Network Dynamics Underlying Speed-Accuracy Trade-Offs in Response to Errors
Bibliographic record
Abstract
The ability to dynamically and rapidly adjust task performance based on its outcome is fundamental to adaptive, flexible behavior. Over trials of a task, responses speed up until an error is committed and after the error responses slow down. These dynamic adjustments serve to optimize performance and are well-described by the speed-accuracy trade-off (SATO) function. We hypothesized that SATOs based on outcomes reflect reciprocal changes in the allocation of attention between the internal milieu and the task-at-hand, as indexed by reciprocal changes in activity between the default and dorsal attention brain networks. We tested this hypothesis using functional MRI to examine the pattern of network activation over a series of trials surrounding and including an error. We further hypothesized that these reciprocal changes in network activity are coordinated by the posterior cingulate cortex (PCC) and would rely on the structural integrity of its white matter connections. Using diffusion tensor imaging, we examined whether fractional anisotropy of the posterior cingulum bundle correlated with the magnitude of reciprocal changes in network activation around errors. As expected, reaction time (RT) in trials surrounding errors was consistent with predictions from the SATO function. Activation in the default network was: (i) inversely correlated with RT, (ii) greater on trials before than after an error and (iii) maximal at the error. In contrast, activation in the right intraparietal sulcus of the dorsal attention network was (i) positively correlated with RT and showed the opposite pattern: (ii) less activation before than after an error and (iii) the least activation on the error. Greater integrity of the posterior cingulum bundle was associated with greater reciprocity in network activation around errors. These findings suggest that dynamic changes in attention to the internal versus external milieu in response to errors underlie SATOs in RT and are mediated by the PCC.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| 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".