Two Distinct Functional Networks for Successful Resolution of Proactive Interference
Bibliographic record
Abstract
In proactive interference (PI) paradigms, previous learning impairs the acquisition of new, related information. In rats, efficient resolution of PI relies on cholinergic modulation from the basal forebrain (BF). To test whether humans resolve PI using a functional network dependent on the medial septum/diagonal band of Broca (MS/DB) nuclei of the BF, we analyzed functional magnetic resonance imaging signal recorded while human participants learned to respond to baseline color paired associates and then additional pairs that interfered with the baseline pairs. Multivariate, partial least-squares analysis supported a MS/DB-dependent functional network: MS/DB activity covaried with activity in areas important to selective attention, including intraparietal sulcus, and memory that are direct cholinergic efferents of the MS/DB, including the hippocampus, as well as the ventrolateral prefrontal cortex, implicated in PI resolution. This network was associated with effective PI-resolution behavior. A second network also correlated with PI resolution but appearing not to be driven by the MS/DB, included the lateral orbitofrontal cortex. Patients with compromised BF function did not engage the MS/DB-dependent network reliably; instead their PI-resolution behavior was well explained by the second network. Thus, 2 functional networks may underly a single cognitive function; when the MS/DB-dependent attention/memory integration network is compromised, an alternate network is available to maintain normal levels of performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".