Subjective reports of mind wandering during encoding predict recognition memory for scenes
Bibliographic record
Abstract
Throughout our daily experiences, it is not uncommon for us to be multitasking or otherwise distracted regardless of the task at hand. The consequences of this are well established: divided attention impairs cognitive performance and, in particular, later visual memory. However, distractions can also come from within, when thoughts drift away from the current task onto unrelated concerns. The extent of the impact of such mind wandering episodes on later visual memory remains unclear. Here, we consider how spontaneous, moment-to-moment fluctuations in attention impact visual memory. We indexed attentional states using subjective reports of depth of mind wandering (MW) and variance in response times (RTV) during a sustained attention task. Participants (N=100) attempted to respond rhythmically to computer-generated scene images presented one at a time and were told to remember them for a later memory test. No judgment or decision was required; participants simply pressed a key rhythmically with each scene’s onset. Following this, we measured old-new recognition memory for the presented scenes and tested whether our metrics of MW predicted performance. Consistent with our predictions, linear mixed-effects modelling revealed that subjective reports of depth of MW significantly predicted subsequent scene memory; greater depth led to poorer memory. This finding indicated that participants’ subjective reports of attention quality during encoding predicted long-term visual memory. However, previously observed associations between RTV and both MW reports and memory were not present. Notifying participants about the upcoming memory test likely enhanced attention and uniformly reduced RT variance. Current follow-ups explore whether making the memory task implicit changes both RTV and the association with later memory, and deriving more continuous readout from long-term memory to test for effects on memory precision. Overall, the present study demonstrates that mind wandering, like divided attention, negatively impacts visual memory.
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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 | 0.006 |
| 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 | 0.000 |
| 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".