Effects of Human—Machine Competition on Intent Errors in a Target Detection Task

Abstract

Objective: This investigation examined the impact of human—machine competition (John Henry effects) on intent errors. John Henry effects, expressed as an unwillingness to use automation, were hypothesized to increase as a function of operators’ personal investment in unaided performance. Background: Misuse and disuse often occur because operators (a) cannot determine if automation or a nonautomated alternative maximizes the likelihood of task success (appraisal errors) or (b) know the utilities of the options but disregard this information when deciding to use or not to use automation (intent errors). Although appraisal errors have been extensively studied, there is a paucity of information regarding the causes and prevention of intent errors. Methods: Operators were told how many errors they and an automated device made on a target detection task. Self-reliant operators (high personal investment) could depend on their performance or automation to identify a target. Other-reliant operators (low personal investment) could rely on another person or automation. Results: As predicted, self-reliance increased disuse and decreased misuse. Conclusion: When the disuse and misuse data are viewed together, they strongly support the supposition that personal investment in unaided performance affects the likelihood of John Henry effects and intent errors. Application: These results demonstrate the need for a model of operator decision making that takes into account intent as well as appraisal errors. Potential applications include developing interventions to counter the deleterious effects of human—machine competition and intent errors on automation usage decisions.