# Large Language Model Recall Uncertainty is Modulated by the Fan Effect

arXiv: https://arxiv.org/abs/2407.06349 · HTML: https://mlsanity.com/paper/2407.06349/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling](https://mlsanity.com/paper/2312.15166/) (2023)
- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [CogBench: a large language model walks into a psychology lab](https://mlsanity.com/paper/2402.18225/) (2024)
- [Do Large Language Models know what humans know?](https://mlsanity.com/paper/2209.01515/) (2022)
- [minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models](https://mlsanity.com/paper/2203.13112/) (2022)
- [Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations](https://mlsanity.com/paper/2109.14989/) (2021)
- [Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5](https://mlsanity.com/paper/2305.04400/) (2023)
- [Do language models learn typicality judgments from text?](https://mlsanity.com/paper/2105.02987/) (2021)
- [How Powerful are Decoder-Only Transformer Neural Models?](https://mlsanity.com/paper/2305.17026/) (2023)
- [Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models](https://mlsanity.com/paper/2311.09194/) (2023)
- [LLM Cognitive Judgements Differ From Human](https://mlsanity.com/paper/2307.11787/) (2023)
- [Using Artificial Populations to Study Psychological Phenomena in Neural Models](https://mlsanity.com/paper/2308.08032/) (2023)
- [Do Large Language Models Learn Human-Like Strategic Preferences?](https://mlsanity.com/paper/2404.08710/) (2024)
- [The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performance](https://mlsanity.com/paper/2406.11634/) (2024)

## Built on by (most cited first, 7 of 7)

- [Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow](https://mlsanity.com/paper/2408.08651/) (2024)
- [Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis](https://mlsanity.com/paper/2409.01882/) (2024)
- [How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs](https://mlsanity.com/paper/2507.14307/) (2025)
- [Investigating Human-Aligned Large Language Model Uncertainty](https://mlsanity.com/paper/2503.12528/) (2025)
- [Basic Category Usage in Vision Language Models](https://mlsanity.com/paper/2503.12530/) (2025)
- [Human-Alignment and Calibration of Inference-Time Uncertainty in Large Language Models](https://mlsanity.com/paper/2508.08204/) (2025)
- [Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty](https://mlsanity.com/paper/2605.30675/) (2026)

## Abstract (arXiv)

This paper evaluates whether large language models (LLMs) exhibit cognitive fan effects, similar to those discovered by Anderson in humans, after being pre-trained on human textual data. We conduct two sets of in-context recall experiments designed to elicit fan effects. Consistent with human results, we find that LLM recall uncertainty, measured via token probability, is influenced by the fan effect. Our results show that removing uncertainty disrupts the observed effect. The experiments suggest the fan effect is consistent whether the fan value is induced in-context or in the pre-training data. Finally, these findings provide in-silico evidence that fan effects and typicality are expressions of the same phenomena.