Human perception and behavior are affected by the situational context, in
particular during social interactions. A recent study demonstrated that humans
perceive visual stimuli differently depending on whether they do the task by
themselves or together with a robot. Specifically, it was found that the
central tendency effect is stronger in social than in non-social task settings.
The particular nature of such behavioral changes induced by social interaction,
and their underlying cognitive processes in the human brain are, however, still
not well understood. In this paper, we address this question by training an
artificial neural network inspired by the predictive coding theory on the above
behavioral data set. Using this computational model, we investigate whether the
change in behavior that was caused by the situational context in the human
experiment could be explained by continuous modifications of a parameter
expressing how strongly sensory and prior information affect perception. We
demonstrate that it is possible to replicate human behavioral data in both
individual and social task settings by modifying the precision of prior and
sensory signals, indicating that social and non-social task settings might in
fact exist on a continuum. At the same time an analysis of the neural
activation traces of the trained networks provides evidence that information is
coded in fundamentally different ways in the network in the individual and in
the social conditions. Our results emphasize the importance of computational
replications of behavioral data for generating hypotheses on the underlying
cognitive mechanisms of shared perception and may provide inspiration for
follow-up studies in the field of neuroscience.

本文研究人类在社交互动中感知和行为的变化，采用预测编码理论为基础的人工神经网络训练，发现修改感官信号及先验信号的精确度可模拟社交与非社交实验中的行为数据，并且分析神经网络的活化痕迹，发现社交环境下信息编码方式与个体环境有本质差异。