an emotionally capable robot or an emotionally incapable robot. Decisions to collaborate (i.e., to assign the robots to generate the answer) rates were high across all trials, especially for tasks that participants found challenging (i.e., the arithmetic task). Collaboration was predicted by perceived robot-task fit, such that the emotional robot was assigned the social task. Interestingly, the arithmetic task was assigned more to the emotionally incapable robot, despite the emotionally capable robot being equally capable of computation. This is consistent with social biases (e.g., gender bias) in mind perception and person-job fit. The theoretical and practical implications of this work for HRI are being discussed.

Original languageAmerican English
Pages (from-to)141-148
Number of pages8
JournalInternational Journal of Social Robotics
Volume14
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Anthropomorphic robots
  • Human-likeness
  • Human–robot interaction
  • Social robots
  • Trust

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