Speed-Accuracy Tradeoffs in Sample-Based Judgment and Choice

目前的研究项目主要集中在基于样本的决策中的速度和准确性权衡。随着两种选择的采样信息的增加，期望的准确率会提高，而决策速度会不可避免地降低。当提高准确性所花费的时间可以用于增加在可用时间内完成的正确决策的数量时，就会出现权衡。之前的研究在很大程度上忽略了这些权衡，然而这些权衡在日常生活的许多领域都很常见。在我们的初步工作中，开发了一个实验任务，允许参与者在做出选择之前确定采样的信息量。在每一项连续任务的试验中，参与者都要根据随机选择的n天这两种投资基金成功的二项随机样本，在两种投资基金之间做出选择。参与者每做一个正确的选择（在抽取样本的人群中成功率较高的选项）就会赢得1个收益单位，每做一个错误的选择就会损失1个收益单位。因为总收益是平均收益（准确性）和完成的选择数量（速度）的乘积，所以参与者必须既准确又快速。然而，至关重要的是，由于完成选项的数量随着n的增加而减少的速度快于准确性的增加，理论上可以预测的结果是戏剧性的准确性偏差。绝大多数参与者收集的样本太大，因此在很大程度上错过了完成选择的最佳数量。三个以心理学和经济学学生以及抑郁症患者为参与者的平行实验证实了这一基本结果。尽管为了让参与者体验到快速策略的优势而设计了几种操作（反馈、样本限制、选择难度、回报方案），但过度抽样仍然存在，这反映了明显的元认知缺陷。即使参与者只赢不输，他们也不愿意采用快速策略。所描述的项目的目标是理解并详细说明这一具有挑战性的现象。首先，我们将在一个新的领域复制所有初步发现，独立于货币投资的盈余含义。其次，计算机模拟将确定不同停止规则和采样模式下的最优策略。第三，在功能层面的方法中，我们研究了迫使参与者体验快速策略优势的各种干预措施的影响，以界定解决权衡的失败。项目的最后一部分是致力于对克里斯托弗·谢传播的可评估性概念的机械帐户的实证检验。因此，相对于准确选择所获得的收益的可评估性，当呈现格式使增加样本的信息成本更具可评估性时，可以消除准确性偏差，并且速度的主导影响将得到重视。

The present research project focuses on speed-accuracy tradeoffs in sample-based decisions. With increasing information sampled about two choice options, the expected accuracy increases while decision speed will decrease inevitably. A tradeoff arises as the time expended to increase accuracy can be used to increase the number of correct decisions completed within the available time. Previous research has largely neglected these tradeoffs, which are however quite common in many areas of everyday life. In our preliminary work, an experimental task was developed that allows participants to determine the amount of information sampled before they make a choice. On every trial of a sequential task, participants make a choice between two investment funds, based on binomial random samples of the two investment funds’ success on n randomly selected days. Participants win 1 payoff unit for each correct choice (of the option with the higher success rate in the population from which the sample is drawn) and they lose 1 unit for each incorrect choice. Because the total payoff is the product of the average payoff (accuracy) and the number of choices completed (speed), participants have to be both accurate and fast. However, crucially, because the number of completed choices decreases faster with increasing n than accuracy increases, a theoretically predictable finding is a dramatic accuracy bias. The vast majority of participants gather far too large samples, thereby missing the optimal number of completed choices by magnitudes. Three parallel experiments with students of psychology and economics and with depressed patients as participants corroborate this basic result. Oversampling persisted in spite of several manipulations (feedback; sample limit; choice difficulty; payoff scheme) devised to let participants experience the superiority of speedy strategies, reflecting a conspicuous metacognitive deficit. Even when participants could only win and never loose, they were reluctant to exploit speedy strategies. The goal of the depicted project is to understand and elaborate on this challenging phenomenon. First, we will replicate all preliminary findings in a new domain, independent of the surplus meaning of money investment. Second, computer simulations will determine optimal strategies for different stopping rules and sampling modes. Third, in a functional-level approach to delimit the failure to solve the tradeoff, we examine the impact of various interventions that force participants to experience the advantage of speedy strategies. The final part of the project is devoted to empirical tests of mechanistic account in terms of the evaluability notion propagated by Christopher Hsee. Accordingly, the accuracy bias can be eliminated and the dominant impact of speed will be appreciated when the presentation format renders the information cost of increasing samples more evaluable, relative to the evaluability of the payoff gained for accurate choices.