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Do large language models show decision heuristics similar to humans? A case study using GPT-3.5
Gaurav Suri1, Lily R Slater1, Ali Ziaee1
1Department of Psychology, San Francisco State University.
Journal of Experimental Psychology. General
|February 8, 2024
Summary
Large Language Models (LLMs) like ChatGPT exhibit cognitive biases, such as anchoring and framing effects, similar to humans. This suggests language itself may influence these context-sensitive responses.
Area of Science:
- Cognitive Science
- Artificial Intelligence
- Psychology
Background:
- Large Language Models (LLMs) are AI systems trained on extensive data to generate human-like text.
- Generative Pre-Trained Transformer (GPT)-3.5, powering ChatGPT, is a prominent example of an LLM.
- Human decision-making is known to be influenced by cognitive heuristics and context-sensitive responses.
Purpose of the Study:
- To investigate whether ChatGPT exhibits heuristics and context-sensitive responses.
- To compare ChatGPT's responses to those of human participants using identical prompts.
- To explore the role of language in cognitive biases.
Main Methods:
- Four studies were conducted using novel prompts to test ChatGPT and human participants.
- Study 1 examined the anchoring effect using random numerical anchors.
- Study 2 assessed representativeness and availability heuristics.
- Study 3 investigated the framing effect with positively and negatively presented information.
- Study 4 explored the endowment effect by comparing owned versus newly found items.
Main Results:
- ChatGPT demonstrated the anchoring effect, being influenced by random numerical anchors.
- ChatGPT exhibited representativeness and availability heuristics, overestimating joint probabilities.
- ChatGPT showed a framing effect, valuing items differently based on presentation.
- ChatGPT displayed an endowment effect, valuing owned items more than identical new items.
- Human participants exhibited similar heuristic and context-sensitive responses across all studies.
Conclusions:
- LLMs like ChatGPT can exhibit cognitive biases and context-sensitive responses, mirroring human behavior.
- The findings suggest that the structure and content of language may play a significant role in generating these effects.
- This research opens avenues for understanding the interplay between language, cognition, and AI behavior.
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