How can reward functions be meaningfully specified when the same outcome may be valuable or detrimental depending on context?

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• Reward Functionconcept0.773
  In RL, a scalar signal from the environment that defines the agent's goal; in active inference, reward is just another observation with associated preference.
• Humans prefer representative distributions over non-representative ones when evaluating outputs at the distribution level, providing a reward gap that VS exploitshypothesis0.750
  Assumption D.3 formalized in the theoretical framework; empirically validated with coin-flip sequence experiments
• For feedback to be meaningful, the end-result must be unpredictable; a predetermined end-state shuts off the possibility of adaptation.claim0.747
  Unpredictability is a necessary condition for genuine adaptation.
• Cognitive functions, including sentience, can potentially be achieved by very different systems and their disparate substrates.claim0.740
  Central multiple-realizability claim of the paper, from abstract and §2.
• Rewards are simply predictable stimuli (and aversive stimuli are, by definition, surprising)claim0.740
  Redefines reward and punishment in terms of predictability.
• it creates a ‘we’; what we are doing, for example, whether our behaviours are coordinated or not, becomes a relevant variable.quote0.739
  Illustrates how non-separable functions shift identity to the collective level.
• Reward Function Categoriesmethod0.737
  Seven categories determined by which components of f[h] are activated: Objective only, Expect only, Compare only, and combinations
• "any goal or purpose can be well thought of as maximization of the expected value of the cumulative sum of a received scalar signal (reward)"quote0.733
  The reward hypothesis underpinning RL, quoted from Sutton and Barto.