Abstract ↔ claims
run not observed · v1 provisional awaiting approvalWhich claims does the abstract carry, and which does it drop?
for Self-association enhances early attentional selection through automatic prioritization of socially salient signals · this layer across all papers · json
Provisional
This layer needs a corpus-scope decision that has not been ruled on yet, so what it produces would change if the decision changed. It waits on claim-format, relation-vocab.
Awaiting approval
Waiting for approval. That is a statement about the record, not about whether anyone has read this: people read the corpus without stamping what they read, and only a stamp leaves a trace. Approval is an operation on a version, not a step of its own — it is recorded against the version it was granted to, so running this layer again does not carry it forward.
What this layer produced
Abstract mapped to claims
The paper's abstract is shown with each sentence linked to the claim(s) it represents in the dependency graph. Hover or click a sentence to highlight the corresponding claim cards. Below: what the graph contains that the abstract leaves out, and vice versa.
1Efficiently processing self-related information is critical for cognition, yet the earliest mechanisms enabling this self-prioritization in humans remain unclear. 2By combining a temporal order judgement task with computational modeling based on the Theory of Visual Attention (TVA), we show how mere, arbitrary associations with the self can fundamentally alter attentional selection of sensory information into aware short-term memory, by enhancing the attentional weights and processing capacity devoted to encoding socially loaded information. 3This self-prioritization in attentional selection occurs automatically at early perceptual stages but reduces when active social decoding is required. 4Importantly, the processing benefits obtained from attentional selection via self-relatedness and via physical salience were additive, suggesting that social and perceptual salience captured attention via separate mechanisms. 5Furthermore, intra-individual correlations revealed an ‘obligatory’ self-prioritization effect, whereby self-relatedness overpowered the contribution of perceptual salience in guiding attentional selection. 6Together, our findings provide evidence for the influence of self-relatedness during earlier, automatic stages of attentional selection at the gateway to perception, distinct from later post-attentive processing stages.
- C2 spe-robust-matching-both-experiments fig8 Strong self-prioritization effects are present in the shape-label matching task in both experiments: Experiment 1 (N=69) d = -1.064 [CI95: -1.38 to -0.75], BF10 = 3.23×10^95; Experiment 2 (N=71) d = -0.982 [CI95: -1.20 to -0.77], BF10 = 4.47×10^109, confirming participants learned and retained the self-associations used in the TOJ task.
- H1.P1 prediction-capacity-model-outperforms-weights-only prediction A condition-specific TVA capacity model (indiv-C) should outperform a fixed-capacity, weights-only model on LOO cross-validation if the mechanism is capacity change rather than pure weight redistribution.
How it is defined
A model answers this layer, so the prompt is the layer. It is reproduced below from the committed file, and it is a declared input — editing it makes every run that used it stale.
The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.
Artifacts
Versions
From the run ledger. There is no changelog beside it to keep in step.
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v1 · 2026-04-20 · unrecorded backfilled from the artifact
backfilled from the artifact on disk
This layer across the corpus
Across the corpus
9 run not observed · 1 stale·a paper links to its own cell, where this layer's output for it is rendered
Inputs and outputs
- Reads, besides its dependencies
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- extract/prompts/abstract-map.md · declared, and not in the repository — it hashes to nothing, so it cannot make a run stale
- Produces
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- site/src/data/abstract-mapping/{paper}.json
One per paper — the table above links each one that exists.
- Views
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- document — rendered above, from the artifact itself
- comparison — on the cell page, two versions aligned by the matcher, wherever the ledger holds more than one
Running it
The command comes from the declaration, so this text and what actually runs cannot
diverge. pipeline.py run also runs the unmet dependencies first.
python3 scripts/pipeline.py run <paper> abstract-map
Underneath, that runs cd extract && python3 -m claim_graphs.cli abstract-map --paper {paper}.