Tianyu Zhang¹ (), Andrew B. Leber¹; ¹The Ohio State University

People vary greatly in how strategically they guide their attention during visual search tasks. To understand why, recent research has explored the factors that distinguish optimal from suboptimal searchers, but this question remains largely unresolved. One possible explanation is that individuals focus on different aspects of the task stimuli, leading them to develop divergent strategies. We investigated this idea by using the Adaptive Choice Visual Search (ACVS) paradigm, which is designed to measure visual search strategy (Irons & Leber, 2016). In the ACVS, the optimal strategy is to search through the less numerous of two color subsets. We hypothesized that optimal participants who focused more on the ratio of color subsets would be more likely to exhibit optimal performance. Alternatively, both optimal and suboptimal participants are equally likely to process the ratios, but only the optimal participants exploit that information. To test this, we assessed participants’ memories of several display properties. In Experiment 1, we utilized after-trial probe questions to examine which aspect of the stimuli that participants attended to during the search task. After 30% of search trials, participants were questioned about various target properties, including the color subset (red/blue), subset ratio (large/small), location, and digit. Results showed that only the accuracy for ratio probes strongly correlated with individuals’ optimality in the ACVS (r = .55, p < .001). In Experiment 2, we explicitly informed participants which feature would be probed at the beginning of each block. Preliminary evidence suggests that participants are most optimal in blocks when they expect to be probed with ratio questions. These findings confirm that participants who attend display characteristics most relevant to the optimal strategy are more likely to use the optimal strategy. Furthermore, the results suggest that encouraging attention to such information makes participants more likely to adopt the optimal strategy.

Acknowledgements: This work is supported by NSF BCS-2021038 to ABL