Attentional selection of levels within hierarchically organized figures is mediated by object-files

Abstract

Objects frequently have a hierarchical organization (tree-branch-leaf). How do we select the level to be attended? This has been explored with compound letters: a global letter built from local letters. One explanation, backed by much empirical support, is that attentional competition is biased toward certain spatial frequency (SF) bands across all locations and objects (a SF filter). This view assumes that the global and local letters are carried respectively by low and high SF bands, and that the bias can persist over time. Here we advocate a complementary view in which perception of hierarchical level is determined by how we represent letters in object-files. Although many properties bound to an object-file (i.e., position, color, even shape) can mutate without affecting its persistence over time, we posit that same object-file cannot be used to store information from different hierarchical levels. Thus, selection of level would be independent from locations but not from the way objects are represented at each moment. These views were contrasted via an attentional blink paradigm that presented letters within compound figures, but only one level at a time. Attending to two letters in rapid succession was easier if they were at the same-compared to different-levels, as predicted by both accounts. However, only the object-file account was able to explain why it was easier to report two targets on the same moving object compared to the same targets on distinct objects. The interference of different masks on target recognition was also easier to predict by the object-file account than by an SF filter. The methods introduced here allowed us to investigate attention to hierarchical levels and to object-files within the same empirical framework. The data suggests that SF information is used to structure the internal organization of object representations, a process understood best by integrating object-file theory with previous models of hierarchical perception.