  The meteoric rise in the adoption of deep neural networks as computational
models of vision has inspired efforts to "align" these models with humans. One
dimension of interest for alignment includes behavioral choices, but moving
beyond characterizing choice patterns to capturing temporal aspects of visual
decision-making has been challenging. Here, we sketch a general-purpose
methodology to construct computational accounts of reaction times from a
stimulus-computable, task-optimized model. Specifically, we introduce a novel
metric leveraging insights from subjective logic theory summarizing evidence
accumulation in recurrent vision models. We demonstrate that our metric aligns
with patterns of human reaction times for stimulus manipulations across four
disparate visual decision-making tasks spanning perceptual grouping, mental
simulation, and scene categorization. This work paves the way for exploring the
temporal alignment of model and human visual strategies in the context of
various other cognitive tasks toward generating testable hypotheses for
neuroscience. Links to the code and data can be found on the project page:
https://serre-lab.github.io/rnn_rts_site.
