Children adapt drawing actions to their own motor variability and to the motivational context of action. Issue 130 (October 2019)

• Record Type: Journal Article
• Authors: Shukri, Siti Rohkmah Mohd; Howes, Andrew
• Is Part Of: International journal of human-computer studies. Issue 130(2019)
• Page Start: 152
• Page End: 165
• Publication Date: 2019-10
• Subjects: Children's drawing -- Drawing strategies -- Motor variability -- Bayesian decision theory; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2019.06.004
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: The model predicts shifts in subjects drawing actions in response to changes of reward and penalty structures within the drawing environment and to subjects own motor uncertainty during rapid drawing movement. Our results show that children make near optimal adaptation to the reward signals and to their own motor performance variability. This suggests a viability of exciting new way to model childrens interaction with technology. Abstract: Children like to draw, but how easy is it for them to draw on a touch screen device? More specifically, how do children adapt the way they draw to the device and to their own motor limitations? To answer this question, we conducted empirical studies on children taking part in drawing tasks to examine how they adapt drawing actions to their own motor variability and to extrinsic motivation (rewards). Our study consisted of drawing tasks that tested the application of the Model of Movement Planning based on Bayesian Decision Theory. The model predicts shifts in subjects' drawing actions in response to changes of reward and penalty structures within the drawing environment and to subjects' own motor uncertainty during rapid drawing movement. Our results show that children make near optimal adaptation to the reward signals and to their own motor performance variability. This suggests a viability of exciting new way to model children's interaction with technology.