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Effects of human-machine competition on intent errors in a target detection task.

Hall P Beck¹, J Bates McKinney, Mary T Dzindolet

¹Psychology Department, 222 Joyce Lawrence Lane, Appalachian State University, Boone, NC 28608, USA. beckhp@appstate.edu

|November 11, 2009

Summary

Operator personal investment in unaided performance increases the likelihood of John Henry effects, leading to intent errors and reduced automation use. This highlights the need to address human-machine competition in operator decision-making models.

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Area of Science:

Human-computer interaction
Cognitive psychology
Automation and control systems

Background:

Intent errors, where operators disregard known automation utility, are understudied compared to appraisal errors.
Human-machine competition, termed John Henry effects, can lead to automation misuse and disuse.
Understanding intent errors is crucial for effective automation design and implementation.

Purpose of the Study:

To investigate the impact of human-machine competition, specifically John Henry effects, on operator intent errors.
To examine how operators' personal investment in unaided performance influences their likelihood of committing intent errors.
To explore the relationship between self-reliance and automation usage patterns.

Main Methods:

A target detection task was employed to compare operator and automated device error rates.
Participants were categorized based on their personal investment in unaided performance (self-reliant vs. other-reliant).
Operator decisions to use or disuse automation were recorded and analyzed in relation to their self-reliance levels.

Main Results:

Increased self-reliance (high personal investment) was associated with greater disuse of automation.
Conversely, higher self-reliance led to a decrease in automation misuse.
The findings indicate a direct correlation between personal investment and automation usage behaviors.

Conclusions:

Personal investment in unaided performance significantly influences the occurrence of John Henry effects and intent errors.
A comprehensive model of operator decision-making must incorporate both intent and appraisal errors.
Interventions are needed to mitigate the negative effects of human-machine competition on automation adoption and effective use.