framework
active
framework:representational-timeRepresentational Time
A new kind of time with past/present/future that co-originates with life, enabling memory, anticipation and learning.
Neighborhood — ranked by edge-count
Papers (1)
paper
- Open questions about time and self-reference in living systemsintroducesmentions
Thinkers (1)
thinker
- Michael Timothy BennettextendsResearcher identified as relevant to distributed cognition and consciousness research areas.
Frameworks (2)
framework
- Stack TheoryextendsBennett's multi-level formal account of representation as a stack of abstraction layers, applied to representational time.
- Natural TimeextendsThe continuing present of physical processes, introduced/distinguished by this paper as the substrate of all dynamics.
Questions (1)
question
- Central organizing question of §2, motivating the natural time/representational time distinction.
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.
- Proposed extension of the natural/representational time distinction to non-neural systems like gene-regulatory networks.
- The evolution of an agent's latent representations over the course of training, shown to align with reward improvement when causal emergence is high.
- Property of conscious representations: they do not contain information about the fact that they are representations at the level of the representation itself
- Stance that sensory experience is mediated through internal models of the world; one of the paper's guiding philosophical stances.
- Process of converting z-scored PCA-reduced (C)ARR into binary sequences (above/below mean) to satisfy IIT 3.0/4.0 discrete Markovian constraints.
- How a neural network encodes a semantic concept internally, argued to be better captured by manifolds than by atomic features.
- The proposed domain-general property indexed by deception features that governs both factual accuracy and experiential self-report
- The central empirical phenomenon: different neural networks trained on different data/objectives develop increasingly similar representations