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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Measuring Individual Differences in the Perfect Automation Schema.

Stephanie M Merritt1, Jennifer L Unnerstall2, Deborah Lee2

  • 1University of Missouri, St. Louis merritts@umsl.edu.

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Summary
This summary is machine-generated.

A new scale measuring the perfect automation schema (PAS) was developed. The all-or-none thinking aspect of the PAS significantly predicted trust decreases after automation errors, suggesting its utility in understanding user trust in automated systems.

Keywords:
all-or-none thinkingautomationhigh expectationsperfect automation schemapropensity to trust machinestrust

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Area of Science:

  • Human-Computer Interaction
  • Psychology
  • Automation Trust

Background:

  • The perfect automation schema (PAS) is hypothesized to correlate with trust reduction after automation failures.
  • No validated self-report measure for PAS currently exists.
  • PAS is proposed to have two factors: high expectations and all-or-none thinking.

Purpose of the Study:

  • To develop and validate a self-report measure for the perfect automation schema (PAS).
  • To assess the psychometric properties of the developed PAS measure.
  • To investigate the relationship between PAS factors and user trust in imperfect automated systems.

Main Methods:

  • Developed a self-report measure assessing high expectations and all-or-none thinking.
  • Conducted two studies where participants completed the PAS measure and interacted with automated aids.
  • Collected data on user trust following exposure to automation errors.

Main Results:

  • The PAS measure showed good factor structure, convergent, and discriminant validity.
  • High expectations and all-or-none thinking scales had low intercorrelations, suggesting they are distinct constructs.
  • All-or-none thinking, but not high expectations, significantly predicted decreases in trust after automation errors.

Conclusions:

  • The all-or-none thinking scale of the PAS measure effectively predicts significant trust decreases.
  • The developed PAS measure, particularly the all-or-none thinking scale, demonstrates acceptable psychometric properties.
  • This validated measure can be utilized in future research and applied settings to assess user levels of perfect automation schema.