Real-world structure facilitates the rapid emergence of scene category information in visual brain signals

Abstract

In everyday life, our visual surroundings are not arranged randomly, but structured in predictable ways. Although previous studies have shown that the visual system is sensitive to such structural regularities, it remains unclear whether the presence of an intact structure in a scene also facilitates the cortical analysis of the scene's categorical content. To address this question, we conducted an EEG experiment during which participants viewed natural scene images that were either "intact" (with their quadrants arranged in typical positions) or "jumbled" (with their quadrants arranged into atypical positions). We then used multivariate pattern analysis to decode the scenes' category from the EEG signals (e.g., whether the participant had seen a church or a supermarket). The category of intact scenes could be decoded rapidly within the first 100ms of visual processing. Critically, after within 200ms of processing category decoding was more pronounced for the intact scenes compared to the jumbled scenes, suggesting that the presence of real-world structure facilitates the extraction of scene category information. No such effect was found when the scenes were presented upside-down, indicating that the facilitation of neural category information is indeed linked to a scene's adherence to typical real-world structure, rather than to differences in visual features between intact and jumbled scenes. Our results demonstrate that early stages of categorical analysis in the visual system exhibit tuning to the structure of the world that may facilitate the rapid extraction of behaviorally relevant information from rich natural environments.

Tracking cortical representations of facial attractiveness using time-resolved representational similarity analysis

“…These data were then unfolded into a 756-element vector for further analyses. Before RDM construction, we performed principal-component analyses (PCAs) to reduce the dimensionality of these response vectors [39,73]. We split the available data into two independent subsets, with an equal number of trials per condition randomly assigned to each subset.…”