Abstract: AbstractInherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimizeda priorithrough stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional fea...
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Topics: 
Artificial intelligence