Computational modeling demonstrates that happiness tracks the combined influence of recent reward expectations and prediction errors, replicated in over 18,000 participants

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• 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.finding0.792
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• Mood is a running average of recent reward prediction errors, functioning as a meta-learning signal, supported by converging computational and neural evidencefinding0.791
  Evidence that phenomenal mood state tracks RL-style prediction error aggregates
• A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model's power).claim0.770
  Prediction orthogonality thesis.
• Hebbian learning in a self-modelling dynamical system effects a positive feedback on correlations; the more things co-occur, the more the connection between them changes to make them more likely to co-occur in future.claim0.767
  Describes the self-reinforcing nature of Hebbian learning in networks.
• 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.
• In the absence of external reward, babies and scientists and others explore their world. Using some sort of adaptive predictive world model, they improve their ability to answer questions such as what happens if I do this or that?quote0.761
  Schmidhuber (2006) characterization of epistemic curiosity used to frame the paper's approach
• Behavioural patterns associated with subjective experiences in humans are considered valid for inferring cognition in non-human animals but not in diverse other systems including plants.claim0.760
  The double standard pointed out by S&C and endorsed by the authors.
• The model tends to reflect more when the question is difficult, and accuracy is generally lower for harder questionshypothesis0.760
  Hypothesis explaining negative correlation between reflection rate and accuracy without implying reflection is harmful