Non-monotonic plasticity from real-time inception of competition between object representations
Bibliographic record
Abstract
When neural representations compete, this can trigger learning processes that act to resolve this competition. The non-monotonic plasticity hypothesis (NMPH) predicts that such competition will alter the relationship between representations according to a U-shaped curve: they integrate when strongly co-activated, differentiate when moderately co-activated, and remain unaffected when weakly co-activated. The NMPH is often tested by quantifying patterns of neural co-activation after the fact. Here, we sought to manipulate and control this co-activation during online perception with real-time fMRI. We used neurofeedback to incept competing object representations at different degrees of co-activation. While viewing a target object (e.g., a bed), we trained participants to activate the neural representation of a competitor object from the same category (e.g., a chair). We performed multivariate pattern analysis in real-time to quantify neural evidence for the competitor relative to other untrained objects from the same category, and this evidence determined the feedback provided. The entire protocol involved five scanning sessions. Sessions 2-4 consisted of multiple runs of neurofeedback, each bookended by a pre- and post-session run without feedback, to estimate the evolving neural representations of the target and competitor objects in each session. Session 1 and 5 allowed us to estimate neural representations before and after the full training protocol. With this multiple session whole-brain approach, we probed for changes in the representational similarity between targets and competitors as a function of the degree of competitor activation that participants achieved with neurofeedback, both within and across sessions. Preliminary results show evidence of non-monotonic changes in multivariate pattern similarity induced by neurofeedback in some areas of the hippocampus, frontal cortex, and visual cortex. These results support the predictions of the NMPH, and also demonstrate the potential of real-time fMRI to induce competition between visual representations and alter their overlap in the brain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".