# Cognitive Effects in Large Language Models

arXiv: https://arxiv.org/abs/2308.14337 · HTML: https://mlsanity.com/paper/2308.14337/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [oLMpics -- On what Language Model Pre-training Captures](https://mlsanity.com/paper/1912.13283/) (2019)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019)

## Built on by (most cited first, 14 of 14)

- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [Exploring Autonomous Agents through the Lens of Large Language Models: A Review](https://mlsanity.com/paper/2404.04442/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions](https://mlsanity.com/paper/2508.18321/) (2025)
- [Aspects of human memory and Large Language Models](https://mlsanity.com/paper/2311.03839/) (2023)
- [Explaining Large Language Models Decisions Using Shapley Values](https://mlsanity.com/paper/2404.01332/) (2024)
- [Playing games with Large language models: Randomness and strategy](https://mlsanity.com/paper/2503.02582/) (2025)
- [Tacit Coordination of Large Language Models](https://mlsanity.com/paper/2601.22184/) (2026)
- [Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI](https://mlsanity.com/paper/2410.02820/) (2024)
- [Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning](https://mlsanity.com/paper/2411.17304/) (2024)
- [Do Language Models Agree with Human Perceptions of Suspense in Stories?](https://mlsanity.com/paper/2508.15794/) (2025)
- [Voter Priming Campaigns: Strategies, Equilibria, and Algorithms](https://mlsanity.com/paper/2412.13380/) (2024)
- [ExpertLens: Activation steering features are highly interpretable](https://mlsanity.com/paper/2502.15090/) (2025)
- [Pro-AI Bias in Large Language Models](https://mlsanity.com/paper/2601.13749/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) such as ChatGPT have received enormous attention over the past year and are now used by hundreds of millions of people every day. The rapid adoption of this technology naturally raises questions about the possible biases such models might exhibit. In this work, we tested one of these models (GPT-3) on a range of cognitive effects, which are systematic patterns that are usually found in human cognitive tasks. We found that LLMs are indeed prone to several human cognitive effects. Specifically, we show that the priming, distance, SNARC, and size congruity effects were presented with GPT-3, while the anchoring effect is absent. We describe our methodology, and specifically the way we converted real-world experiments to text-based experiments. Finally, we speculate on the possible reasons why GPT-3 exhibits these effects and discuss whether they are imitated or reinvented.