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Cognitive Thermometers: Machine Learning and Logical Complexity
Author1, Author2, Author3, Author4, Author5
semantic complexitymachine learninglogical definabilitycognitive science
2610.10724
Builder Relevance
2h ago70%
Abstract
This paper proposes that machine learning provides a more agnostic approach to measuring semantic complexity compared to traditional logical definability.
Reality Card
Core Claim
Machine learning models can serve as 'cognitive thermometers' that bridge the gap between symbolic logic and connectionist AI, offering a unified approach to measuring semantic complexity.
Method / Result
Emerging evidence shows that logic and machine learning often yield converging results on relative complexity.
Limitations
The reliance on specific logical languages may limit the generalizability of the findings.
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