Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Sci Rep. 2020 Jul 2;10(1):10959. doi: 10.1038/s41598-020-67861-2.
Serial dependence, how immediately preceding experiences bias our current estimations, has been described experimentally during delayed-estimation of many different visual features, with subjects tending to make estimates biased towards previous ones. It has been proposed that these attractive biases help perception stabilization in the face of correlated natural scene statistics, although this remains mostly theoretical. Color, which is strongly correlated in natural scenes, has never been studied with regard to its serial dependencies. Here, we found significant serial dependence in 7 out of 8 datasets with behavioral data of humans (total n = 760) performing delayed-estimation of color with uncorrelated sequential stimuli. Moreover, serial dependence strength built up through the experimental session, suggesting metaplastic mechanisms operating at a slower time scale than previously proposed (e.g. short-term synaptic facilitation). Because, in contrast with natural scenes, stimuli were temporally uncorrelated, this build-up casts doubt on serial dependencies being an ongoing adaptation to the stable statistics of the environment.
序列依赖，即先前的经验如何影响我们当前的估计，已经在对许多不同视觉特征的延迟估计实验中得到了描述，实验表明，被试往往会使估计偏向于先前的估计。有人提出，这些有吸引力的偏差有助于在面对相关的自然场景统计数据时稳定感知，尽管这主要是理论上的。颜色在自然场景中是强相关的，但从未有研究关注过其序列依赖性。在这里，我们在 8 个数据集的 7 个数据集中发现了显著的序列依赖性，这些数据集是通过人类的行为数据（总 n=760）进行的，对颜色进行了延迟估计，使用的是不相关的顺序刺激。此外，序列依赖强度随着实验过程而增强，这表明在比以前提出的（例如短期突触易化）更慢的时间尺度上存在着可塑性机制。因为，与自然场景相反，刺激在时间上是不相关的，所以这种增强对序列依赖是对环境稳定统计数据的持续适应提出了质疑。
