School of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Department of Psychology and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, United States of America.
PLoS Comput Biol. 2023 May 5;19(5):e1011116. doi: 10.1371/journal.pcbi.1011116. eCollection 2023 May.
Our duration estimation flexibly adapts to the statistical properties of the temporal context. Humans and non-human species exhibit a perceptual bias towards the mean of durations previously observed as well as serial dependence, a perceptual bias towards the duration of recently processed events. Here we asked whether those two phenomena arise from a unitary mechanism or reflect the operation of two distinct systems that adapt separately to the global and local statistics of the environment. We employed a set of duration reproduction tasks in which the target duration was sampled from distributions with different variances and means. The central tendency and serial dependence biases were jointly modulated by the range and the variance of the prior, and these effects were well-captured by a unitary mechanism model in which temporal expectancies are updated after each trial based on perceptual observations. Alternative models that assume separate mechanisms for global and local contextual effects failed to capture the empirical results.
我们的时长估计能够灵活地适应时间上下文的统计属性。人类和非人类物种表现出对先前观察到的时长均值以及序列依赖的感知偏差，即对最近处理过的事件时长的感知偏差。在这里，我们想知道这两种现象是源于单一机制，还是反映了分别适应环境全局和局部统计数据的两个独立系统的运作。我们采用了一组时长再现任务，其中目标时长是从具有不同方差和均值的分布中采样的。中心趋势和序列依赖偏差共同受到先前范围和方差的调制，这些效应可以通过一个单一的机制模型很好地捕捉到，在该模型中，时间期望在每次试验后基于感知观察进行更新。而假设全局和局部上下文效应有独立机制的替代模型无法捕捉到实验结果。
