HOT-1: Generative, top-down or noisy perception modules

Indicator: multiple sources of perceptual activity including top-down and noise required for reality monitoring.

Neighborhood — ranked by edge-count

Papers (1)

paper

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
introduces

Frameworks (1)

framework

Perceptual Reality Monitoring Theory (PRM)
associated_with
A computational HOT claiming that consciousness depends on metacognitive monitoring distinguishing reliable perceptual representations from noise.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

HOT-2: Metacognitive monitoring distinguishing reliable perceptual representations from noiseconcept0.806
Indicator: reality monitoring mechanism assigning veracity probabilities.
HOT-3: Agency guided by a general belief-formation system, with strong disposition to update beliefs per monitoring outputsconcept0.752
Indicator: consumer system that relies on 'real' tagged perceptions for rational action.
HOT-4: Sparse and smooth coding generating a 'quality space'concept0.738
Indicator: representation format providing qualitative character via similarity/discriminability.
Models with 1-hot activation sparsity still have polysemantic neurons; single neuron trained on 4 mutually exclusive features prefers polysemantic representation with loss ~0.7 vs 0.8finding0.712
Counter-example disproving that architectural sparsity alone can prevent polysemanticity
There is a many-to-many mapping between neurons and concepts, meaning multiple high-level causal variables might be encoded in overlapping groups of neuronsclaim0.706
Fundamental theoretical claim motivating DAS, attributed to Smolensky/Rumelhart/McClelland.
It's tricky, because for a typical language model the entity is sort of tricameral: the base simulator, the simulated simulator, and the simulated awareness.quote0.706
Antra's earlier definitive statement of the tricameral model.
GWT-1: Multiple specialised systems capable of operating in parallel (modules)concept0.706
Indicator from GWT: existence of specialized parallel modules.
InceptionV1 implements a four-layer circuit for pose-invariant dog head detection with mirrored left/right pathways that inhibit each other then unite, exhibiting XOR-like propertiesfinding0.704
Evidence that neural networks learn sophisticated invariance mechanisms through structured circuits rather than loose feature aggregation