Reciprocity and its neurological correlates in human-agent cooperation

Jiahao Li, Shen Dong, Erin K. Chiou, Jie Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Reciprocal cooperation is prevalent in human society. Understanding human reciprocal cooperation in human-agent interaction can help design human-agent systems that promote cooperation and joint performance. Studies have found that people reciprocate cooperative behavior when interacting with computer agents in social dilemma games. However, few studies have investigated human reciprocal cooperation with agents in complex dynamic environments. This article examines the behavioral and neurological patterns of reciprocal cooperation in a hospital management microworld experiment. The participants (n = 30) work with both high- and low-cooperation computer agents to share resources to cope with dynamic demands. Participants' resource sharing behaviors were recorded and their prefrontal cortex (PFC) activation was measured using functional near-infrared spectroscopy (fNIRS) technology. Similar to previous studies conducted with participants in the United States, results demonstrate that participants in China showed reciprocal cooperation behaviors with the agents. Specifically, participants share more resources and achieve higher performance when working with a high-cooperation agent than with a low-cooperation agent. A high activation level is detected in the right dorsolateral PFC when working with a high-cooperation agent. Other PFC activation patterns imply that cooperation could be unnecessarily mentally taxing in certain situations. These findings suggest that human cooperativeness in human-agent systems can be calibrated by an agent's cooperation behavior. System designers should design for appropriate cooperativeness and avoid the inefficient use of system resources. Neurological measures could be a useful tool to investigate the mental process in human-agent cooperation.
Original languageEnglish (US)
Pages (from-to)384-394
JournalIEEE Transactions on Human-Machine Systems
Volume50
Issue number5
DOIs
StatePublished - Jun 9 2020

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