Neural Activity Is Modulated by Trial History: A Functional Magnetic Resonance Imaging Study of the Effects of a Previous Antisaccade
Bibliographic record
Abstract
Saccadic latencies are influenced by what occurred during the previous trial. When the previous trial is an antisaccade, the latencies of both prosaccades and antisaccades are prolonged. The aim of this study was to identify neural correlates of this intertrial effect of antisaccades. Specifically, based on both monkey electrophysiology and human neuroimaging findings, we expected trials preceded by antisaccades to be associated with reduced frontal eye field (FEF) activity relative to those preceded by prosaccades. Twenty-one healthy participants performed pseudorandom sequences of prosaccade and antisaccade trials during functional magnetic resonance imaging (fMRI) with concurrent monitoring of eye position. We compared activity in trials preceded by an antisaccade with activity in trials preceded by a prosaccade. The primary result was that a previous antisaccade prolonged saccadic latency and reduced fMRI activity in the FEF and other regions. No regions showed increased activity. We interpret the reduced FEF activity and slower saccadic responses to reflect inhibitory influences on the response system as a consequence of performing an antisaccade in the previous trial. This demonstrates that neural activity is modulated by trial history, consistent with a rapid, dynamic form of learning. More generally, these results highlight the importance of trial history as a source of variability in both behavioral and neuroimaging studies.
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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 | 0.002 |
| 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".