Abstract:

PURPOSE: Mental rotation of complex 3D structure is an important visuospatial ability. To examine the plasticity of this ability, traditional laboratory approaches rely on laborious data collection involving multiple training sessions, and thus are normally limited to small sample size and sometimes insuffcient training. Our study presented an innovative approach combining the advantages of game-based assessment and Internet-based data collection, and demonstrated the potential of this approach in providing new insights on the plasticity of visuospatial ability.
METHODS: In each session of a game-like assessment, participants were presented with a mental rotation problem in each trial, and the difficulty of each trial increased gradually. The number of successfully completed trials in a session was recorded as a score indexing the participants’ visuospatial ability. This game was distributed in a social-networking mobile application (i.e., WeChat), and a large sample (N = 119,662) of participants voluntarily played the game more than once. The changes in scores between sessions were used to illustrate the learning effeciency, or the plasticity, of the visuospatial ability. We asked how the plasticity of the visuospatial ability changed as a function of age.
RESULTS: The performance in visuospatial ability peaked at the age of 26-30, whereas the improvement of the performance was observed over repetitive participation in all ages, which peaked at the age of 15-20 and dramatically reduced over 50s. In short, the present study demonstrated that the visuospatial ability can be improved by training, and its plasticity varied across ages.
CONCLUSIONS: The present study was a ?rst attempt of using game-based assessment and large-scale internet-based data collection in cognitive plasticity, and illustrated the potential of this approach in large-scale data collection, especially in research fields requiring large sample size and repetitive participation.

Key words: Mental rotation, plasticity, game-based assessment, big data, individual difference