concept
active
concept:counterfactual-hypothesisCounterfactual Hypothesis
Ability to entertain competing hypotheses within one inference engine; proposed hallmark of mindful inference
Neighborhood — ranked by edge-count
Frameworks (1)
framework
- Hard Problem Of Consciousnessassociated_withChalmers' problem: why structural/functional criteria should correlate with subjective experience; acknowledged as unsolvable in 3rd person.
Claims (1)
claim
- Proposed criterion distinguishing conscious from non-conscious inference processes
Concepts (5)
concept
- Counterfactual Behaviorrelated_toThe behavior that would have occurred had the value of a causal variable been different while everything else remained the same; used as training labels in DAS/MAS.
- Counterfactualrelated_toThe output value a model produces when an interchange intervention forces certain variables to take values from source inputs.
- Counterfactual Thinkingrelated_toEncoding possible future or alternative states; bioelectric patterns can represent an alternative morphological target even in an intact animal.
- Counterfactual Representationrelated_to
- Bayesian Model Selectionassociated_withChoosing among candidate models based on model evidence.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- The state a neural network is placed in when its activations are modified via intervention
- The mental effort of holding models of how things could/should be different from actuality, contributing to compression stress.
- The claim in RL that any goal can be expressed as maximizing the expected cumulative sum of a scalar reward signal.
- Pre-recorded latent vector encoding the expected causal variable values post-intervention; used as ground truth in the CLMAS auxiliary loss.
- Auxiliary objective combining L2 and cosine losses against pre-recorded CL vectors to improve causal relevance when one model is causally inaccessible.
- MAS variant with an auxiliary CL loss objective for cases where one model is causally inaccessible, enabling ANN-BNN comparisons.
- Auxiliary training objective from Grant (2025) that constrains intervened representations to remain near natural distribution