How big should this object be? Perceptual influences on viewing-size preferences

Author:

Chen Yi-ChiaORCID,Deza ArturoORCID,Konkle TaliaORCID

Abstract

AbstractWhen viewing objects depicted in a frame, observers prefer to view large objects like cars in larger sizes and smaller objects like cups in smaller sizes. That is, the visual size of an object that “looks best” is linked to its typical physical size in the world. Why is this the case? One intuitive possibility is that these preferences are driven by semantic knowledge: For example, when we recognize a sofa, we access our knowledge about its real-world size, and this influences what size we prefer to view the sofa within a frame. However, might visual processing play a role in this phenomenon—that is, do visual features that are related to big and small objects look better at big and small visual sizes, respectively, even when observers do not have explicit access to semantic knowledge about the objects? To test this possibility, we used “texform” images, which are synthesized versions of recognizable objects, which critically retain local perceptual texture and coarse form information, but are no longer explicitly recognizable. To test for visual size preferences, we used a two-interval forced choice task, in which each texform was presented at the preferred visual size of its corresponding original image, and a visual size slightly bigger or smaller. Observers consistently selected the texform presented at the canonical visual size as the more aesthetically pleasing one. These results suggest that the preferred visual size of an object depends not only on explicit knowledge of its real-world size, but also can be evoked by mid-level visual features that systematically covary with an object’s real-world size.

Publisher

Cold Spring Harbor Laboratory

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5. Deza, A. , Chen, Y. -C , Long, B. , & Konkle, T. (2019, September 15). Accelerated texforms: Alternative methods for generating unrecognizable object images with preserved mid-level features. [Paper presentation]. Conference on Cognitive Computational Neuroscience, Berlin, Germany, https://ccneuro.org/2019/proceedings/0000879.pdf

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