Department of Psychology, Arizona State University.
Department of Mathematics, Arizona State University.
Psychol Rev. 2018 Jan;125(1):117-130. doi: 10.1037/rev0000056.
The optimal strategy in detection theory is to partition the decision axis at a criterion C, labeling all events that score above C "Signal", and all those that fall below "Noise." The optimal position of C, C*, depends on signal probability and payoffs. If observers place their criterion at some place other than C*, they suffer a loss in the Expected Value (EV) of payoffs over the course of many decisions. We provide an explicit equation for the degree of loss, where it is shown that the falloff in value will be steep in contexts of good discrimination and will be a flatter gradient in contexts of poor discrimination. It is these gradients of loss in EV that, in theory, drive C toward C*, strongly when discrimination is good, weakly when discrimination is poor. When signal probabilities or distributions variances are unequal, the basins of attraction are asymmetric, so that dynamic adjustments in C will be asymmetric, and thus, as we show, will leave it biased. We address our analysis to acquisition speed, response variability, discrimination reversal and other aspects of discriminated performance. In the final section, we develop an error correction model that predicts empirically observed deviations from C* that are inconsistent with the standard model, but follow from the proposed model given knowledge of d'. (PsycINFO Database Record
在检测理论中，最佳策略是在准则 C 处对决策轴进行划分，将所有得分高于 C 的事件标记为“信号”，将所有低于 C 的事件标记为“噪声”。最佳的 C 位置（C*）取决于信号概率和收益。如果观察者将其准则置于 C以外的某个位置，他们将在多次决策过程中遭受收益的预期值（EV）损失。我们提供了一个明确的方程来表示损失的程度，其中表明，在良好的辨别力情况下，价值的下降将是陡峭的，而在辨别力较差的情况下，价值的下降将是较平坦的梯度。正是这些 EV 损失的梯度，理论上，在辨别力良好时会强烈地将 C 推向 C，在辨别力较差时则会较弱。当信号概率或分布方差不相等时，吸引域是不对称的，因此 C 的动态调整将是不对称的，因此，正如我们所展示的，它将产生偏差。我们将分析的重点放在获取速度、反应变异性、辨别力反转和其他辨别性能方面。在最后一节中，我们开发了一个错误纠正模型，该模型预测了与标准模型不一致的经验观察到的偏离 C*的情况，但从给定 d'的知识出发，这些情况符合所提出的模型。
