Curious objects: Preattentive processing of object complexity

Abstract

Objects in the world frequently strike us as being complex (and informationally rich), or simple (and informationally sparse). For example, a crenulate and richly-organized leaf might look more complex than a plain stone. What is the nature of our experience of complexity — and why do we have this experience in the first place? We algorithmically generated hundreds of smoothed-edge shapes, and determined their complexity by computing the cumulative surprisal of their internal skeletal structure — essentially quantifying the amount of information in the object. Subjects then completed a visual search task in which a single complex target appeared among identical simple distractors, or a single simple target appeared among identical complex distractors. Not only was search for complex targets highly efficient (8ms/item), but it also exhibited a search asymmetry: a complex target among simple distractors was found faster than a simple target among complex distractors — suggesting that visual complexity is extracted 'preattentively'. (These results held over and above low-level properties that may correlate with complexity, including area, number of sides, spatial frequency, angular magnitudes, etc.). Next, we explored the function of complexity; why do we experience simplicity and complexity in the first place? We investigated the possibility that visual complexity is an attention-grabbing signal indicating that a stimulus contains something worth learning. Subjects who had to memorize and later recall serially presented objects recalled complex objects better than simple objects — but only when such objects appeared within a set of other objects, and not when they were presented one-at-a-time (suggesting that the effect is not driven simply by increased distinguishability of complex shapes). We suggest not only that object complexity is extracted efficiently and preattentively, but also that complexity arouses a kind of 'visual curiosity' about objects that improves subsequent learning and memory. Meeting abstract presented at VSS 2018