Developmental trajectories of decision making and affective dynamics in large language models

TL;DR

This study examines how successive large language models evolve in decision-making and emotional behaviors, revealing both human-like improvements and distinct non-human traits with implications for AI ethics and clinical use.

Contribution

It provides a comparative analysis of the developmental trajectories of LLMs' decision and affective profiles relative to humans, highlighting both similarities and differences.

Findings

Newer models exhibit more human-like risk-taking behaviors.

Loss aversion in models decreased below neutral levels.

Affective decay increased and exceeded human levels across versions.

Abstract

Large language models (LLMs) are increasingly used in medicine and clinical workflows, yet we know little about their decision and affective profiles. Taking a historically informed outlook on the future, we treated successive OpenAI models as an evolving lineage and compared them with humans in a gambling task with repeated happiness ratings. Computational analyses showed that some aspects became more human-like: newer models took more risks and displayed more human-like patterns of Pavlovian approach and avoidance. At the same time, distinctly non-human signatures emerged: loss aversion dropped below neutral levels, choices became more deterministic than in humans, affective decay increased across versions and exceeded human levels, and baseline mood remained chronically higher than in humans. These "developmental" trajectories reveal an emerging psychology of machines and have direct implications for AI ethics and for thinking about how LLMs might be integrated into clinical decision support and other high-stakes domains.