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Found 2 papers, 1 papers with code
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
Using FLOPs as an analog for reaction time, we compare networks with humans on curve-fit error, category-wise correlation, and curve steepness, and conclude that cascaded dynamic neural networks are a promising model of human reaction time in object recognition tasks.
Reinforcement Learning and its Connections with Neuroscience and Psychology
In this paper, we comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision making in the brain.
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