Automated discovery of symbolic laws governing skill acquisition from naturally occurring data

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Nature computational science Pub Date : 2024-05-24 DOI:10.1038/s43588-024-00629-0
Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, Zongkai Yang
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Abstract

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 an algorithmic explosion in searching. A deep learning model is initially employed to determine the learner’s cognitive state and assess the feature importance. Symbolic regression algorithms are then used to parse the neural network model into algebraic equations. Experimental results show that 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. This paper introduces an algorithm to uncover laws of skill acquisition from naturally occurring data. By combining deep learning and symbolic regression, it accurately identifies cognitive states and extracts algebraic equations.

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从自然发生的数据中自动发现支配技能习得的符号法则
技能习得是认知心理学的一个重要研究领域,因为它包含多种心理过程。在实验范式下发现的规律存在争议,缺乏普适性。本文旨在从大规模训练日志数据中发掘技能学习的规律。本文开发了一种两阶段算法,以解决认知状态不可观测和搜索算法爆炸的问题。首先采用深度学习模型来确定学习者的认知状态并评估特征的重要性。然后使用符号回归算法将神经网络模型解析为代数方程。实验结果表明,该算法能在连续反馈设置的噪声范围内准确还原预设规律。当应用于 Lumosity 训练数据时,该方法在适配性方面优于传统模型和最新模型。这项研究揭示了技能习得规律的两种新形式,并再次证实了之前的一些发现。
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