Automated discovery of symbolic laws governing skill acquisition from
  naturally occurring data
Sannyuya Liu Qing Li Xiaoxuan Shen Jianwen Sun Zongkai Yang
Skill acquisition is a key area of research in cognitive psychology as it
encompasses multiple psychological processes. The laws discovered under
experimental paradigms are controversial and lack generalizability. This paper
aims to unearth the laws of skill learning from large-scale training log data.
A two-stage algorithm was developed to tackle the issues of unobservable
cognitive states and algorithmic explosion in searching. Initially a deep
learning model is employed to determine the learner's cognitive state and
assess the feature importance. Subsequently, symbolic regression algorithms are
utilized to parse the neural network model into algebraic equations. The
experimental results of simulated data demonstrate that the proposed algorithm
can accurately restore various preset laws within a certain range of noise, in
continues feedback setting. Application of proposed method to Lumosity training
data demonstrates superior performance compared to traditional and latest
models in terms of fitness. The results indicate the discovery of two new forms
of skill acquisition laws, while some previous findings have been reaffirmed.