Mood is a running average of recent reward prediction errors, functioning as a meta-learning signal, supported by converging computational and neural evidence

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Computational modeling demonstrates that happiness tracks the combined influence of recent reward expectations and prediction errors, replicated in over 18,000 participantsfinding0.791
  Large-scale replication supporting the claim that subjective well-being maps onto prediction error structure
• A generalized model where Level 2 modulates all Level 1 precision parameters would capture mood, emotion, and broader phenomenology of mental states.hypothesis0.761
  Future work direction; extends current model beyond attentional precision to full space of emotional and metacognitive phenomena.
• Under reward shaping (G=100, H=-100, F=0), Active Inference scored 99.52, Bayesian RL 99.77, Q-learning 95.56, with nearly identical behavior between belief-based agents.finding0.755
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• Emotion refers to a state concept, so stateful representations in general may be more persistent across tokens.claim0.750
  Interpretive hypothesis offered to explain why emotion features are more persistent
• In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.740
  Abstract; central distinction.
• Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs (Laine et al. 2024)concept0.737
  Situational awareness dataset; cited for hypothesis that future models will better recall training information
• Emotion may refer to a state, and more stateful concepts in general tend to be more persistent across tokens than non-stateful onesclaim0.737
  Proposed mechanistic explanation for why emotion features are more persistent
• Persistent conversational context that produced emotion-relevant activations is a plausible driver of observed persistence resultsclaim0.736
  Authors' caveat that conversational context persistence rather than internal emotion state persistence could explain findings