社会背景下的世界似乎不同：人类实验数据的神经网络分析
The world seems different in a social context: a neural network analysis of human experimental data
摘要：
人类的感知和行为受到情境的影响，特别是在社会交往中。最近的一项研究表明，人类对视觉刺激的感知不同，这取决于他们是自己完成任务还是与机器人一起完成任务。具体而言，它被发现，集中趋势效应是强于在社会比非社会任务设置。然而，由社会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然没有得到很好的理解。在本文中，我们解决这个问题，通过训练一个人工神经网络的启发，预测编码理论对上述行为数据集。使用这个计算模型，我们调查是否在人类实验中的情境背景下所造成的行为的变化可以解释为不断修改的参数表达如何强烈的感官和先验信息影响感知。我们证明，它是可以复制人类的行为数据在个人和社会任务设置通过修改之前和感官信号的精度，表明社会和非社会任务设置可能实际上存在于一个连续体。与此同时，对训练过的网络的神经激活痕迹的分析提供了证据，表明在个人和社会条件下，信息在网络中以根本不同的方式编码。我们的研究结果强调了行为数据的计算复制对共享感知的潜在认知机制产生假设的重要性，并可能为神经科学领域的后续研究提供灵感。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
