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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Employing large language models for cognitive heuristic stimulus validation in decision-making experiments.

Thom Hawkins¹, Daniel N Cassenti², Erin Zaroukian²

¹US Army Project Manager for Command & Control Data and Artificial Intelligence, Aberdeen Proving Ground, MD, USA. jeffrey.t.hawkins10.civ@army.mil.

Behavior Research Methods

|April 13, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) like ChatGPT can validate cognitive heuristic stimuli for decision-making research, offering a faster, more consistent alternative to human judges. This AI approach improves accuracy and reduces costs in experimental design.

Keywords:

Chain-of-thought prompting Cognitive heuristics Inter-rater reliability (IRR)Large language models (LLMs)Machine psychology Natural language processing (NLP)Stimulus validation

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Area of Science:

Cognitive Science
Artificial Intelligence
Decision Science

Background:

Traditional validation of cognitive heuristics in decision-making experiments is resource-intensive and prone to human bias.
Ensuring experimental stimuli accurately reflect intended cognitive heuristics presents interpretative consistency challenges.

Purpose of the Study:

To evaluate large language models (LLMs), specifically ChatGPT, as a tool for validating cognitive heuristic stimuli.
To assess the efficacy of LLMs in improving the precision and accuracy of heuristic labeling in decision-making rationales.

Main Methods:

Utilized ChatGPT to classify decision-making rationales into 10 distinct cognitive heuristics.
Employed iterative refinement and chain-of-thought prompting to address heuristic misalignment and ambiguity.

Main Results:

LLMs demonstrated effectiveness in aligning experimental rationales with intended heuristic categories.
Significant reductions in validation time and resource requirements were observed using LLM-based validation.
Improved precision and accuracy in heuristic labeling were achieved through LLM application.

Conclusions:

LLMs present a cost-effective and scalable alternative to traditional methods for validating cognitive heuristic stimuli.
Successful LLM deployment requires careful model selection, understanding of variability, and precise contextualization for complex domains.
Validated stimuli will inform future research on heuristic preferences and the design of AI-supported decision-support tools.