An fMRI study identifying brain regions activated when performing well-learned versus newly learned visuomotor associations
Bibliographic record
Abstract
The current fMRI study identified brain regions implicated in performing well-learned versus new visuomotor associations. Stimuli were 2 sets of 6 abstract images, each paired arbitrarily with a unique hand gesture. Participants rehearsed one set of pairings over 4 days and learned the other set immediately prior to scanning. Data were obtained for 14 participants, who demonstrated an average 76ms motor reaction time advantage when performing the well-learned associations immediately prior to fMRI scanning. Regions-of-interest for the left lateral-occipital (LO), the left anterior intra-parietal (AIP) and left medial intra-parietal (MIP) areas were obtained by an independent functional localizer. Parameter estimates extracted from these regions demonstrate a greater BOLD response in left LO for new compared to well-learned associations (t(13) = 3.322, p = .006), but not left AIP or left MIP. Results suggest the left-hemisphere ventral stream is strongly activated before the automatization of visuomotor associations.
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 | 0.000 | 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 | 0.000 |
| 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".