Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We examine whether LLMs "report" varying levels of motivation, how these reports relate to their behavior, and whether external factors can influence them. Our experiments reveal consistent and structured patterns that echo human psychology: self-reported motivation aligns with different behavioral signatures, varies across task types, and can be modulated by external manipulations. These findings demonstrate that motivation is a coherent organizing construct for LLM behavior, systematically linking reports, choices, effort, and performance, and revealing motivational dynamics that resemble those documented in human psychology. This perspective deepens our understanding of model behavior and its connection to human-inspired concepts.

翻译：动机是人类行为的关键驱动力，它塑造决策、目标和任务表现。随着大语言模型（LLMs）日益与人类偏好对齐，我们探究它们是否表现出某种类似于动机的特性。我们检验了LLMs是否“报告”不同水平的动机，这些报告如何与其行为相关联，以及外部因素是否能够影响它们。我们的实验揭示了与人类心理学相呼应的一致且结构化的模式：自我报告的动机与不同的行为特征相符，随任务类型而变化，并且能够被外部操控所调节。这些发现表明，动机是LLM行为的一个连贯的组织性构念，它系统性地将报告、选择、努力和表现联系起来，并揭示了与人类心理学中记载的动机动态相似的特性。这一视角深化了我们对模型行为及其与人类启发性概念之间联系的理解。