Deza, A. | bioRxiv Science

bioRxiv2021

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

When 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 contour information, but are no longer explicitly recognizable. To test for visual size preferences, we first used a size adjustment task, and the results were equivocal. However, clear results were obtained using 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. An additional control experiment ruled out alternative explanations related to size priming effects. 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 objects real-world size. HighlightsO_LIWe prefer to view large objects like cars large, and small objects like cups small C_LIO_LIIntuitively, such preferences may be driven by our knowledge of object sizes C_LIO_LIWe used unrecognizable texforms of objects that preserved mid-level visual features C_LIO_LISimilar viewing size preferences can be revealed with these texforms C_LIO_LISuch preferences thus arise not only from knowledge but also from visual processing C_LI

animal behavior and cognition↗

bioRxiv2021

General object-based features account for letter perception better than specialized letter features

After years of experience, humans become experts at perceiving letters. Is this visual capacity attained by learning specialized letter features, or by reusing general visual features previously learned in service of object categorization? To explore this question, we first measured the perceptual similarity of letters in two behavioral tasks, visual search and letter categorization. Then, we trained deep convolutional neural networks on either 26-way letter categorization or 1000-way object categorization, as a way to operationalize possible specialized letter features and general object-based features, respectively. We found that the general object-based features more robustly correlated with the perceptual similarity of letters. We then operationalized additional forms of experience-dependent letter specialization by altering object-trained networks with varied forms of letter training; however, none of these forms of letter specialization improved the match to human behavior. Thus, our findings reveal that it is not necessary to appeal to specialized letter representations to account for perceptual similarity of letters. Instead, we argue that it is more likely that the perception of letters depends on domain-general visual features. AUTHOR SUMMARYFor over a century, scientists have conducted behavioral experiments to investigate how the visual system recognizes letters, but it has proven difficult to propose a model of the feature space underlying this capacity. Here we leveraged recent advances in machine learning to model a wide variety of features ranging from specialized letter features to general object-based features. Across two large-scale behavioral experiments we find that general object-based features account well for letter perception, and that adding letter specialization did not improve the correspondence to human behavior. It is plausible that the ability to recognize letters largely relies on general visual features unaltered by letter learning.

neuroscience↗