Department of Economics, Columbia University, New York, NY, USA.
Nat Hum Behav. 2022 Aug;6(8):1142-1152. doi: 10.1038/s41562-022-01352-4. Epub 2022 May 30.
Humans differentially weight different stimuli in averaging tasks, which has been interpreted as reflecting encoding bias. We examine the alternative hypothesis that stimuli are encoded with noise and then optimally decoded. Under a model of efficient coding, the amount of noise should vary across stimuli and depend on statistics of the stimuli. We investigate these predictions through a task in which the participants are asked to compare the averages of two series of numbers, each sampled from a prior distribution that varies across blocks of trials. The participants encode numbers with a bias and a noise that both depend on the number. Infrequently occurring numbers are encoded with more noise. We show how an efficient-coding, Bayesian-decoding model accounts for these patterns and best captures the participants' behaviour. Finally, our results suggest that Wei and Stocker's "law of human perception", which relates the bias and variability of sensory estimates, also applies to number cognition.
人类在平均任务中会对不同的刺激进行不同的加权，这被解释为反映了编码偏差。我们考察了替代假设，即刺激是带有噪声进行编码的，然后进行最佳解码。在有效的编码模型下，噪声的量应该因刺激而异，并取决于刺激的统计信息。我们通过一项任务来研究这些预测，在这项任务中，参与者被要求比较两个数字序列的平均值，每个序列都是从试验块中变化的先验分布中采样的。参与者用依赖于数字的偏差和噪声来编码数字。不常出现的数字的编码噪声更大。我们展示了一个高效编码、贝叶斯解码模型如何解释这些模式，并最好地捕捉参与者的行为。最后，我们的结果表明，Wei 和 Stocker 的“人类感知定律”，即感官估计的偏差和可变性，也适用于数字认知。
