Semantic Influences on Object Detection: Drift Diffusion Modeling Provides Insights Regarding Mechanism

Abstract

AbstractResearch shows that semantics, activated by words, impacts object detection. Skocypec & Peterson (2022) indexed object detection via correct reports of where figures lie in bipartite displays depicting familiar objects on one side of a border. They reported 2 studies with intermixed Valid and Invalid labels shown before test displays and a third, control, study. Valid labels denoted display objects. Invalid labels denoted unrelated objects in a different or the same superordinate-level category in studies 1 & 2, respectively. We used drift diffusion modeling (DDM) to elucidate the mechanisms of their results. DDM revealed that, following Valid labels, drift rate toward the correct decision increased, i.e., SNR increased. Following Invalid labels, SNR was lower only for upright displays in study 2. Thresholds were higher in studies 1 & 2 than in control. That more evidence must be accumulated from displays that follow labels implies that familiar object detection entails semantic activation. Threshold was even higher following Invalid labels in study 2, suggesting that more evidence from the display is needed to resolve within- category conflicts. These results support the view that semantic networks are engaged in object detection.