论文标题
模型不合时宜的适合理解人类寻求信息的信息
Model-agnostic Fits for Understanding Information Seeking Patterns in Humans
论文作者
论文摘要
在不确定性下的决策任务中，人类在寻求，整合和行动与该任务相关的信息时表现出特征偏见。在这里，我们重新检查了以前精心设计的实验的数据，该实验是按大规模收集的，这些实验以骨料形式测量并分类了这些偏差。我们设计了深度学习模型，以总计复制这些偏见，同时还可以捕获个人行为变化。我们工作的一个关键发现是，可以通过从人群中抽样大量受试者，同时仍能捕获个体差异来克服从每个受试者收集的数据。此外，我们可以以很高的精度预测人类行为，而无需对任务目标，奖励结构或个人偏见做出任何假设，从而为任务中的人类行为提供了模型不合时宜的拟合。这种方法可以避免建模者指定的归纳偏见的潜在局限性，并且对人类认知功能的计算建模具有影响，尤其是人类AI界面。
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.
