Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Japan.
Dev Sci. 2018 May;21(3):e12565. doi: 10.1111/desc.12565. Epub 2017 Jun 20.
Pointing is one of the communicative actions that infants acquire during their first year of life. Based on a hypothesis that early pointing is triggered by emergent reaching behavior toward objects placed at out-of-reach distances, we proposed a neural network model that acquires reaching without explicit representation of 'targets'. The proposed model controls a two-joint arm in a horizontal plane, and it learns a loop of internal forward and inverse transformations; the former predicts the visual feedback of hand position and the latter generates motor commands from the visual input through random generation of the motor commands. In the proposed model, the motor output and visual input were represented by broadly tuned neural units. Even though explicit 'targets' were not presented during learning, the simulation successfully generated reaching toward visually presented objects at within-reach and out-of-reach distances.
指向是婴儿在生命的第一年获得的交际行为之一。基于这样一种假设，即早期的指向是由对放置在不可及距离处的物体的突发伸手行为引发的，我们提出了一个神经网络模型，该模型在没有对“目标”进行明确表示的情况下获得伸手行为。所提出的模型控制水平面上的两个关节臂，并学习内部正向和逆向变换的循环；前者预测手位置的视觉反馈，后者通过随机生成运动命令从视觉输入生成运动命令。在所提出的模型中，运动输出和视觉输入由广泛调谐的神经单元表示。即使在学习过程中没有明确呈现“目标”，模拟也成功地生成了对在可及和不可及距离内呈现的物体的伸手行为。
