# Towards a Psychology of Machines: Large Language Models Predict Human Memory

arXiv: https://arxiv.org/abs/2403.05152 · HTML: https://mlsanity.com/paper/2403.05152/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 80 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study](https://mlsanity.com/paper/1706.08606/) (2017)
- [Mind meets machine: Unravelling GPT-4's cognitive psychology](https://mlsanity.com/paper/2303.11436/) (2023)
- [Is GPT a Computational Model of Emotion? Detailed Analysis](https://mlsanity.com/paper/2307.13779/) (2023)

## Built on by (most cited first, 4 of 4)

- [Human-like object concept representations emerge naturally in multimodal large language models](https://mlsanity.com/paper/2407.01067/) (2024)
- [CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics](https://mlsanity.com/paper/2407.02885/) (2024)
- [Judgment of Learning: A Human Ability Beyond Generative Artificial Intelligence](https://mlsanity.com/paper/2410.13392/) (2024)
- [MacBehaviour: An R package for behavioural experimentation on large language models](https://mlsanity.com/paper/2405.07495/) (2024)

## Abstract (arXiv)

Large language models (LLMs), such as ChatGPT, have shown remarkable abilities in natural language processing, opening new avenues in psychological research. This study explores whether LLMs can predict human memory performance in tasks involving garden-path sentences and contextual information. In the first part, we used ChatGPT to rate the relatedness and memorability of garden-path sentences preceded by either fitting or unfitting contexts. In the second part, human participants read the same sentences, rated their relatedness, and completed a surprise memory test. The results demonstrated that ChatGPT's relatedness ratings closely matched those of the human participants, and its memorability ratings effectively predicted human memory performance. Both LLM and human data revealed that higher relatedness in the unfitting context condition was associated with better memory performance, aligning with probabilistic frameworks of context-dependent learning. These findings suggest that LLMs, despite lacking human-like memory mechanisms, can model aspects of human cognition and serve as valuable tools in psychological research. We propose the field of machine psychology to explore this interplay between human cognition and artificial intelligence, offering a bidirectional approach where LLMs can both benefit from and contribute to our understanding of human cognitive processes.