Philippsen Anja, Tsuji Sho, Nagai Yukie
International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan.
Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan.
Front Neurorobot. 2022 Jun 20;16:856184. doi: 10.3389/fnbot.2022.856184. eCollection 2022.
Predictive coding has recently been proposed as a mechanistic approach to explain human perception and behavior based on the integration of perceptual stimuli (bottom-up information) and the predictions about the world based on previous experience (top-down information). However, the gap between the computational accounts of cognition and evidence of behavioral studies remains large. In this study, we used a computational model of drawing based on the mechanisms of predictive coding to systematically investigate the effects of the precision of top-down and bottom-up information when performing a drawing completion task. The results indicated that sufficient precision of both signals was required for the successful completion of the stimuli and that a reduced precision in either sensory or prediction (i.e., prior) information led to different types of atypical drawing behavior. We compared the drawings produced by our model to a dataset of drawings created by children aged between 2 and 8 years old who drew on incomplete drawings. This comparison revealed that a gradual increase in children's precision of top-down and bottom-up information as they develop effectively explains the observed change of drawing style from scribbling toward representational drawing. Furthermore, individual differences that are prevalent in children's drawings, might arise from different developmental pathways regarding the precision of these two signals. Based on these findings we propose a theory of how both general and individual development of drawing could be explained in a unified manner within the framework of predictive coding.
预测编码最近被提出作为一种机制方法，用于基于感知刺激（自下而上的信息）与基于先前经验对世界的预测（自上而下的信息）的整合来解释人类的感知和行为。然而，认知的计算模型与行为研究证据之间的差距仍然很大。在本研究中，我们使用了一个基于预测编码机制的绘画计算模型，系统地研究在执行绘画完成任务时自上而下和自下而上信息的精度的影响。结果表明，成功完成刺激需要两种信号都具有足够的精度，并且感觉或预测（即先验）信息中任何一种精度的降低都会导致不同类型的非典型绘画行为。我们将模型生成的绘画与2至8岁儿童在不完整绘画上进行创作的绘画数据集进行了比较。这种比较表明，随着儿童的成长，他们自上而下和自下而上信息的精度逐渐提高，这有效地解释了观察到的绘画风格从涂鸦向具象绘画的变化。此外，儿童绘画中普遍存在的个体差异可能源于这两种信号精度的不同发展路径。基于这些发现，我们提出了一种理论，即在预测编码框架内如何以统一的方式解释绘画的一般发展和个体发展。
