Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the “black box” of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi-faceted decisions that are taken in varying contexts and are based on many different types of information.

中文翻译：

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通过过程跟踪和神经数据增强社会和战略决策模型

我们做出的每一个决定都伴随着反应延迟、注视位置、瞳孔扩张和神经活动的特征模式。然而，许多社会决策模型忽略了相应的过程跟踪数据，只关注最终的选择结果。在这里，我们认为这是一个错误，因为使用过程数据可以帮助建立更好的人类行为模型、创建更好的实验并改进政策干预。具体来说，这些数据使我们能够解开决策过程的“黑匣子”，并评估我们社会选择背后的机制。使用这些数据，我们可以直接验证潜在模型变量，在相互竞争的个人动机之间进行仲裁，并捕获信息处理策略。这些好处在社会科学中特别有价值，