Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. 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 RTNet’s ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants 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 participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet’s predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.

卷积神经网络显示出作为生物视觉模型的前景。然而，它们的决策行为，包括它们是确定性的以及对简单和困难的刺激使用相同数量的计算的事实，与人类决策明显不同，从而限制了它们作为人类感知行为模型的适用性。在这里，我们开发了一种新的神经网络 RTNet，它可以生成随机决策和类似人类的响应时间 (RT) 分布。我们进一步进行了全面的测试，结果表明 RTNet 再现了人类准确性、RT 和置信度的所有基本特征，并且比当前的所有替代方案都更好。为了测试 RTNet 在新图像上预测人类行为的能力，我们收集了 60 名执行数字辨别任务的人类参与者的准确性、RT 和置信度数据。我们发现 RTNet 对单个新颖图像产生的准确性、RT 和置信度与人类参与者产生的相同数量相关。至关重要的是，与人类平均表现更相似的人类参与者也被发现更接近 RTNet 的预测，这表明 RTNet 成功捕捉了人类的平均行为。总体而言，RTNet 是一种很有前途的人类 RT 模型，它展示了感知决策的关键特征。