Slower Postencoding Stimulus Reaction Time Predicts Poorer Subsequent Source Memory and Increased Midline Cortical Activity
Bibliographic record
Abstract
Individuals vary widely in their ability to encode and retrieve past personal experiences in rich contextual detail (episodic memory). However, it remains unclear how within-subject variations in attention, measured on a trial-by-trial basis at encoding, and between-subject variation in attention and executive function abilities affect encoding-related brain activity and subsequent episodic retrieval. In the present study, 38 healthy young adults (mean age = 26.5 ± 4.4, 21 females) completed a task fMRI study in which they were instructed to encode colored photographs of everyday objects and their left/right spatial location. In addition, participants were asked to respond as quickly as possible to a central fixation cross that expanded in size at a variable duration after each encoding trial. RTs to the fixation cross preceding and following the object were hypothesized to reflect attentional variations pre- and postencoding stimulus, respectively. A mixed-effects logistic regression was performed to predict source memory success from pre- and poststimulus RT. Slower poststimulus RT, but not prestimulus RT, predicted poorer subsequent source memory within-subject. In addition, between-subject variation in task-switching ability, self-reported cognitive failures, and self-reported attentional abilities affected the association between poststimulus RT and subsequent memory. In addition, trial-by-trial task fMRI analysis indicated that increased encoding activity within default mode network regions was associated with slower poststimulus RT and with subsequent source retrieval failures. These results shed light onto the cognitive and neural factors that contribute to within-subject and between-subject variations in source memory ability.
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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) | 0.003 | 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".