Repeated performance in problem-solving tasks attenuates human cortical responses
Bibliographic record
Abstract
Abstract A ubiquitous characteristic of human cortical networks is their tendency to rapidly change their response properties upon repetition. While this phenomenon has been amply documented using simple sensory-motor tasks, it is still unclear to what extent brain activations change on a short time scale when we are engaged in high level, complex tasks. Here, we examined this question using three types of high-level visual problems. We analyzed data from intracranial recordings performed in eight patients, focusing on the location and type of changes and on their relationship to overt behavior. Our results show significant repetition effects, manifested as signal decrease with repetition, in three different groups of electrodes: Visual sites, which increased their activity during stimuli presentation; Processing Positive sites, which demonstrated increased activity throughout the experimental trial; and Processing Negative sites, which demonstrated suppression of activity during the trial as compared to baseline. Interestingly, despite these significant repetition effects, response time remained unchanged with repetition. These findings bear directly upon our ability to interpret results aggregated across multiple repetitions of the same complex task.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".