Coverage
stale · v3 provisional awaiting approvalWhat does the paper assert that no claim represents?
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.
Out of date
These inputs changed after this ran:
- claims/scheller-2026-self-prioritization/decisional-dimension-tradeoff.md
- claims/scheller-2026-self-prioritization/self-prioritization-perceptual-decision-automatic.md
- claims/scheller-2026-self-prioritization/self-salience-reduces-perceptual-benefit.md
- claims/scheller-2026-self-prioritization/self-social-additive-perceptual.md
What it produced28 spans.orphans
Read from coverage/scheller-2026-self-prioritization.json · 16 KB. claims 23panels.pct 33.3statistics.total 0statistics.pct 100spans.segmented 407spans.obligations 64spans.textual 343spans.accounted 36spans.pct 56.2
| # | uid | section | text | stats | panels |
|---|---|---|---|---|---|
| 1 | introduction-013 | introduction | As a model of attention-biased stimulus encoding, the Theory of Visual Attention (TVA; Bundesen, 1990 ) formally describes mechanisms underpinning prior entry (; Tünnermann et al., 2017 ; Figure 1 ). | [] | ["fig1"] |
| 2 | introduction-020 | introduction | Figure 1. Mechanisms of attentional selection. | [] | ["fig1"] |
| 3 | introduction-050 | introduction | (Experiment 1) (H2) Based on previous findings ( Constable et al., 2019 ; Jublie and Kumar, 2021 ; Truong et al., 2017 ), we hypothesized that participants would show a bias toward self-associated inf… | [] | ["fig2"] |
| 4 | introduction-051 | introduction | If self-relatedness also biases attentional selection automatically at the perceptual feature level, self-related information should show higher relative attentional weights/absolute processing rates … | [] | ["fig2"] |
| 5 | introduction-053 | introduction | Figure 2. Decision dimensions. | [] | ["fig2"] |
| 6 | introduction-068 | introduction | Using a TOJ task with stimuli in which shape features have been arbitrarily associated with the self and a stranger identity ( Figure 3 ), we measured whether mere social associations lead to a differ… | [] | ["fig3"] |
| 7 | introduction-072 | introduction | Figure 3. Task design. | [] | ["fig3"] |
| 8 | results-016 | results | Figure 4—figure supplement 1. Individual estimates: Absolute processing rates ( v p \begin{document}$v_{p}$\end{document} ) for the probe stimulus (self-associated), shown for individual participants … | [] | ["fig4s1"] |
| 9 | results-026 | results | Consequently, social salience introduced not only a change in attentional weights across the perceptual objects, but also a change in processing capacity (see Figure 1c ). | [] | ["fig1c"] |
| 10 | results-052 | results | Raw response data and parameter estimates for individual participants are provided in Figure 5—figure supplement 1 and 2 , respectively. | [] | ["fig5s1"] |
| 11 | results-053 | results | Figure 5—figure supplement 1. Individual psychometric functions: Individual participant response data indicating the proportion with which participants responded that the probe flickered first as a fu… | [] | ["fig5s1"] |
| 12 | results-058 | results | Figure 5—figure supplement 2. Individual estimates: absolute processing rates ( v p \begin{document}${v}_{p}$\end{document} ) for the probe stimulus (self-associated), shown for individual participant… | [] | ["fig5s2"] |
| 13 | results-096 | results | Figure 6—figure supplement 1. Individual psychometric functions: Individual participant response data indicating the proportion with which participants responded that the probe flickered first as a fu… | [] | ["fig6s1"] |
| 14 | results-098 | results | Figure 6—figure supplement 2. Individual estimates: Absolute processing rates ( v p \begin{document}$v_{p}$\end{document} ) for the probe stimulus (perceptually salient), shown for individual particip… | [] | ["fig6s2"] |
| 15 | results-109 | results | To that end, we added the processing rate changes resulting from perceptual salience alone and social associations alone and subtracted them from the processing rate changes in the condition in which … | [] | ["fig7s1"] |
| 16 | results-115 | results | Figure 7—figure supplement 1. Formalization of interaction: formalization of interaction effect assessment, using attentional weights. | [] | ["fig7s1"] |
| 17 | results-148 | results | For self-associated shapes, on the other hand, mere social salience effects were the best predictor ( B F i n c l u s i o n \begin{document}$BF_{inclusion}$\end{document} =2458.52) while a susceptibil… | [] | ["table2"] |
| 18 | results-152 | results | Coefficient P(incl) P(incl|Data) B F i n c l u s i o n \begin{document}$BF_{inclusion}$\end{document} Mean SD C I 95 \begin{document}$CI^{95}$\end{document} Lower C I 95 \begin{document}$CI^{95}$\end{… | [] | ["table2"] |
| 19 | captions-001 | captions | === Figure 1 === Figure 1. Mechanisms of attentional selection. | [] | ["fig1"] |
| 20 | captions-010 | captions | To arbitrate between these two mechanisms, we employed model comparisons to assess whether changes in relative attentional weight or changes in absolute processing rates (capacity and weights) better … | [] | ["fig2"] |
| 21 | captions-017 | captions | Note that the directionality of the sensory and social information does not make assumptions about the temporal dynamics of the underlying process. === Figure 3 === Figure 3. Task design. | [] | ["fig3"] |
| 22 | captions-047 | captions | Absolute processing rate changes for the probe (self-associated) and reference (other-associated) shapes, as well as their relative change, are shown on the bottom right. === Figure 4s1 === Figure 4—f… | [] | ["fig4s1"] |
| 23 | captions-054 | captions | Raw response data and parameter estimates for individual participants are provided in Figure 5—figure supplement 1 and 2 , respectively. === Figure 5s1 === Figure 5—figure supplement 1. Individual psy… | [] | ["fig5s1"] |
| 24 | captions-058 | captions | Furthermore, this participant shows a decreased proportion of ‘probe first’ responses when the probe was self-associated and the social identity had to be reported. === Figure 5s2 === Figure 5—figure … | [] | ["fig5s2"] |
| 25 | captions-067 | captions | Different conditions are shown in different shadings. === Figure 6s2 === Figure 6—figure supplement 2. Individual estimates: Absolute processing rates ( v p \begin{document}$v_{p}$\end{document} ) for… | [] | ["fig6s2"] |
| 26 | captions-072 | captions | The right panel shows the difference in perceptual-salience induced processing benefit between the self- and other-associated stimuli. === Figure 7s1 === Figure 7—figure supplement 1. Formalization of… | [] | ["fig7s1"] |
| 27 | captions-082 | captions | Bayes factors assessing the probability of a linear correlation and posterior estimation info is provided above each plot. === Figure 10 === Figure 10. Parameter estimates and their respective uncerta… | [] | ["fig10","fig11"] |
| 28 | tables-002 | tables | Coefficient P(incl) P(incl|Data) B F i n c l u s i o n \begin{document}$BF_{inclusion}$\end{document} Mean SD C I 95 \begin{document}$CI^{95}$\end{document} Lower C I 95 \begin{document}$CI^{95}$\end{… | [] | ["table2"] |
Artifacts
Versions
From the run ledger. There is no changelog beside it to keep in step.
-
v3 · 2026-09-13 · scripts/pipeline.py run
ran via scripts/pipeline.py
cd extract && python3 -m elife_extract.cli coverage --paper scheller-2026-self-prioritization --json ../coverage/scheller-2026-self-prioritization.json
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v2 · 2026-09-12 · scripts/pipeline.py run
re-run after prepare v2
cd extract && python3 -m elife_extract.cli coverage --paper scheller-2026-self-prioritization --json ../coverage/scheller-2026-self-prioritization.json
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v1 · 2026-09-11 · scripts/pipeline.py run
coverage for the nine papers that had none
cd extract && python3 -m elife_extract.cli coverage --paper scheller-2026-self-prioritization --json ../coverage/scheller-2026-self-prioritization.json
This layer across the corpus
Across the corpus
4 blocked upstream · 6 stale·a paper links to its own cell, where this layer's output for it is rendered
Inputs and outputs
- Produces
-
- coverage/{paper}.json
One per paper — the table above links each one that exists.
- Views
-
- document — declared, and this artifact is not the shape this view needs
- table — rendered above, over the 128 spans.orphans in the artifact
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> coverage
Underneath, that runs cd extract && python3 -m claim_graphs.cli coverage --paper {paper} --json ../coverage/{paper}.json.