  Humans no doubt use language to communicate about their emotional
experiences, but does language in turn help humans understand emotions, or is
language just a vehicle of communication? This study used a form of artificial
intelligence (AI) known as large language models (LLMs) to assess whether
language-based representations of emotion causally contribute to the AI's
ability to generate inferences about the emotional meaning of novel situations.
Fourteen attributes of human emotion concept representation were found to be
represented by the LLM's distinct artificial neuron populations. By
manipulating these attribute-related neurons, we in turn demonstrated the role
of emotion concept knowledge in generative emotion inference. The
attribute-specific performance deterioration was related to the importance of
different attributes in human mental space. Our findings provide a
proof-in-concept that even a LLM can learn about emotions in the absence of
sensory-motor representations and highlight the contribution of
language-derived emotion-concept knowledge for emotion inference.
