GAMR: A Guided Attention Model for (visual) Reasoning
Mohit Vaishnav, T. Serre · PubMed · 2022
), which instantiates an active vision theory - positing that the brain solves complex visual reasoning problems dynamically - via sequences of attention shifts to select and route task-relevant visual information into memory. Experiments on an array of visual reasoning tasks and datasets demonstrate GAMR's ability to learn visual routines in a robust and sample-efficient manner. In addition, GAMR is shown to be capable of zero-shot generalization on completely novel reasoning tasks. Overall, our work provides computational support for cognitive theories that postulate the need for a critical interplay between attention and memory to dynamically maintain and manipulate task-relevant visual information to solve complex visual reasoning tasks.