Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
This work addresses the challenge of deriving symbolic laws from real-world data for cognitive psychology, offering a more generalizable approach compared to experimental paradigms, though it is incremental in combining existing techniques.
The paper tackled the problem of discovering generalizable laws of skill acquisition from large-scale training logs by developing a two-stage algorithm that combines deep learning and symbolic regression. The method accurately recovered preset laws in simulations and outperformed existing models on Lumosity data, revealing two new forms of skill acquisition laws.
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. Experimental results show the algorithm can accurately restore preset laws within a noise range in continuous feedback settings. When applied to Lumosity training data, the method outperforms traditional and recent models in fitness terms. The study reveals two new forms of skill acquisition laws and reaffirms some previous findings.