Dissociable roles of neural pattern reactivation and transformation during recognition of words read aloud and silently: An MVPA study of the production effect
Bibliographic record
Abstract
Abstract Recent work surrounding the neural correlates of episodic memory retrieval has focussed on the decodability of neural activation patterns elicited by unique stimuli. Research in this area has revealed two distinct phenomena: (i) neural pattern reactivation, which describes the fidelity of activation patterns between encoding and retrieval; (ii) neural pattern transformation, which describes systematic changes to these patterns. This study used fMRI to investigate the roles of these two processes in the context of the production effect, which is a robust episodic memory advantage for words read aloud compared to words read silently. Twenty-five participants read words either aloud or silently, and later performed old-new recognition judgements on all previously seen words. We applied multivariate analysis to compare measures of reactivation and transformation between the two conditions. We found that, compared with silent words, successful recognition of aloud words was associated with reactivation in the left insula and transformation in the left precuneus. By contrast, recognising silent words (compared to aloud) was associated with relatively more extensive reactivation, predominantly in left ventral temporal and prefrontal areas. We suggest that recognition of aloud words might depend on retrieval and metacognitive evaluation of speech-related information that was elicited during the initial encoding experience, while recognition of silent words is more dependent on reinstatement of visual-orthographic information. Overall, our results demonstrate that different encoding conditions may give rise to dissociable neural mechanisms supporting single word recognition.
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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 | 0.003 |
| 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".