Rutgers Center for Cognitive Science, Rutgers University.
Department of Psychology, Rutgers University.
Psychol Rev. 2014 Jan;121(1):96-123. doi: 10.1037/a0035232.
We present a computational model to explain the results from experiments in which subjects estimate the hidden probability parameter of a stepwise nonstationary Bernoulli process outcome by outcome. The model captures the following results qualitatively and quantitatively, with only 2 free parameters: (a) Subjects do not update their estimate after each outcome; they step from one estimate to another at irregular intervals. (b) The joint distribution of step widths and heights cannot be explained on the assumption that a threshold amount of change must be exceeded in order for them to indicate a change in their perception. (c) The mapping of observed probability to the median perceived probability is the identity function over the full range of probabilities. (d) Precision (how close estimates are to the best possible estimate) is good and constant over the full range. (e) Subjects quickly detect substantial changes in the hidden probability parameter. (f) The perceived probability sometimes changes dramatically from one observation to the next. (g) Subjects sometimes have second thoughts about a previous change perception, after observing further outcomes. (h) The frequency with which they perceive changes moves in the direction of the true frequency over sessions. (Explaining this finding requires 2 additional parametric assumptions.) The model treats the perception of the current probability as a by-product of the construction of a compact encoding of the experienced sequence in terms of its change points. It illustrates the why and the how of intermittent Bayesian belief updating and retrospective revision in simple perception. It suggests a reinterpretation of findings in the recent literature on the neurobiology of decision making.
我们提出了一个计算模型，用于解释实验结果，在这些实验中，被试通过逐个结果来估计逐步非平稳伯努利过程结果的隐藏概率参数。该模型定性和定量地捕捉到以下结果，仅使用 2 个自由参数：（a）被试不会在每次结果后更新他们的估计值；他们在不规则的时间间隔从一个估计值跳到另一个估计值。（b）步长和步高的联合分布不能用假设来解释，即必须超过一定量的变化才能表明他们的感知发生了变化。（c）观察到的概率与中位数感知概率之间的映射是全概率范围内的恒等函数。（d）精度（估计值与最佳可能估计值的接近程度）在全范围内都很好且保持不变。（e）被试可以快速检测到隐藏概率参数的显著变化。（f）感知概率有时会在一次观察到下一次观察之间发生急剧变化。（g）被试在观察到更多结果后，有时会对之前的变化感知产生第二次想法。（h）他们感知变化的频率在会话期间朝着真实频率的方向移动。（要解释这一发现，需要另外两个参数假设。）该模型将当前概率的感知视为根据其变化点构建经验序列紧凑编码的副产品。它说明了简单感知中间歇性贝叶斯信念更新和回顾性修正的原因和方式。它提示了对最近关于决策神经生物学文献中发现的重新解释。
