Center for Neural Science, New York University, New York, NY 10003, USA.
Department of Neurobiology, Harvard University, Boston, MA 02115, USA.
Philos Trans R Soc Lond B Biol Sci. 2023 Sep 25;378(1886):20220344. doi: 10.1098/rstb.2022.0344. Epub 2023 Aug 7.
A key computation in building adaptive internal models of the external world is to ascribe sensory signals to their likely cause(s), a process of causal inference (CI). CI is well studied within the framework of two-alternative forced-choice tasks, but less well understood within the cadre of naturalistic action-perception loops. Here, we examine the process of disambiguating retinal motion caused by self- and/or object-motion during closed-loop navigation. First, we derive a normative account specifying how observers ought to intercept hidden and moving targets given their belief about (i) whether retinal motion was caused by the target moving, and (ii) if so, with what velocity. Next, in line with the modelling results, we show that humans report targets as stationary and steer towards their initial rather than final position more often when they are themselves moving, suggesting a putative misattribution of object-motion to the self. Further, we predict that observers should misattribute retinal motion more often: (i) during passive rather than active self-motion (given the lack of an efference copy informing self-motion estimates in the former), and (ii) when targets are presented eccentrically rather than centrally (given that lateral self-motion flow vectors are larger at eccentric locations during forward self-motion). Results support both of these predictions. Lastly, analysis of eye movements show that, while initial saccades toward targets were largely accurate regardless of the self-motion condition, subsequent gaze pursuit was modulated by target velocity during object-only motion, but not during concurrent object- and self-motion. These results demonstrate CI within action-perception loops, and suggest a protracted temporal unfolding of the computations characterizing CI. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
建立外部世界自适应内部模型的关键计算是将感觉信号归因于其可能的原因（多个），这是一个因果推断（CI）的过程。CI 在二选一强制选择任务的框架内得到了很好的研究，但在自然主义的行动感知循环框架内理解得较少。在这里，我们研究了在闭环导航过程中辨别由自身和/或物体运动引起的视网膜运动的过程。首先，我们得出了一个规范的解释，说明观察者应该如何拦截隐藏和移动的目标，给出他们对（i）视网膜运动是否由目标运动引起的信念，以及（ii）如果是，速度是多少。接下来，根据建模结果，我们表明，当人类自身移动时，他们更经常地报告目标为静止，并朝着目标的初始位置而不是最终位置转向，这表明对物体运动的潜在错误归因于自身。此外，我们预测观察者应该更频繁地错误归因于视网膜运动：（i）在被动而不是主动的自身运动期间（由于前者缺乏告知自身运动估计的传出副本），以及（ii）当目标呈现偏心而不是中央时（由于在向前自身运动期间，偏心位置的横向自身运动流矢量更大）。结果支持这两个预测。最后，眼动分析表明，尽管初始向目标的扫视在很大程度上是准确的，无论自身运动条件如何，但在仅物体运动期间，后续的凝视追踪会被目标速度调制，但在同时存在物体和自身运动期间不会。这些结果表明在行动感知循环中存在 CI，并表明 CI 所特征化的计算具有延长的时间展开。本文是主题问题“多感觉感知中的决策和控制过程”的一部分。
