An Agent-Based Intervention to Assist Drivers Under Stereotype Threat: Effects of In-Vehicle Agents' Attributional Error Feedback
For members of a group negatively stereotyped in a domain, making mistakes can aggravate the influence of stereotype threat because negative stereotypes often blame target individuals and attribute the outcome to their lack of ability. Virtual agents offering real-time error feedback may influence p...
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Veröffentlicht in: | Cyberpsychology, behavior and social networking behavior and social networking, 2016-10, Vol.19 (10), p.615-620 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | For members of a group negatively stereotyped in a domain, making mistakes can aggravate the influence of stereotype threat because negative stereotypes often blame target individuals and attribute the outcome to their lack of ability. Virtual agents offering real-time error feedback may influence performance under stereotype threat by shaping the performers' attributional perception of errors they commit. We explored this possibility with female drivers, considering the prevalence of the "women-are-bad-drivers" stereotype. Specifically, we investigated how in-vehicle voice agents offering error feedback based on responsibility attribution (internal vs. external) and outcome attribution (ability vs. effort) influence female drivers' performance under stereotype threat. In addressing this question, we conducted an experiment in a virtual driving simulation environment that provided moment-to-moment error feedback messages. Participants performed a challenging driving task and made mistakes preprogrammed to occur. Results showed that the agent's error feedback with outcome attribution moderated the stereotype threat effect on driving performance. Participants under stereotype threat had a smaller number of collisions when the errors were attributed to effort than to ability. In addition, outcome attribution feedback moderated the effect of responsibility attribution on driving performance. Implications of these findings are discussed. |
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ISSN: | 2152-2715 2152-2723 |
DOI: | 10.1089/cyber.2016.0153 |