title: Human-like Affective Cognition in Foundation Models
summary: 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.
paper_authors._id: 66eb7e7a2fc2ac253bc6ea99
paper_authors.name: Kanishk Gandhi
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-19T01:34:55.900Z
paper_authors._id: 63e6a880f2e9a8f22c5a1630
paper_authors.avatarUrl: /avatars/53b57690fe052ce6882bbfc87b11567c.svg
paper_authors.fullname: Kanishk Gandhi
paper_authors.type: user
paper_authors.user: obiwan96
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9a
paper_authors.name: Zoe Lynch
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9b
paper_authors.name: Jan-Philipp Fränken
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9c
paper_authors.name: Kayla Patterson
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9d
paper_authors.name: Sharon Wambu
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9e
paper_authors.name: Tobias Gerstenberg
paper_authors._id: 66eb7e7a2fc2ac253bc6ea9f
paper_authors.name: Desmond C. Ong
paper_authors._id: 66eb7e7a2fc2ac253bc6eaa0
paper_authors.name: Noah D. Goodman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:44:13.540Z
paper_authors._id: 67321274c1f20c742bcf7a8d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ltcQhre6eDRVzn6Vbbyhu.png
paper_authors.fullname: Noah D. Goodman
paper_authors.type: user
paper_authors.user: ngoodman
paper_title: Human-like Affective Cognition in Foundation Models
paper_summary: 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.
paper_ai_keywords: affective cognition foundation models psychological theory appraisals emotions expressions outcomes chain-of-thought reasoning