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A language model of problem solving in humans and macaque monkeys
Qianli Yang1, Zhihua Zhu1, Ruoguang Si2
1Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
We developed a Language of Problem Solving (LoPS) model to analyze complex problem-solving behaviors in humans and monkeys. The model reveals evolving, hierarchical structures in problem-solving, offering insights into intelligence and its evolution.
Area of Science:
- Cognitive Science
- Comparative Psychology
- Computational Neuroscience
Background:
- Human intelligence excels at sequential problem-solving.
- Understanding the evolution of problem-solving is key to understanding intelligence.
- Cross-species comparisons are vital for uncovering cognitive mechanisms.
Purpose of the Study:
- Introduce the Language of Problem Solving (LoPS) model for quantitative analysis of problem-solving behavior.
- Investigate the structure and evolution of problem-solving strategies in humans and macaque monkeys.
- Compare problem-solving capacities and identify underlying grammatical structures across species.
Main Methods:
- Applied the novel LoPS model to gameplay data from humans and macaques in an adapted Pac-Man game.
- Utilized a language model framework to extract latent structures (grammars) from problem-solving sequences.
- Correlated LoPS grammar complexity with individual performance and species-level differences.
Main Results:
- The LoPS model identified non-Markovian temporal dependencies and hierarchical structures in problem-solving behavior for both species.
- LoPS grammar complexity correlated with game performance, differentiating human and macaque capacities.
- Both humans and monkeys demonstrated evolving LoPS grammars during learning, progressing from simple to complex structures.
Conclusions:
- Problem-solving structure is not fixed but evolves to enhance efficiency and sophistication.
- The study reveals insights into how humans and monkeys decompose complex tasks and navigate them.
- This work establishes a foundation for exploring the neural basis of problem-solving and intelligence evolution.
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