The Effect of Correlated Observations on Human-Automation Interaction in a Signal Detection Task

Abstract

Automated signal detection aids assist human operators in various activities (e.g., medical diagnosis). However, operators tend to interact with these aids in suboptimal ways. An under-considered factor that might influence operators’ aid use is the correlation between the aid’s and the operator’s observations. Although prior research has generally assumed that human operators and automated aids rely on independent information sources, correlated observations may be common in naturalistic automation-aided detection tasks. The present study explored the effects of correlated observations on automation-use by analyzing performance in a numerical signal detection task using the Contingent Cutoff (CC) model, a statistical cognitive model of automation-aided decision making. Participants completed the task with and without assistance of an aid, and in one of two conditions: correlated observations vs. uncorrelated observations. The CC model accurately described observed performance for both conditions. Overall aid use efficiency was unaffected by correlated observations.