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Individual differences predict low prevalence visual search performance

Critical real-world visual search tasks such as radiology and baggage screening rely on the detection of rare targets. When targets are rare, observers search for a relatively short amount of time and have a high miss rate, a pattern of results known as the low prevalence effect. Attempts to improve...

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Detalles Bibliográficos
Autores principales: Peltier, Chad, Becker, Mark W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5281659/
https://www.ncbi.nlm.nih.gov/pubmed/28203633
http://dx.doi.org/10.1186/s41235-016-0042-3
Descripción
Sumario:Critical real-world visual search tasks such as radiology and baggage screening rely on the detection of rare targets. When targets are rare, observers search for a relatively short amount of time and have a high miss rate, a pattern of results known as the low prevalence effect. Attempts to improve the search for rare targets have been unsuccessful or resulted in an increase in detections at the price of more false alarms. As an alternative to improving visual search performance through experimental manipulations, an individual differences approach found that those with higher working memory capacity were better at finding rare targets. We build on the individual differences approach and assess 141 observers’ visual working memory capacity (vWMC), vigilance, attentional control, big five personality traits, and performance in both high and low prevalence search tasks. vWMC, vigilance, attentional control, high prevalence visual search performance, and level of introversion were all significant predictors of low prevalence search accuracy, and together account for more than 50% of the variance in search performance. With the exception of vigilance, these factors are also significant predictors of reaction time; better performance was associated with longer reaction times, suggesting these factors identify observers who maintain relatively high quitting thresholds, even with low target prevalence. Our results suggest that a quick and easy-to-administer battery of tasks can identify observers who are likely to perform well in low prevalence search tasks, and these predictor variables are associated with higher quitting thresholds, leading to higher accuracy.