The dynamic network subserving the three phases of cognitive procedural learning
Bibliographic record
Abstract
Cognitive procedural learning is characterized by three phases (cognitive, associative, and autonomous), each involving distinct processes. We performed a behavioral study and a positron emission tomography (PET) activation study using the Tower of Toronto task. The aim of the behavioral study was to determine cognitive predictors for the length of each of the three learning phases, in order to preselect subjects for the PET study. The objective of the second study was to describe the cerebral substrates subtending these three phases. Contrasted with a reference (motor) task, the cognitive phase activated the prefrontal cortex, cerebellum, and parietal regions, all of which became less active as learning progressed. The associative phase was characterized by the activation of the occipital regions, right thalamus, and caudate nucleus. During the autonomous phase, new regions were involved, including the left thalamus and an anterior part of the cerebellum. These results, by employing a direct comparison between phases, provide the first evidence of the involvement and the time course of activation of different regions in each learning phase, in accordance with current models of cognitive procedural learning. The involvement of a frontoparietal network suggests the use of strategies in problem solving during the cognitive phase. The involvement of the occipital regions during the associative and autonomous phase suggests the intervention of mental imagery. Lastly, the activation of the cerebellum during the autonomous phase is consistent with the fact that performance in this phase is determined by psychomotor abilities.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".