Human-like Affective Cognition in Foundation Models
Authors: name: Kanishk Gandhi, name: Zoe Lynch, name: Jan-Philipp Fränken, name: Kayla Patterson, name: Sharon Wambu, name: Tobias Gerstenberg, name: Desmond C. Ong, name: Noah D. Goodman

Understanding emotions is fundamental to human interaction and experience.
Humans easily infer emotions from situations or facial expressions, situations
from emotions, and do a variety of other affective cognition. How adept
is modern AI at these inferences? We introduce an evaluation framework for
testing affective cognition in foundation models. Starting from psychological
theory, we generate 1,280 diverse scenarios exploring relationships between
appraisals, emotions, expressions, and outcomes. We evaluate the abilities of
foundation models (GPT-4, Claude-3, Gemini-1.5-Pro) and humans (N = 567) across
carefully selected conditions. Our results show foundation models tend to agree
with human intuitions, matching or exceeding interparticipant agreement. In
some conditions, models are ``superhuman'' -- they better predict modal human
judgements than the average human. All models benefit from chain-of-thought
reasoning. This suggests foundation models have acquired a human-like
understanding of emotions and their influence on beliefs and behavior.