We introduce a new benchmark dataset based on classic methods of studying 3D shape perception from texture and motion, inspired by earlier work on Gestalt principles of perceptual organization and the ecological (Gibsonian) approach to perception of structure in moving displays. The dataset consists of parametric 3D shapes (superquadrics) with procedurally generated textures rotating and translating against a similarly textured backdrop. We expect these stimuli to be challenging for current computer vision models, as they depart from the statistics of real-world or realistically rendered stimuli. We test a variety of models’ ability to segment textured stimuli across three training conditions: pre-trained on naturalistic stimuli, pre-trained+fine-tuned on textured stimuli, trained on textured stimuli. While no models generalize to segment textured stimuli without fine-tuning, performance improves with fine-tuning and training on textured stimuli. We will discuss how this benchmark can guide models of scene perception towards more human-like robustness and generality.

Curiosity drives much of human behavior, but its open-ended nature makes it hard to study in the laboratory. Moreover, computational theories of curiosity -- models of how intrinsic motivation promotes complex behaviors -- have been challenging to test because of technical limits. To circumvent this problem, we develop a new way to assess intrinsic motivation for building: we assume people build what they find interesting, so we asked them to rate the "interestingness" of visual stimuli -- in this case, simple block towers. Adults gave a range of ratings to towers built by children, with taller towers rated higher. To probe interestingness further, we developed controlled tower stimuli in a simulated 3D environment. While tower height predicted much of the variation in ratings, people also favored more precarious towers, as inferred from geometric features and simulated dynamics. These ratings and features therefore give a clear target for computational accounts of curiosity to explain.

Curiosity is a fundamental driver of human behavior, and yet because of its open-ended nature and the wide variety of behaviors it inspires in different contexts, it is remarkably difficult to study in a laboratory context. A promising approach to developing and testing theories of curiosity is to instantiate them in artificial agents that are able to act and explore in a simulated environment, and then compare the behavior of these agents to humans exploring the same stimuli. Here we propose a new experimental paradigm for examining children's -- and AI agents' -- curiosity about objects' physical interactions. We let them choose which object to drop another object onto in order to create the most interesting effect. We compared adults' (N=155) and children's choices (N=66; 3-7 year-olds) and found that both children and adults show a strong preference for choosing target objects that could potentially contain the dropped object. Adults alone also make choices consistent with achieving support relations. We contextualize our results using heuristic computational models based on 3D physical simulations of the same scenarios judged by participants.