Skills and cautiousness predict performance in difficult search.

People differ in how well they search. What are the factors that might contribute to this variability? We tested the contribution of two cognitive abilities: visual working memory (VWM) capacity and object recognition ability. Participants completed three tasks: a difficult inefficient visual search task, where they searched for a target letter T among skewed L distractors; a VWM task, where they memorized a color array and then identified whether a probed color belonged to the previous array; and the Novel Object Memory Test (NOMT), where they learnt complex novel objects and then identified them amongst objects that closely resembled them. Exploratory and confirmatory factor analyses revealed that there are two latent factors that explain the shared variance among these three tasks: a factor indicative of the level of caution participants exercised during the challenging visual search task, and a factor representing their visual cognitive abilities. People who score high on the search cautiousness tend to perform a more accurate but slower search. People who score high on the visual cognitive ability factor tend to have a higher VWM capacity, a better object recognition ability, and a faster search speed. The results reflect two points: (1) Visual search tasks share components with visual working memory and object recognition tasks. (2) Search performance is influenced not only by the search display's properties but also by individual predispositions such as caution and general visual abilities. This study introduces new factors for consideration when interpreting variations in visual search behaviors.

Distractor-distractor interactions in visual search for oriented targets explain the increased difficulty observed in nonlinearly separable conditions.

The linear separability effect refers to a benefit in search performance observed in a feature-search task, where target and distractor features vary along a continuous feature dimension: Search performance is best when there is a boundary in feature space that separates the distractor features from the target feature. However, the role that distractor heterogeneity plays in this effect is not well understood. Here, we reexamined this effect in the context of a new predictive procedure from Lleras et al. (2019) that quantifies the impact of distractor heterogeneity on search performance. Experiments 1A and 1B measured people's performance in homogeneous search conditions where they searched for the target among one type of distractor. The parameters observed in Experiments 1A and B were then used to predict search times in Experiments 2 and 3, where the target was presented in heterogeneous displays containing two types of distractors. The results show that total variance accounted for was 95% to 98%, without including any factor indexing the linear separability rule. The results demonstrate that heterogeneous search in orientation space is a function of target-distractor similarity and interitem interactions. The study highlights the robustness of the predictive procedure and demonstrates the generalizability of the method to estimate interitem interactions to new stimulus types. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Predicting how surface texture and shape combine in the human visual system to direct attention.

Objects differ from one another along a multitude of visual features. The more distinct an object is from other objects in its surroundings, the easier it is to find it. However, it is still unknown how this distinctiveness advantage emerges in human vision. Here, we studied how visual distinctiveness signals along two feature dimensions-shape and surface texture-combine to determine the overall distinctiveness of an object in the scene. Distinctiveness scores between a target object and distractors were measured separately for shape and texture using a search task. These scores were then used to predict search times when a target differed from distractors along both shape and texture. Model comparison showed that the overall object distinctiveness was best predicted when shape and texture combined using a Euclidian metric, confirming the brain is computing independent distinctiveness scores for shape and texture and combining them to direct attention.