Mechanisms of Vigilance Loss in Sensory and Cognitive Tasks

When observers monitor for infrequent signals for extended durations, they generally experience a decline in detections over time. This decline is termed the vigilance decrement. Current theories of vigilance attribute the decrement to three potential mechanisms: conservative shifts in response bias, losses of sensitivity, and an increased rate of attentional lapses over time. Understanding which mechanisms contribute to the losses of vigilance is necessary to mitigate the decrement in applied settings. Unfortunately, much of the existing literature examining vigilance performance relies on measures that are not suited for distinguishing between all three proposed mechanisms. Using novel methods of analysis, the present project examined the extent to which bias shifts, sensitivity losses, and attentional lapses contributed to the vigilance decrement across a range of vigilance tasks. Study 1 (Chapter 3) used psychometric curves to analyze changes in response bias, sensitivity, and lapse rate in two online vigilance tasks. Data showed that the decrement was largely driven by an attentional lapses and conservative shifts in bias over time, with inconclusive evidence for a sensitivity loss. Study 2 (Chapter 4) presents a generative process model to simulate cognitive mechanisms directly and tests the adequacy of the model by reanalyzing data previously fitted with psychometric curves. Results provide converging evidence that the decrement was driven by attentional lapses and shifts in bias. Study 3 (Chapter 5) uses the generative model to assess vigilance performance within a cognitive vigilance task. Vigilance was relatively stable in the cognitive task and data gave strong evidence that the decrement, albeit small, was driven by an increase in attentional lapses. Together, findings provide strong evidence that vigilance decrement is driven by attentional lapses, followed by conservative shifts in bias. Relatively weak evidence for sensitivity loss. Suggests interventions that target lapses and response criteria most effective for minimizing vigilance losses in applied settings

Metacognition, Numeracy, and Automation-aided Decision-making

Automated decision aids can improve human decision-making but the benefits are often compromised by inefficient use. The current experiment examined whether metacognition—the ability to assess self-performance—and numeracy—the ability to understand and work with numbers—predict the efficiency of automation use in a signal detection task. Two-hundred twenty-one participants classified random dot images as blue or orange dominant, receiving assistance from an 84% reliable decision aid on some trials. Type 1 and metacognitive signal detection measures were estimated from participants’ confidence ratings, and numeracy was measured using a subjective scale. The inefficiency of automation use was assessed by measuring the deviation from optimal bias following cues from the aid (bias error). Data gave strong evidence that metacognition was not associated with bias error, and anecdotal evidence that numeracy and suboptimality were weakly negatively correlated. These results suggest that operators used a strategy of combining the aid’s judgments with their own that is not metacognitively driven, but may depend on numeracy