AI Article Synopsis
- Humans and machines often learn without direct feedback or supervision, relying heavily on unsupervised data.
- There is debate around whether unsupervised learning is beneficial for humans, with mixed empirical results suggesting that self-reinforcement of predictions can be advantageous or detrimental based on the alignment of those predictions with the task.
- The authors propose a framework to explain these mixed results and offer insights into effective learning strategies relevant to education and lifelong learning.
Article Abstract
Humans and machines rarely have access to explicit external feedback or supervision, yet manage to learn. Most modern machine learning systems succeed because they benefit from unsupervised data. Humans are also expected to benefit and yet, mysteriously, empirical results are mixed. Does unsupervised learning help humans or not? Here, we argue that the mixed results are not conflicting answers to this question, but reflect that humans self-reinforce their predictions in the absence of supervision, which can help or hurt depending on whether predictions and task align. We use this framework to synthesize empirical results across various domains to clarify when unsupervised learning will help or hurt. This provides new insights into the fundamentals of learning with implications for instruction and lifelong learning.
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| http://dx.doi.org/10.1016/j.tics.2024.09.005 | DOI Listing |
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