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Published on: November 30, 2018
How do visual and conceptual factors predict the object content in typical scene drawings?
Gongting Wang1,2, Ilker Duymaz2, Matthew J Foxwell3
1Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.
British Journal of Psychology (London, England : 1953)
|July 31, 2026
Summary
Visual and conceptual factors jointly influence object selection in scene drawings. Both visual occurrence statistics and semantic relationships predict object inclusion, with combined models showing superior performance.
Area of Science:
- Cognitive Psychology
- Computer Vision
- Computational Neuroscience
Background:
- Scene composition is influenced by visual statistics and semantic relationships.
- Understanding how these factors shape typical scene drawings is crucial.
Purpose of the Study:
- To investigate the contribution of visual occurrence statistics and semantic relations to object selection in scene drawings.
- To determine how combined visual and conceptual factors predict object inclusion.
Main Methods:
- Analysis of 1192 drawings across six indoor scene categories from 303 participants.
- Estimation of object visual occurrence frequency using the ADE20K dataset.
- Calculation of object semantic relatedness to scene concepts via word2vec models and generalized linear models.
Main Results:
- Both visual and conceptual factors significantly predicted object likelihood in scene drawings.
- A combined model integrating visual and conceptual factors outperformed single-factor models.
- Object specificity showed weak predictive power for object selection, but infrequent objects remained diagnostic.
Conclusions:
- Visual occurrence statistics and semantic relationships jointly shape the composition of typical scene drawings.
- The interplay between visual and conceptual information is key to understanding scene representation.
- Future research can explore the neural underpinnings of these processes.
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