Center for Neural Science, New York University, New York City, New York, United States of Amercia.
PLoS Comput Biol. 2021 Oct 29;17(10):e1009159. doi: 10.1371/journal.pcbi.1009159. eCollection 2021 Oct.
The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several "point-estimating" observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by "committing" to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.
视觉项目的空间分布使我们能够推断出世界上潜在原因的存在。例如，蚂蚁的空间聚集可以让我们推断出存在共同的食物来源。然而，最优推断需要在实际情况下整合计算上难以处理的大量世界状态。例如，基于 N 个空间分布的视觉项目，对是否存在共同原因进行最优推断，需要对潜在原因的位置和 2N 种可能的归属模式进行边缘化（每个项目可能与潜在原因有关或无关）。大脑如何进行这种推断？我们发现，与贝叶斯最优相比，主体行为在质上存在偏差，特别是在虚报率上出现了意想不到的 N（视觉项目的数量）的正效应。我们提出了几种“点估计”观测器模型，它们比贝叶斯模型更能拟合主体行为。它们都通过对生成模型的至少一个变量进行“承诺”来避免对生成模型的至少一个变量进行昂贵的计算边缘化。这些发现表明，当基于复杂的现实世界数据检测潜在原因时，大脑可能会实现贝叶斯模型的部分承诺变体。
