Chaz Firestone¹, Tal Boger¹; ¹Johns Hopkins University

Among the most impressive effects in recent vision science are those associated with “canonical size”. When a building and a rubber duck occupy the same number of pixels on a display, the mind nevertheless encodes the real-world size difference between them. Such encoding occurs automatically, organizes neural representations, and drives higher-order judgments. However, objects that differ in canonical size also differ in many mid- and low-level visual properties; this makes it difficult—and seemingly impossible—to isolate canonical size from its covariates (which are known to produce similar effects on their own). Can this challenge be overcome? Here, we leverage a new technique called “visual anagrams”, which uses diffusion models to generate static images whose interpretations change with image orientation. For example, such an image may look like a rabbit in one orientation and an elephant when upside-down. We created a stimulus set of visual anagrams whose interpretations differed in canonical size; each image depicted a canonically large object in one orientation but a canonically small object when rotated, while being pixel-wise identical in every other respect. Six experiments show that most (though not all) canonical size effects survive such maximal control. Experiments 1–2 tested Stroop effects probing the automaticity of canonical size encoding; consistent with previous findings, subjects were faster to correctly judge the onscreen size of an object when its canonical size was congruent with its onscreen size. Experiments 3–4 tested effects on viewing-size preferences; consistent with previous findings, subjects chose larger views for canonically larger objects. Experiments 5–6 tested efficient visual search when targets differed from distractors in canonical size; departing from previous findings, we found no such search advantage. This work not only applies a long-awaited control to classic experiments on canonical size, but also presents a case study of the usefulness of visual anagrams for vision science.

Acknowledgements: NSF BCS 2021053, NSF GRFP