Publication
Identifying Inefficient Strategies in Automation-Aided Signal Detection
Publisher:
Center for Open Science
Date:
16-10-2022
DOI:
10.31219/OSF.IO/TU7QP
Abstract: Automated diagnostic aids can assist human operators in signal detection tasks, providing alarms, warnings, or diagnoses. Operators often use decision aids poorly, though, falling short of best-possible performance levels. Previous research has suggested that operators interact with binary signal detection aids using a sluggish contingent cutoff (CC) strategy (Robinson & Sorkin, 1985), shifting their response criterion in the direction stipulated by the aid’s diagnosis each trial but making adjustments that are smaller than optimal. The current study tested this model by examining the efficiency of automation-aided signal detection under different levels of task dificulty. In a pair of experiments, participants performed a numeric decision-making task requiring them to make signal or noise judgments on the basis of probabilistic readings. The standard deviation of the readings differed between groups of participants, producing two levels of task difficulty. Data were fit with the CC model and two alternative accounts of automation- aided strategy: a discrete deference (DD) model, which assumed participants defer to the aid on a subset of trials, and a mixture model, which assumed that participants choose randomly between the CC and DD strategies every trial. Model fits favored the mixture model. Results indicate multiple forms of inefficiency in operators’ strategies for using signal detection aids.