Presentation
Automation Reliability Impairs Evidence Accumulation Efficiency: Computational Modeling of Monitoring Under Time Pressure
DescriptionAutomated decision aids are increasingly deployed in safety-critical domains, yet their inevitable imperfections require op-erators to continuously monitor and independently verify system recommendations. Prior research has established that time pressure and low automation reliability impair monitoring performance, but the underlying cognitive mechanisms remain unclear. The present study employed a simulated air traffic control (ATC) conflict detection task with a 2 (Time Pressure: high vs. low) × 2 (Automation Reliability: high vs. low) within-subjects design, integrating behavioral analysis with linear ballistic accumulator (LBA) modeling. Five competing models were constructed to identify how reliability affects cogni-tive processes under time pressure. Behavioral results revealed significant main effects and interaction effects of time pres-sure and reliability on both accuracy and sensitivity (d'). Model comparison demonstrated that the drift rate model (Model C) provided a decisively superior fit: low reliability under time pressure significantly suppressed the rate of evidence ac-cumulation (drift rate) rather than altering the decision threshold. Individual-level analysis further revealed cognitive strat-egy heterogeneity within the sample—56% of participants were primarily drift-rate regulated, while 36% were threshold-regulated. Based on this dual-pathway heterogeneity, differentiated human-automation interface design recommendations are proposed for operators with distinct cognitive profiles.
Event Type
Lecture
TimeThursday, October 22nd11am - 11:20am PDT
Location
Similar Presentations