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Benchmarking Aided Decision Making in a Signal Detection Task.

Megan L Bartlett¹, Jason S McCarley²

¹Flinders University, Adelaide, Australia.

|August 11, 2017

Summary

This summary is machine-generated.

Human operators use highly reliable automated decision aids suboptimally. Their performance with automation-aided decision-making was closer to inefficient models than ideal ones, revealing insights into human-automation interaction strategies.

Keywords:

contingent criterion model decision-making strategies human–automation interaction signal detection theory

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Area of Science:

Human-computer interaction
Cognitive psychology
Decision science

Background:

Operators frequently interact suboptimally with automated decision aids.
This leads to performance below statistically ideal predictions.
Understanding these inefficiencies is crucial for improving human-automation collaboration.

Purpose of the Study:

To investigate human operators' strategies when using highly reliable automated decision aids.
To compare operator performance with predictions from various statistical models of collaborative decision-making.

Main Methods:

Participants performed a binary signal detection task, aided by a 93% reliable automated decision aid (graded or binary cues).
Operator performance was compared against seven statistical models, including an optimal model and the contingent criterion model.
Model comparisons aimed to identify operators' decision integration strategies.

Main Results:

Operator performance closely aligned with the least efficient collaborative models, significantly below ideal levels.
Performance consistency was observed regardless of whether the aid provided graded or binary judgments.
Model analysis suggested specific strategies employed by participants in integrating their judgments with the aid's output.

Conclusions:

Human operators do not fully leverage highly reliable automated decision aids.
Findings provide benchmarks for predicting automation-aided performance and understanding operator strategies.
Insights can inform the design of more effective human-automation systems.