Ni Long, Stocker Alan A
Department of Psychology, University of Pennsylvania, USA.
Department of Psychology, University of Pennsylvania, USA.
Cognition. 2023 Mar;232:105334. doi: 10.1016/j.cognition.2022.105334. Epub 2022 Dec 5.
Not every item in a stimulus ensemble equally contributes to the perceived ensemble average. Rather, items with feature values close to the ensemble mean (inlying items) contribute stronger compared to those items whose feature values are further away from the mean (outlying items). This nonuniform weighting process, named robust averaging, has been interpreted as evidence against an optimal integration of sensory information. Here, however, we show that robust averaging naturally emerges from an optimal integration process when sensory encoding is efficiently adapted to the ensemble statistics in the experiment. We demonstrate that such a model can accurately fit several existing datasets showing robust perceptual averaging in discriminating low-level stimulus features such as orientation. Across various feature domains, our model accurately predicts subjects' decision accuracy and nonuniform weighting profile, and both their dependency on the specific stimulus distribution in the experiments. Our results suggest that the human visual system forms efficient sensory representations on short time-scales to improve overall decision performance.
并非刺激集合中的每个项目对感知到的集合平均值都有同等贡献。相反，与那些特征值远离平均值的项目（异常值项目）相比，特征值接近集合平均值的项目（内值项目）贡献更强。这种非均匀加权过程，称为稳健平均，被解释为反对感觉信息最优整合的证据。然而，在这里我们表明，当感觉编码在实验中有效地适应集合统计时，稳健平均自然地从最优整合过程中出现。我们证明，这样一个模型可以准确地拟合几个现有的数据集，这些数据集显示在区分诸如方向等低层次刺激特征时存在稳健的感知平均。在各种特征域中，我们的模型准确地预测了受试者的决策准确性和非均匀加权分布，以及它们对实验中特定刺激分布的依赖性。我们的结果表明，人类视觉系统在短时间尺度上形成有效的感觉表征，以提高整体决策性能。
