分析大模型决策与情绪演化，发现其越来越像人却也有非人特征。
Developmental trajectories of decision making and affective dynamics in large language models
- 将多代OpenAI模型视为演化谱系，对比人类在赌博任务中的表现。
- 新模型风险偏好上升，但损失厌恶低于人类，情绪衰减超人类水平。
- 适合关注AI伦理与临床决策支持的读者参考。
大型语言模型（LLMs）在医疗和临床流程中应用日益广泛，但我们对其决策与情绪特征了解甚少。本文以历史视角考察未来，将相继推出的OpenAI模型视为一个演化的谱系，并与人类在重复幸福感评分的赌博任务中进行比较。计算分析显示，部分特征趋于人类化：较新模型更愿意冒险，表现出更接近人类的巴甫洛夫式趋近与回避模式。然而，显著的非人类特征也浮现：损失厌恶降至中性以下，选择变得比人类更确定，情绪衰减随版本迭代增强且超过人类水平，基线情绪持续高于人类。这些“发展轨迹”揭示了机器心理的形成过程，对人工智能伦理及大模型在临床决策支持等高风险领域中的整合具有直接启示。
原文摘要 · Abstract (English)
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.
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