RPT-2: Input modules generating organised, integrated perceptual representations

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

• RPT-1: Input modules using algorithmic recurrenceconcept0.822
  Indicator derived from RPT: use of algorithmic recurrence in input modules.
• PP-1: Input modules using predictive codingconcept0.753
  Indicator from PP: use of predictive coding in input modules.
• HOT-2: Metacognitive monitoring distinguishing reliable perceptual representations from noiseconcept0.739
  Indicator: reality monitoring mechanism assigning veracity probabilities.
• Incorporating machine learning provides objective standards that help mitigate subjectivity in emergence identification.claim0.729
  Authors argue ML optimizers act as objective observers.
• Precise characterization of the perceptual representational format — specific, sustained, globally constitutive — remains an open problemconcept0.725
  Acknowledged gap in CIMC's theory: the exact computational description of the perceptual regime is not yet specified
• what is the precise characterization of the perceptual representational format in which information exists as a specific, sustained, globally constitutive structure?question0.725
  Open theoretical problem CIMC acknowledges: precisely characterizing the representational format of perception
• AE-2: Embodiment: Modeling output-input contingencies and using the model in perception or controlconcept0.724
  Indicator of embodiment requiring forward models used for perception or motor control.
• Perceptual learning is literally an integral part of value learning, necessary to integrate out dependencies on inferred causes of sensory information.claim0.718
  Core unifying claim: perception and value-learning are unified through free energy minimization.