4.7 Article

The time-course of real-world scene perception: Spatial and semantic processing

Journal

ISCIENCE
Volume 25, Issue 12, Pages -

Publisher

CELL PRESS
DOI: 10.1016/j.isci.2022.105633

Keywords

-

Funding

  1. Engineering and Physical Sciences Research Council (UK) [EP/K005952/1, EP/S016368/1]

Ask authors/readers for more resources

This study tested the space-centered theory by investigating the temporal dynamics of spatial and semantic perception in scene perception. The results challenge the traditional 'bottom-up' views by suggesting that humans depend more on semantic information rather than spatial layout to discriminate spatial structure categories.
Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory maintains that a scene's spatial structure (e.g., openness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene's spatial properties and semantic content allows for semantic identity to be inferred from its layout. We tested this theory by investigating (1) the temporal dynamics of spatial and semantic perception in real-world scenes, and (2) dependencies between spatial and semantic judgments. Participants viewed backward-masked images for 13.3 to 106.7 ms, and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed-off) category. We found no temporal precedence of spatial discrimination relative to semantic discrimination. Computational analyses further suggest that, instead of using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. These findings challenge traditional 'bottom-up' views of scene perception.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available