Human-like Affective Cognition in Foundation Models
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.
66eb7e7a2fc2ac253bc6ea99
Kanishk Gandhi
extracted_confirmed
2024-09-19T01:34:55.900Z
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/avatars/53b57690fe052ce6882bbfc87b11567c.svg
Kanishk Gandhi
user
obiwan96
66eb7e7a2fc2ac253bc6ea9a
Zoe Lynch
66eb7e7a2fc2ac253bc6ea9b
Jan-Philipp Fränken
66eb7e7a2fc2ac253bc6ea9c
Kayla Patterson
66eb7e7a2fc2ac253bc6ea9d
Sharon Wambu
66eb7e7a2fc2ac253bc6ea9e
Tobias Gerstenberg
66eb7e7a2fc2ac253bc6ea9f
Desmond C. Ong
66eb7e7a2fc2ac253bc6eaa0
Noah D. Goodman
claimed_verified
2024-11-15T09:44:13.540Z
67321274c1f20c742bcf7a8d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ltcQhre6eDRVzn6Vbbyhu.png
Noah D. Goodman
user
ngoodman
Human-like Affective Cognition in Foundation Models
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.
Foundation models match or exceed human performance in affective cognition tasks by evaluating diverse scenarios related to emotions, appraisals, expressions, and outcomes.
foundation models GPT-4 Claude-3 Gemini-1.5-Pro chain-of-thought reasoning