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.

翻译：深度神经网络作为视觉计算模型的广泛应用，激发了将其与人类进行"对齐"的努力。对齐维度之一涉及行为选择，但超越表征选择模式以捕捉视觉决策的时间方面仍具挑战性。在此，我们概述了一种通用方法论，用于从可刺激计算的任务优化模型中构建反应时间的计算解释。具体而言，我们引入了一种新颖度量，利用主观逻辑理论中关于循环视觉模型证据积累的见解。我们证明，该度量与人类在四个不同视觉决策任务（涵盖感知分组、心理模拟和场景分类）中针对刺激操作的反应时间模式一致。这项工作为探索模型与人类视觉策略在各类认知任务中的时间对齐铺平了道路，进而为神经科学产生可检验的假设。代码与数据链接详见项目页面：https://serre-lab.github.io/rnn_rts_site。