Back to search

Article

How do visual and conceptual factors predict the composition of typical scene drawings?

2025-09-18

Abstract excerpt

Imagine you are asked to draw a typical bedroom, what would you put on paper? Your choice of objects is likely to depend on visual occurrence statistics (i.e., the objects present in previously encountered bedrooms) and semantic relations between objects and scenes (i.e., the semantic relationship between the bedroom and its constituent objects). To investigate how these two factors contribute to the composition o...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
0280493c-334b-5ad9-adf7-6e159afc41ba
DOI
10.1101/2025.09.15.676247
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
How do visual and conceptual factors predict the composition of typical scene drawings?DOI 10.1101/2025.09.15.676247
The spatiotemporal neural dynamics of object recognition for natural images and line drawingsSemantic proximity
Using drawings and deep neural networks to characterize the building blocks of human visual similaritySemantic proximity
Woman or tennis player? Visual typicality and lexical frequency affect variation in object namingSemantic proximity
How we draw and recognize things that don’t existSemantic proximity
From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstractionSemantic proximity
Leveraging Context for Perceptual Prediction using Word EmbeddingsSemantic proximity
Leveraging Context for Perceptual Prediction using Word EmbeddingsSemantic proximity
Leveraging Context for Perceptual Prediction using Word EmbeddingsSemantic proximity
Zero-shot learning of naturalistic concepts via conceptual systems alignmentSemantic proximity
Revealing the multidimensional mental representations of natural objects underlying human similarity judgmentsSemantic proximity
Human and generative AI integrate visual cues differently in a shape completion taskSemantic proximity
Probing the Link Between Vision and Language in Material Perception Using Psychophysics and Unsupervised LearningSemantic proximity
Select a neighboring publication to make it the new centre.