Knowledge of letter shapes is central to reading. In experiments focusing primarily on a single letter shape-the "looptail" lowercase print -we found surprising gaps in skilled readers' knowledge. In Experiment 1 most participants failed to recall the existence of looptail g when asked if has two lowercase print forms, and almost none were able to write looptail g accurately. In Experiment 2 participants searched for s in text with multiple looptails. Asked immediately thereafter to write the g form they had seen, half the participants produced an "opentail" (the typical handwritten form), and only one wrote looptail accurately. In Experiment 3 participants performed poorly in discriminating looptail from distractors with important features mislocated or misoriented. These results have implications for understanding types of knowledge about letters, and how this knowledge is acquired. For example, our findings speak to hypotheses concerning the role of writing in learning letter shapes. More generally, our findings raise questions about the conditions under which massive exposure does, and does not, yield detailed, accurate, accessible knowledge. In this context we relate our findings to studies showing poor knowledge or memory for various types of stimuli despite extensive exposure. (PsycINFO Database Record

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When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior.

We typically think of intuitive physics in terms of high-level cognition, but might aspects of physics also be extracted during lower-level visual processing? Might we not only think about physics, but also see it? We explored this using multiple tasks in online adult samples with objects covered by soft materials—as when you see a chair with a blanket draped over it—where you must account for the physical interactions between cloth, gravity, and object. In multiple change-detection experiments ( n = 200), observers from an online testing marketplace were better at detecting image changes involving underlying object structure versus those involving only the superficial folds of cloths—even when the latter were more extreme along several dimensions. And in probe-comparison experiments ( n = 100), performance was worse when both probes (vs. only one) appeared on image regions reflective of underlying object structure (equating visual properties). This work collectively shows how vision uses intuitive physics to recover the deeper underlying structure of scenes.

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