AE-2: Embodiment: Modeling output-input contingencies, including systematic effects, and using this model in perception or control

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

• Embodimentconcept0.799
  The idea that intelligence requires a body to interact with the world; expands beyond physical 3D bodies to virtual and interface-mediated forms.
• AE-1: Minimal agency: Learning from feedback and selecting outputs to pursue goals, especially with flexible responsiveness to competing goalsconcept0.780
  Agency and embodiment indicator; supported by GWT, PRM, and PP; key feature shared by animals and AI agents
• AE-1: Agency: Learning from feedback and flexible responsiveness to competing goalsconcept0.775
  Indicator of agency requiring goal pursuit and flexibility.
• What Role Does Embodiment Play In Developing Artificialquestion0.760
• Agency and Embodiment Framework (for consciousness)framework0.753
  Set of indicators involving minimal agency and embodiment as listed in Butlin et al. 2023.
• The model is able to express many subforms of a persona, with different elicitation methods eliciting different manifestationshypothesis0.751
  PSM-derived hypothesis supported by discourse-type facet analysis
• multiscale embodimentconcept0.747
  The recognition that intelligence is realized across many scales and substrates, requiring expansive ethical consideration.
• An interplay between causal abstraction and feature geometry deepens mechanistic understanding of language modelsclaim0.739
  Methodological claim about the scientific value of combining causal abstraction with representational geometry analysis