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Language models and psychological sciences.
Giuseppe Sartori1, Graziella Orrù2
1Department of General Psychology, University of Padova, Padova, Italy.
Large language models (LLMs) show cognitive psychology task performance comparable to neurotypical adults, reviving associationist principles. Analyzing LLM errors and abilities offers insights into human cognition and intelligence mechanisms.
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Area of Science:
- Cognitive Science
- Artificial Intelligence
- Computational Neuroscience
Background:
- Large language models (LLMs) exhibit advanced reasoning and problem-solving skills.
- LLM performance challenges traditional critiques of associative models in cognitive psychology.
- Recent research explores the intersection of LLMs and cognitive science.
Purpose of the Study:
- To analyze recent findings on LLMs within cognitive science.
- To discuss how LLMs revive and demonstrate associationist principles.
- To explore LLMs as tools for understanding human intelligence and cognitive biases.
Main Methods:
- Analysis of recent literature at the intersection of LLMs and cognitive science.
- Examination of LLM capabilities in reasoning, problem-solving, and association.
- Comparative analysis of LLM performance and errors with human cognition.
Main Results:
- LLMs demonstrate abilities like long-distance associations, enabling complex reasoning, comparable to neurotypical adults.
- LLM limitations exist in causal cognition and planning, but phenomena like emergence suggest potential for growth.
- Methods like providing examples and increasing network dimensions enhance LLM abilities, mirroring human cognitive facilitation.
Conclusions:
- LLMs represent a promising development for cognitive modeling from an associationist perspective.
- Analyzing LLM errors provides valuable insights into human cognitive biases.
- Evaluating LLMs using cognitive psychology tools can advance understanding of the human mind's building blocks.