Real-world object categories and scene contexts conjointly structure statistical learning for the guidance of visual search

We examined how object categories and scene contexts act in conjunction to structure the acquisition and use of statistical regularities to guide visual search. In an exposure session, participants viewed five object exemplars in each of two colors in each of 42 real-world categories. Objects were presented individually against scene context backgrounds. Exemplars within a category were presented with different contexts as a function of color (e.g., the five red staplers were presented with a classroom scene, and the five blue staplers with an office scene). Participants then completed a visual search task, in which they searched for novel exemplars matching a category label cue among arrays of eight objects superimposed over a scene background. In the context-match condition, the color of the target exemplar was consistent with the color associated with that combination of category and scene context from the exposure phase (e.g., a red stapler in a classroom scene). In the context-mismatch condition, the color of the target was not consistent with that association (e.g., a red stapler in an office scene). In two experiments, search response time was reliably lower in the context-match than in the context-mismatch condition, demonstrating that the learning of category-specific color regularities was itself structured by scene context. The results indicate that categorical templates retrieved from long-term memory are biased toward the properties of recent exemplars and that this learning is organized in a scene-specific manner.