Reconstructing mental representations of block towers: A new bias for physical stability in visual working memory.

Abstract

Recent studies have explored the perception of physical properties (such as mass and stability) in psychology, neuroscience, and artificial intelligence (AI), and perhaps the most popular stimulus from such studies is the block tower - since such displays (of stacked rectilinear objects) evoke immediate vivid impressions of physical (in)stability. Previous work has studied how people make higher-level decisions about such towers, often with explicit questions ("Which way will it fall?"), and limited to those tower properties that are systematically manipulated as explicit independent variables. Here, in contrast, we explored how such stimuli are spontaneously encoded into visual working memory in the first place. Using a novel drag-and-drop reconstruction task in a custom three-dimensional interface, observers briefly viewed randomly generated block towers, and then simply reproduced them from memory - without ever being asked about stability, per se. By simulating both the original and reconstructed towers in a physics engine, we discovered a novel bias: observers reliably remember towers as more stable than they originally were, even when the reconstructions are otherwise accurate. This reveals for the first time how visual memory is structured (and biased) not only by how scenes are segmented into objects, but also by how those objects are physically related to each other - all without ever asking anyone anything about physics.