Anchor objects drive realism while diagnostic objects drive categorization in GAN generated scenes.

ABSTRACT: Our visual surroundings are highly complex. Despite this, we understand and navigate them effortlessly. This requires transforming incoming sensory information into representations that not only span low- to high-level visual features (e.g., edges, object parts, objects), but likely also reflect co-occurrence statistics of objects in real-world scenes. Here, so-called anchor objects are defined as being highly predictive of the location and identity of frequently co-occuring (usually smaller) objects, derived from object clustering statistics in real-world scenes, while so-called diagnostic objects are predictive of the larger semantic context (i.e., scene category). Across two studies (N₁ = 50, N₂ = 44), we investigate which of these properties underlie scene underst

SUBMITTER: Kallmayer A 

PROVIDER: S-EPMC11332195 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature