Interactive Intelligence Group, Delft University of Technology, Delft, Netherlands.
Artificial Intelligence Department, Radboud University, Nijmegen, Netherlands.
PLoS One. 2022 Aug 30;17(8):e0273643. doi: 10.1371/journal.pone.0273643. eCollection 2022.
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.
人类的感知和行为受到情境语境的影响，特别是在社交互动中。最近的一项研究表明，人类对视觉刺激的感知会因独自完成任务还是与机器人一起完成任务而有所不同。具体来说，研究发现，在社交任务环境中，中心趋势效应比非社交任务环境更强。然而，社交互动引起的这种行为变化的特殊性质及其在人类大脑中的潜在认知过程仍未得到很好的理解。在本文中，我们通过在上述行为数据集上使用受预测编码理论启发的人工神经网络来解决这个问题。我们使用这个计算模型，研究了由人类实验中的情境语境引起的行为变化是否可以通过连续修改一个参数来解释，该参数表示感官和先验信息对感知的影响程度。我们证明，通过修改先验和感官信号的精度，可以复制个体和社交任务环境中的人类行为数据，这表明社交和非社交任务环境实际上可能存在于一个连续体中。同时，对训练网络的神经激活轨迹的分析提供了证据，表明信息在个体和社交条件下以根本不同的方式在网络中进行编码。我们的结果强调了对行为数据进行计算复制对于生成关于共享感知的潜在认知机制的假设的重要性，并可能为神经科学领域的后续研究提供启示。
