Human perception is based on unconscious inference, where sensory input integrates with prior information. This phenomenon, known as context dependency, helps in facing the uncertainty of the external world with predictions built upon previous experience. On the other hand, human perceptual processes are inherently shaped by social interactions. However, how the mechanisms of context dependency are affected is to date unknown. If using previous experience - priors - is beneficial in individual settings, it could represent a problem in social scenarios where other agents might not have the same priors, causing a perceptual misalignment on the shared environment. The present study addresses this question. We studied context dependency in an interactive setting with a humanoid robot iCub that acted as a stimuli demonstrator. Participants reproduced the lengths shown by the robot in two conditions: one with iCub behaving socially and another with iCub acting as a mechanical arm. The different behavior of the robot significantly affected the use of prior in perception. Moreover, the social robot positively impacted perceptual performances by enhancing accuracy and reducing participants overall perceptual errors. Finally, the observed phenomenon has been modelled following a Bayesian approach to deepen and explore a new concept of shared perception.
翻译:人类的感知是基于无意识的推断, 感官输入与先前的信息结合。 这种现象被称为环境依赖性, 有助于面对外部世界的不确定性, 其预测建立在以往经验的基础上。 另一方面, 人类的感知过程是社会互动所固有的。 然而, 环境依赖性机制如何受到影响, 迄今尚不清楚。 如果使用先前的经验- 先前的经验- 以往的经验- 对个人环境有利, 它可能会在社会情景中代表一个问题, 而在社会情景中, 其它物剂可能没有相同的前科, 导致对共享环境的感知性不匹配。 本研究探讨了这一问题。 我们研究了在与人造机器人iCub互动的环境中对背景的依赖性, 并研究了作为刺激性示范器的人类机器人iCub。 参与者复制了机器人在两种条件下显示的长度: 一个是iCub在社会上,另一个是iCub作为机械臂。 机器人的不同行为严重影响了先前感知的利用。 此外, 社会机器人通过提高准确性和减少参与者的整体感知性误差, 积极影响着视觉性表现。 最后, 观察到的现象是仿造的贝斯的模型, 深入探索。