Time to consider time: Comparing human reaction times to dynamical signatures from recurrent vision models on a perceptual grouping task

To make sense of its retinal inputs, our visual system organizes perceptual elements into coherent figural objects. This perceptual grouping process, like many aspects of visual cognition, is believed to be dynamic and at least partially reliant on feedback. Indeed, cognitive scientists have studied its time course through reaction time measurements (RT) and have associated it with a serial spread of object-based attention. Recent progress in biologically-inspired machine learning, has put forward convolutional recurrent neural networks (cRNNs) capable of exhibiting and mimicking visual cortical dynamics. To understand how the visual routines learned by cRNNs compare to humans, we need ways to extract meaningful dynamical signatures from a cRNN and study temporal human-model alignment. We introduce a framework to train, analyze, and interpret cRNN dynamics. Our framework triangulates insights from attractor-based dynamics and evidential learning theory. We derive a stimulus-dependent metric, ξ, and directly compare it to existing human RT data on the same task: a grouping task designed to study object-based attention. The results reveal a “filling-in” strategy learned by the cRNN, reminiscent of the serial spread of object-based attention in humans. We also observe a remarkable alignment between ξ and human RT patterns for diverse stimulus manipulations. This alignment emerged purely as a byproduct of the task constraints (no supervision on RT). Our framework paves the way for testing further hypotheses on the mechanisms supporting perceptual grouping and object-based attention, as well as for inter-model comparisons looking to improve the temporal alignment with humans on various other cognitive tasks.