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Summary of Automated Discovery Of Symbolic Laws Governing Skill Acquisition From Naturally Occurring Data, by Sannyuya Liu et al.


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

by Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun, Zongkai Yang

First submitted to arxiv on: 8 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper investigates the laws of skill learning from large-scale training log data, addressing controversies surrounding experimental paradigms. To tackle issues of unobservable cognitive states and algorithmic explosion, a two-stage algorithm is developed. First, a deep learning model determines learner’s cognitive state and assesses feature importance. Then, symbolic regression algorithms parse neural network models into algebraic equations. Experimental results show the algorithm accurately restores preset laws within noise ranges in continuous feedback settings. The method outperforms traditional and recent models on Lumosity training data, revealing two new forms of skill acquisition laws and reaffirming some previous findings.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper looks at how people learn new skills from lots of practice data. They want to figure out the rules that govern learning, but old methods didn’t work well. The researchers create a special algorithm that uses two steps: first, it figures out what’s going on in someone’s brain while they’re practicing; then, it breaks down complex math into simpler equations. They test this method and find it works really well! It even beats other popular methods for learning from practice data. This study uncovers new rules for how we learn skills and confirms some old ideas.

Keywords

* Artificial intelligence  * Deep learning  * Neural network  * Regression