# Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning

arXiv: https://arxiv.org/abs/2407.09281 · HTML: https://mlsanity.com/paper/2407.09281/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [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)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Language models show human-like content effects on reasoning tasks](https://mlsanity.com/paper/2207.07051/) (2022)
- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- [Turning large language models into cognitive models](https://mlsanity.com/paper/2306.03917/) (2023)
- [LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editing](https://mlsanity.com/paper/2402.10294/) (2024)
- [Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals](https://mlsanity.com/paper/2302.04449/) (2023)
- [A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity](https://mlsanity.com/paper/2204.10806/) (2022)
- [SpeedyIBL: A Comprehensive, Precise, and Fast Implementation of Instance-Based Learning Theory](https://mlsanity.com/paper/2111.10268/) (2021)
- [Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents](https://mlsanity.com/paper/2307.08171/) (2023)
- [Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models](https://mlsanity.com/paper/2308.09219/) (2023)

## Built on by (most cited first, 8 of 8)

- [Population-Aligned Persona Generation for LLM-based Social Simulation](https://mlsanity.com/paper/2509.10127/) (2025)
- [From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs](https://mlsanity.com/paper/2502.17701/) (2025)
- [Games Agents Play: Towards Transactional Analysis in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2507.21354/) (2025)
- [LLM-based Agent Simulation for Maternal Health Interventions: Uncertainty Estimation and Decision-focused Evaluation](https://mlsanity.com/paper/2503.22719/) (2025)
- [Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning](https://mlsanity.com/paper/2505.17249/) (2025)
- [Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance](https://mlsanity.com/paper/2509.09314/) (2025)
- [Dynamic Theory of Mind as a Temporal Memory Problem: Evidence from Large Language Models](https://mlsanity.com/paper/2603.14646/) (2026)
- [LLMs Should Not Yet Be Credited with Decision Explanation](https://mlsanity.com/paper/2605.01164/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated their capabilities across various tasks, from language translation to complex reasoning. Understanding and predicting human behavior and biases are crucial for artificial intelligence (AI) assisted systems to provide useful assistance, yet it remains an open question whether these models can achieve this. This paper addresses this gap by leveraging the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks. These tasks involve balancing between exploitative and exploratory actions and handling delayed feedback, both essential for simulating real-life decision processes. We compare the performance of LLMs with a cognitive instance-based learning (IBL) model, which imitates human experiential decision-making. Our findings indicate that LLMs excel at rapidly incorporating feedback to enhance prediction accuracy. In contrast, the cognitive IBL model better accounts for human exploratory behaviors and effectively captures loss aversion bias, i.e., the tendency to choose a sub-optimal goal with fewer step-cost penalties rather than exploring to find the optimal choice, even with limited experience. The results highlight the benefits of integrating LLMs with cognitive architectures, suggesting that this synergy could enhance the modeling and understanding of complex human decision-making patterns.