Abstract ↔ claims
run not observed · v1 provisional awaiting approvalWhich claims does the abstract carry, and which does it drop?
for Distinct representational properties of cues and contexts shape fear and reversal learning · 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.
1When we learn that something is dangerous, a fear memory is formed. 2However, this memory is not fixed and can be updated through new experiences, such as learning that the threat is no longer present. 3This process of updating, known as extinction or reversal learning, is highly dependent on the context in which it occurs. 4How the brain represents cues, contexts, and their changing threat value remains a major question. 5Here, we used functional magnetic resonance imaging and a novel fear learning paradigm to track the neural representations of stimuli across fear acquisition, reversal, and test phases. 6We found that initial fear learning creates generalized neural representations for all threatening cues in the brain’s fear network. 7During reversal learning, when threat contingencies switched for some of the cues, two distinct representational strategies were observed. 8On the one hand, we still identified generalized patterns for currently threatening cues, whereas on the other hand, we observed highly stable representations of individual cues (i.e. item-specific) that changed their valence, particularly in the precuneus and prefrontal cortex. 9Furthermore, we observed that the brain represents contexts more distinctly during reversal learning. 10Furthermore, additional exploratory analyses showed that the degree of this context specificity in the prefrontal cortex predicted the subsequent return of fear, providing a potential neural mechanism for fear renewal. 11Our findings reveal that the brain uses a flexible combination of generalized and specific representations to adapt to a changing world, shedding new light on the mechanisms that support cognitive flexibility and the treatment of anxiety disorders via exposure therapy.
- E1 behavioral-learning-confirms-contingencies fig2A US expectancy ratings follow the hierarchy CS++ > CS+- > CS-+ > CS-- across all experimental phases (LME: CS type F=479.35, p<0.0001; phase F=125.6, p<0.001; interaction p<0.001), confirming participants learned threat contingencies and their reversals.
- E4 cs-plus-univariate-fear-network-acquisition fig2Bi During fear acquisition, threatening cues (CS+) produce significantly greater BOLD activation than safe cues (CS-) in dACC, superior frontal gyrus, caudate nucleus, and middle temporal gyrus, replicating the canonical fear network activation pattern.
- E6 current-threat-activates-fear-network-reversal fig2Bii During reversal, currently threatening cues (CS++ and CS-+) produce greater BOLD activation than non-threatening cues (CS+- and CS--) across the same fear network regions as acquisition (dACC, SFG, MTG, IFG), reflecting rapid updating of neural threat responses.
- I1 prior-threat-activates-fear-network-weakly fig2Biii During reversal, cues that were threatening during acquisition but not currently threatening (CS++) vs those that were safe during acquisition and currently threatening (CS-+) still show fear network activation — (CS++ > CS+-) > (CS-+ > CS--) — though to a lesser extent than the current-threat contrast, suggesting a lingering prior fear memory trace.
- C2 no-bold-differences-test-phases fig2B (test phases) During both test phases (test_new and test_old), no significant BOLD activation differences between any CS type contrasts are found, despite significant US expectancy differences at the behavioral level, motivating RSA over univariate analysis.
- D3 no-item-stability-difference-acquisition fig3A During fear acquisition, item stability (within-cue neural pattern similarity) does not differ between CS+ and CS- cues anywhere in the brain, showing that threat learning selectively increases cross-item generalization but not single-item representational consistency.
- E5 cue-generalization-limited-dacc-reversal-consistent fig3Bi During reversal, cue generalization for CS++ (always threatening) vs CS-- (always safe) is elevated only in dACC, not in the broader fear network regions where this effect was present during acquisition, suggesting that consistent threat representations are maintained more narrowly.
- E7 item-stability-persists-test-phases fig3C, fig3D Item stability (but not cue generalization) persists into test phases in the absence of a US: CS+- > CS++ item stability in MTG at test_new, and CS++ > CS-- item stability in inferior temporal gyrus at test_old, showing that individual-item memory traces outlast the training context.
- D1 ifg-reinstates-reversal-traces-item-specific fig4Bi In IFG during test_old, item reinstatement is higher for reversal memory traces than for acquisition or test_new memory traces (F(2,253)=5.50, p<0.01), showing that IFG preferentially reinstates the most recently learned item-specific representations.
- D1 dmpfc-reinstates-acquisition-traces-generalized fig4Bii In dmPFC during test_old, generalized reinstatement is higher for acquisition memory traces than for test_new memory traces (F(2,259)=4.01, p<0.05; t(259)=2.96, p<0.05), showing that dmPFC preferentially reinstates category-level (generalized) acquisition representations.
- E2 context-specificity-predicts-acquisition-reinstatement-regional-dissociation fig5Di Higher reversal context specificity (PFC) interacts with CS type to predict generalized reinstatement of acquisition memory traces in ACC/SFG (favoring initially threatening CS+-, t(22)=6.25, p<0.05) and precuneus (favoring initially safe CS-+, t(22)=4.89, p<0.01), with opposite directions across regions.
- E3 context-specificity-predicts-reversal-reinstatement-dmpfc fig5Dii Higher reversal context specificity (PFC) predicts greater item reinstatement of CS-+ than CS+- reversal memory traces in dmPFC during test_old (t(22)=5.56, p<0.05), favoring reinstatement of threatening reversal memories in the same region that generalizes acquisition traces.
- C1 context-specificity-predicts-reinstatement-new-context-mtg fig5Diii Higher reversal context specificity (PFC) predicts greater item reinstatement of CS-+ than CS+- acquisition memory traces in MTG during test_new (t(22)=2.51, p<0.05), the phase with entirely new contexts, suggesting context specificity generalizes beyond the training context.
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}.