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Related Experiment Video

Updated: Aug 12, 2025

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

Published on: April 18, 2017

Using cognitive psychology to understand GPT-3.

Marcel Binz¹, Eric Schulz¹

¹Max Planck Research Group (MPRG) Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.

Proceedings of the National Academy of Sciences of the United States of America

|February 2, 2023

Summary

We used cognitive psychology to test large language models like GPT-3. While impressive in many tasks, GPT-3 struggles with subtle changes and causal reasoning, revealing limitations in its decision-making and exploration.

Keywords:

artificial intelligence cognitive psychology decision-making language models reasoning

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Area of Science:

Cognitive Psychology
Artificial Intelligence
Machine Learning

Background:

Large language models (LLMs) like GPT-3 exhibit complex behaviors.
Understanding their cognitive-like abilities is crucial for AI development.

Purpose of the Study:

To assess GPT-3's decision-making, information search, deliberation, and causal reasoning.
To compare GPT-3's performance against human subjects in canonical cognitive tasks.

Main Methods:

Utilized a battery of established experiments from cognitive psychology literature.
Evaluated GPT-3's responses to vignette-based tasks and decision-making scenarios.
Assessed performance in a multiarmed bandit task and causal reasoning challenges.

Main Results:

GPT-3 matched or exceeded human performance on vignette-based tasks.
Demonstrated competent decision-making from descriptions and outperformed humans in a multiarmed bandit task.
Showed limitations with task perturbations, lacked directed exploration, and failed causal reasoning tasks.

Conclusions:

GPT-3 displays advanced capabilities but is sensitive to minor task variations.
Cognitive psychology tools offer valuable insights into LLM behavior.
Further research is needed to understand opaque artificial agents.