Fiser, J. 1 & Ledley, J. 2
1 Central European University
2 Brandeis University
Although visual rule learning has been viewed as a potential candidate of how humans develop higher-order internal representations, typical rule-learning experiments have focused on the ability to extract abstract rules defined by element repetition rather than regularities based on feature dimensions. To link rule learning to the natural task of visual recognition, we investigated learning rules that were based on size regularities of visual objects perceived in a graphically generated three-dimensional layout. We found that adult subjects could extract the classical AAB type of rules based on size relations, and that the extracted rule was defined by the perceived 3-dimensional interpreted size of the objects rather than by the actual 2-dimensional extent of their images. Moreover, the extracted rule generalized not only to new displays with never-before-seen objects, but also to new 2-dimensional contexts where the original 3D perceptual constraints were not present at all. These results extend the generality of rule learning in vision supporting the view that the extracted rules are not purely semantic but incoprorate mid-level perceptual information, yet at the same time, they are abstract enough to apply across wide range of contexts.