Imprecise Inference from Sequentially Presented Evidence

理解人们是如何做出决策的，对于推进我们对经济机制的理解至关重要。尽管理性选择理论成功地解释了不确定性下人类决策的某些方面，但未能捕捉到行为中观察到的某些反复出现的模式，如选择中的偏见和明显的随机性。其中一些偏离最佳行为的行为表明，信息在大脑中的处理方式引入了不精确，类似于感官感知的不精确。越来越多的关于知觉判断的文献认为，在感觉语境中，考虑到可用认知资源的限制，这些不精确实际上往往代表着有效的适应。这就提出了一个问题，即在较高水平的认知加工(数值大小的比较、波动序列平均值的估计以及对未知变量的推断)的情况下，是否不能以类似的方式理解不精确的模式。我们通过一系列实验来研究这一假设，在这些实验中，人类受试者被要求根据顺序呈现的多条信息做出决定。基于顺序证据做出的决策不仅在实际经济生活中很重要，而且作为研究对象还有一个好处，那就是它们允许我们调查每一条信息在决策中是如何被单独考虑的。这揭示了认知资源在处理每一件证据时的相对分配，这反过来又揭示了大脑面临的潜在限制。总体而言，我们研究的主要目标是提供建立在规范性原则基础上的人类判断和经济决策的描述，这些判断和经济决策可以推广到人类决策的许多问题。用更专业的术语，我们在经济决策的背景下调查神经认知假设，即大脑面临约束优化问题：即分配其有限的信息处理资源以实现可能的最佳决策。根据这一一般原理，我们得出了认知加工和决策的可测试量化模型，这些模型对人类选择中的偏差和随机性做出具体预测。具体地说，这些次优模式被预测为依赖于关于从中提取所呈现的证据的概率分布的先验信念，以及与给定上下文中的不同响应相关联的奖励。为了验证这一假设，我们设计了六个实验，在这些实验中，我们同时操纵了先前和奖励结构，并检查了我们提出的约束信息处理模型在多大程度上捕捉到了实验数据中发现的行为模式。这一奖励反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding how people make decisions is crucial to advancing our understanding of economic mechanisms. Rational-choice theory, despite successes in accounting for some aspects of human decision making under uncertainty, fails to capture certain recurrent patterns observed in behavior, such as biases and apparent randomness in choices. Some of these deviations from optimal behavior suggest that information is processed in the brain in a way that introduces imprecision, similar to the imprecision in sensory perception. A growing literature on perceptual judgments argues that in sensory contexts, these imprecisions often actually represent efficient adaptations, given constraints on the available cognitive resources. This raises a question as to whether patterns of imprecision in the case of higher-level cognitive processing (comparisons of numerical magnitudes, estimates of the average values of fluctuating series, and inference about unknown variables) cannot be understood in a similar way. We investigate this hypothesis through an array of experiments in which human subjects are asked to make decisions on the basis of multiple pieces of information, presented sequentially. Decisions made on the basis of sequential evidence are not only important in actual economic life, but also have the advantage as an object of study that they allow us to investigate how each piece of information is separately taken into account in the decision. This reveals the relative allocation of cognitive resources to the processing of each piece of evidence, which in turn sheds light on the underlying constraints faced by the brain. Overall, the main goal of our research is to provide an account of human judgments and economic decisions that is founded on normative principles, and that can be generalized to many problems of human decision-making.In more technical terms, we investigate, in the context of economic decisions, the neurocognitive hypothesis that the brain faces a problem of constrained optimization: that of allocating its limited information-processing resources to achieve the best possible decisions. From this general principle, we derive testable quantitative models of cognitive processing and decision making that make specific predictions regarding the bias and randomness in human choices. In particular, these suboptimal patterns are predicted to depend on prior beliefs about the probability distribution from which the presented evidence is drawn, and on the rewards associated with different responses in a given context. To test this hypothesis, we design six experiments, in which we manipulate both the prior and the reward structure, and we examine the degree to which our proposed models of constrained information processing capture the behavioral patterns found in experimental data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.