From Representation to Behavioral Control: An Empirical Study of Fine-Grained and Unified Emotional Signals in LLMs

Emotions are critical for human cognition and behavior, motivating the study of similar emotional signals in LLMs. Existing work is limited by narrow evaluation scopes focusing on subjective generation, and discrete emotion representations that overlook continuous variations and relations among emotions. To address these limitations, we conduct a systematic empirical study on the effects of emotional signals, supported by **E-STEER**, an interpretable fine-grained emotion steering framework designed as a research instrument. E-STEER models emotions in VAD space, providing a continuous and unified representation. Building on this representation, E-STEER identifies emotion-related latent features and enables quantitative intervention in hidden states. With the framework, we examine the impact of emotion on diverse tasks. The results reveal non-monotonic emotion-behavior relations consistent with established psychological theories, demonstrating how emotional signals systematically shape model behaviors. We further demonstrate the practical value of this signal as a fine-grained behavioral control factor for LLM applications, with jailbreak defense as a representative case.