Tianyu Zhang¹, Vladislav Khvostov¹, Andrew B. Leber¹; ¹The Ohio State University

In daily life, people frequently engage in visual search, such as finding a friend in a crowded mall or locating a car in a parking lot. Individuals vary in how strategically they guide their attention to find the target, and their strategies are often surprisingly suboptimal. Why would people prefer suboptimal strategies? One proposal is that they avoid the cognitive effort required to implement the optimal strategy, because they perceive that the required effort investment is not worth the performance gains realized. We used the Adaptive Choice Visual Search (ACVS) paradigm to measure the optimality of individuals’ visual search behaviors (Irons & Leber, 2018). In this paradigm, the optimal strategy is to search through the less numerous of the two color subsets; search optimality is quantified as the percentage of trials on which participants report the target in the less numerous color subset. In order to test the aforementioned hypothesis, we manipulated the ACVS search difficulty across a series of experiments by varying target-nontarget similarity and set size. We designed the experiments in such a way that greater task difficulty corresponded with greater performance benefits for using the optimal strategy. Results from all experiments revealed a significant effect of search difficulty on strategy optimization, with optimality increasing with greater task difficulty. In addition, when we informed participants of the optimal search strategy, the effect of this instruction was greater in the hard search condition than in the easy condition. Taken together, these findings suggest that increased search demands encourage people to invest the cognitive effort needed to achieve more optimal search behaviors.