fMRI investigation of speed–accuracy strategy switching
Bibliographic record
Abstract
Switching between rapid and accurate responses is an important aspect of decision-making. However, the brain mechanisms important to smoothly change the speed-accuracy strategy remain mostly unclear. This issue was addressed here by using functional magnetic resonance imaging (fMRI). On each trial, right-handed healthy participants had to stress speed or accuracy in performing a color discrimination task on a target stimulus according to the instructions given by an initial cue. Participants were capable of trading speed for accuracy and vice versa. Analyses of cue-related fMRI activations revealed a significant recruitment of left middle frontal gyrus and right cerebellum when switching from speed to accuracy. The left superior parietal lobule was activated in the same switching condition but only after the target onset. The anterior cingulate cortex was more recruited, also after target presentation, when speed had to be maintained from one trial to the next. These results are interpreted within a theoretical framework that attributes a role in criterion-setting to the left lateral prefrontal cortex, perceptual evidence accumulation to the superior parietal lobule, and action energization to the anterior cingulate cortex, extending previous findings to the domain of speed-accuracy tradeoff regulations.
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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 | 0.000 |
| 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".