Department of Life Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan.
Sci Rep. 2020 Oct 20;10(1):17822. doi: 10.1038/s41598-020-74924-x.
Humans make decisions under various natural circumstances, integrating multiple pieces of information that are distributed over space and time. Although psychophysical and physiological studies have investigated temporal dynamics underlying perceptual decision making, weighting profiles for inliers and outliers during temporal integration have yet to be fully investigated in most studies. Here, we examined the temporal weighting profile of a computational model characterized by a leaky integrator of sensory evidence. As a corollary of its leaky nature, the model predicts the recency effect and overweights outlying elements around the end of the stream. Moreover, we found that the model underweights outlying values occurring earlier in the stream (i.e., robust averaging). We also show that human observers exhibit exactly the same weighting profile in an average estimation task. These findings suggest that the adaptive decision process in the brain results in the time-dependent decision weighting, the "peak-at-end" rule, rather than the peak-end rule in behavioral economics.
人类在各种自然环境下做出决策，整合分布在空间和时间上的多份信息。尽管心理物理学和生理学研究已经探究了知觉决策背后的时间动态，但在大多数研究中，对于时间整合过程中内群和外群的加权分布仍未被充分研究。在这里，我们研究了一个以感觉证据的漏泄积分器为特征的计算模型的时间加权分布。作为其漏泄性质的推论，该模型预测了近因效应，并在外群元素接近流尾时赋予过高权重。此外，我们发现该模型在流的早期（即稳健平均）低估了外群值。我们还表明，人类观察者在平均估计任务中表现出完全相同的加权分布。这些发现表明，大脑中的自适应决策过程导致了时间相关的决策加权，即“峰在尾”规则，而不是行为经济学中的峰终规则。
