Cognitive Thermometers: Machine Learning and Logical Complexity

Key points

• How does the human mind represent semantic categories?1
• Why do natural languages favor certain meanings over others?1
• We review emerging evidence that logic and machine learning often yield converging results on relative complexity and its resulting effects in semantic typology.1
• We argue that treating machine learning models as "cognitive thermometers" enables a unified approach to complexity that bridges symbolic logic and connectionist AI.1

Sources (1)

• [1]Cognitive Thermometers: Machine Learning and Logical Complexity
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:04 PM

  In this article, we propose that machine learning provides a somewhat more agnostic approach to measuring semantic complexity.

  How does the human mind represent semantic categories?

Extractive summary: sentences quoted from the sources.