» Articles » PMID: 35281732

Scale Ambiguities in Material Recognition

Overview
Journal iScience
Publisher Cell Press
Date 2022 Mar 14
PMID 35281732
Authors
Affiliations
Soon will be listed here.
Abstract

Many natural materials have complex, multi-scale structures. Consequently, the inferred identity of a surface can vary with the assumed spatial scale of the scene: a plowed field seen from afar can resemble corduroy seen up close. We investigated this 'material-scale ambiguity' using 87 photographs of diverse materials (e.g., water, sand, stone, metal, and wood). Across two experiments, separate groups of participants ( = 72 adults) provided judgements of the material category depicted in each image, either with or without manipulations of apparent distance (by verbal instructions, or adding objects of familiar size). Our results demonstrate that these manipulations can cause identical images to be assigned to completely different material categories, depending on the assumed scale. Under challenging conditions, therefore, the categorization of materials is susceptible to simple manipulations of apparent distance, revealing a striking example of top-down effects in the interpretation of image features.

Citing Articles

Top-down effects on translucency perception in relation to shape cues.

Nagai T, Kiyokawa H, Kim J PLoS One. 2025; 20(2):e0314439.

PMID: 39965015 PMC: 11835294. DOI: 10.1371/journal.pone.0314439.


Blurring the boundary between models and reality: Visual perception of scale assessed by performance.

Meese T, Baker D, Summers R PLoS One. 2023; 18(5):e0285423.

PMID: 37155632 PMC: 10166532. DOI: 10.1371/journal.pone.0285423.


Unsupervised learning reveals interpretable latent representations for translucency perception.

Liao C, Sawayama M, Xiao B PLoS Comput Biol. 2023; 19(2):e1010878.

PMID: 36753520 PMC: 9942964. DOI: 10.1371/journal.pcbi.1010878.

References
1.
Hubbard T, Kall D, Baird J . Imagery, memory, and size-distance invariance. Mem Cognit. 1989; 17(1):87-94. DOI: 10.3758/bf03199560. View

2.
Konkle T, Oliva A . Canonical visual size for real-world objects. J Exp Psychol Hum Percept Perform. 2010; 37(1):23-37. PMC: 3408867. DOI: 10.1037/a0020413. View

3.
Sawayama M, Nishida S, Shinya M . Human perception of subresolution fineness of dense textures based on image intensity statistics. J Vis. 2017; 17(4):8. DOI: 10.1167/17.4.8. View

4.
Harris C, Millman K, van der Walt S, Gommers R, Virtanen P, Cournapeau D . Array programming with NumPy. Nature. 2020; 585(7825):357-362. PMC: 7759461. DOI: 10.1038/s41586-020-2649-2. View

5.
Virtanen P, Gommers R, Oliphant T, Haberland M, Reddy T, Cournapeau D . SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020; 17(3):261-272. PMC: 7056644. DOI: 10.1038/s41592-019-0686-2. View