Convolutional neural networks currently provide the best 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, and also reproduces all foundational features of human accuracy, RT, and confidence. To test RTNet’s 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 RTNet’s predictions. Overall, RTNet is the first neural network that exhibits all basic signatures of perceptual decision making, and therefore provides the most detailed model of all critical features of human behavior for novel images. [The link to the preprint][1] [1]: https://www.biorxiv.org/content/10.1101/2022.08.23.505015v1.full