Toward a unified theory of decision criterion learning in perceptual categorization
Toward a unified theory of decision criterion learning in perceptual categorization
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DOI：
10.1901/jeab.2002.78-567
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发表时间：
2002-11-01
影响因子：
2.7
通讯作者：
Maddox, WT
中科院分区：
文献类型：
--
作者：
Maddox, WT
最优的决策标准放置最大化了预期的回报，并要求对类别基本率（先验概率）和回报（错误和正确响应的成本和收益）敏感。当基本比率不相等时，人类的决策标准几乎是最优的，但是当收益不相等时，即使最优决策标准在两种情况下是相同的，也会观察到次优决策标准的放置。本文回顾了一系列研究，以检验这一发现的普遍性，并描述了决策标准学习的统一理论（Maddox & Dodd, 2001）。该理论假设两个关键机制在决策准则学习中起作用。一种机制涉及奖励和准确性最大化之间的竞争：观察者试图按照指示最大化奖励，但同时也重视准确性最大化。第二种机制涉及一个最大平坦假设，它假设观察者对最大奖励决策标准的估计是由目标奖励函数的陡峭程度决定的，目标奖励函数将期望奖励与决策标准的放置联系起来。用于发展和测试理论的实验要求每个观察者完成大量的试验，并参与实验的所有条件。这提供了对观察者的强化历史的最大控制，并允许关注个人行为概况。该理论被应用于决策标准学习问题，这些问题检验了类别可辨别性、收益矩阵乘法和加法效应、最优分类器的独立性假设以及不同类型的逐次反馈。在每一种情况下，该理论都很好地解释了数据，最重要的是，它为决策标准学习中涉及的心理过程提供了有用的见解。
Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning.