The relationship between the subjective experience of real-world cognitive failures and objective target-detection performance in visual search

Visual search is a common occurrence in everyday life, such as searching for the location of keys, identifying a friend in a crowd, or scanning an upcoming intersection for hazards while driving. Visual search is also used in professional contexts, such as medical diagnostic imaging and airport baggage screening. These contexts are often characterised by low-prevalence or rare targets. Here we tested whether individual differences in the detection of targets in visual search could be predicted from variables derived from the rich informational source of participants' subjective experience of their cognitive and attentional function in everyday life. We tested this in both low-prevalence (Experiment 1) and high-prevalence (Experiment 2) visual search conditions. In both experiments, participants completed a visual search with arrays containing multiple photorealistic objects, and their task was to detect the presence of a gun. Following this, they completed the Cognitive Failures Questionnaire (CFQ) and the Attentional Control Scale (ACS). In Experiment 1, the target was present on 2% of trials, while in Experiment 2, it was present on 50%. In both experiments, participants' scores on the False Triggering component of the CFQ were negatively associated with accuracy on target-present trials, while participants' scores on the Forgetfulness component of the CFQ were positively associated with target-present accuracy. These results show that objective performance in visual search can be predicted from subjective experiences of cognitive function. They also highlight that the CFQ is not monolithic. Instead, the CFQ subfactors can have qualitatively different relationships with performance. Theoretical and practical implications are discussed.This work was supported by an Australian Research Council (ARC) Future Fellowship (FT170100021) awarded to S.C.G. The authors thank Prof Mike Smithson for his helpful guidance on the mixed-effects binary logistic regression