RTNet: A neural network that exhibits the signatures of human perceptual decision making

Abstract

Convolutional neural networks show promise as models of biological vision. However, their decision behavior, including the facts that they are deterministic and use equal number of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behavior. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT, and confidence and does so better than all current alternatives. To test RTNets ability to predict human behavior on novel images, we collected accuracy, RT, and confidence data from 60 human subjects performing a digit discrimination task. We found that the accuracy, RT, and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human subjects. Critically, human subjects who were more similar to the average human performance were also found to be closer to RTNets predictions, suggesting that RTNet successfully captured average human behavior. Overall, RTNet is a promising model of human response times that exhibits the critical signatures of perceptual decision making.