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From pixels to categories: the representational dynamics of real-world scene understanding

Detail Summary
Organised by Host: Steven Scholte
Date 18 December 2017
Time 16:00 - 17:00

Although human visual categorization is characterized by impressive speed and accuracy, the neural mechanisms supporting this ability remain elusive. Previous work has demonstrated that scenes can be categorized via a number of different features including low- to mid-level visual features; objects; spatial layout; and a scene’s functions. Critically, these features are not independent of one another.

For example, changing a scene’s spatial layout changes both its affordances and low-level visual features. To what extent do these features independently contribute to scene categorization, and when do they do it? Using hierarchical linear regression, we examined the contribution of a number of features to scene categorization behavior in a massive online experiment, finding that functions captured 85.5% of overall explained variance, with nearly half of the explained variance captured only by functions. Then, using representational similarity analysis (RSA) in conjunction with variance partitioning and high-density event-related potentials, we examined the use of these features over time in the brain. 

Surprisingly, we found parallel development of both exclusively visual features, and seemingly higher-level features such as functions and semantics. Taken together, these results argue for the seamless integration between visual and conceptual processing in scene categorization.

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