Recent studies have demonstrated altered visual processing of stimuli in the proximal region of the hand. It has been challenging to characterize the range and nature of these processing differences. In our attempt to deconstruct the factors giving rise to the Hand-Proximity Effects (HPEs), we manipulated the organization of items in a visual search display. In two experiments, we observed the absence of HPE. Specifically, in Experiment 1, we presented the search display in only one half of the monitor (split diagonally), which could be either near or far from the hand placed on the corner of the monitor. The results of a Bayesian analysis showed that the search efficiency was not significantly different for neither ‘near’ nor ‘far’ condition when compared with the baseline condition in which the hand rested on the lap. In Experiment 2, the search display was arranged horizontally across the monitor. A Bayesian analysis showed that RTs did not vary depending on the proximity of the target to the hand as well as the baseline (lap) condition. The present results characterize features of the HPE that have not been reported previously and are in line with recent reports of the failure to replicate HPE under various circumstances.

Recently, Wang, Buetti and Lleras ( 2017 ) developed an equation to predict search performance in heterogeneous visual search scenes (i.e., multiple types of non-target objects simultaneously present) based on parameters observed when participants perform search in homogeneous scenes (i.e., when all non-target objects are identical to one another). The equation was based on a computational model where every item in the display is processed with unlimited capacity and independently of one another, with the goal of determining whether the item is likely to be a target or not. The model was tested in two experiments using real-world objects. Here, we extend those findings by testing the predictive power of the equation to simpler objects. Further, we compare the model’s performance under two stimulus arrangements: spatially-intermixed (items randomly placed around the scene) and spatially-segregated displays (identical items presented near each other). This comparison allowed us to isolate and quantify the facilitatory effect of processing displays that contain identical items (homogeneity facilitation), a factor that improves performance in visual search above-and-beyond target-distractor dissimilarity. The results suggest that homogeneity facilitation effects in search arise from local item-to-item interaction (rather than by rejecting items as “groups”) and that the strength of those interactions might be determined by stimulus complexity (with simpler stimuli producing stronger interactions and thus, stronger homogeneity facilitation effects).

Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items ( Buetti et al., 2016 ). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other’s processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search.