In decision making tasks under uncertainty, humans display characteristic biases in seeking, integrating, and acting upon information relevant to the task. Here, we reexamine data from previous carefully designed experiments, collected at scale, that measured and catalogued these biases in aggregate form. We design deep learning models that replicate these biases in aggregate, while also capturing individual variation in behavior. A key finding of our work is that paucity of data collected from each individual subject can be overcome by sampling large numbers of subjects from the population, while still capturing individual differences. In addition, we can predict human behavior with high accuracy without making any assumptions about task goals, reward structure, or individual biases, thus providing a model-agnostic fit to human behavior in the task. Such an approach can sidestep potential limitations in modeler-specified inductive biases, and has implications for computational modeling of human cognitive function in general, and of human-AI interfaces in particular.

该研究使用深度学习模型复制人类在决策任务中出现的偏见和行为，研究发现通过从人口中抽样数量庞大的受试者，可以克服来自个体受试者采集数据量的缺陷。此外，该方法可以在不做任何关于任务目标、奖励结构或个体偏见的假设的情况下高准确度地预测人类行为，对于计算建模人类认知功能和人机交互具有重要意义。

用于理解人类信息查找模式的不依赖于模型的适配器