Effect of Memory on Attention: Incidentally Formed Auditory Associations Using Lateralized Tone Detection Reveal a Predictive Advantage (A Behavioural Study)
Bibliographic record
Abstract
Memory influences what we attend to which affects what we remember. The effects of attention on memory are well established, but the corollary effects of memory guiding attention during retrieval are relatively unknown, particularly in the auditory domain. Recent work using auditory scene-target pairings showed that behavioural expression of learning at test required attending to the target when encoding. The present study extends this work with a novel paradigm in which lateralized targets are assigned to the corresponding hand (i.e., left target, left-hand motor response). We tasked young adults to respond to a lateralized pure-tone target embedded within a binaural audio-scene by using the corresponding hand (left hand for left tone). The results support our predictions of better memory performance when incidental auditory associations are formed for audio-scenes with predictive (fixed) targets compared with unpredictive (random) targets. We found that auditory associations formed with fixed scene-target pairings sped target detection during learning, and at the subsequent memory test more than random scene-target pairings. Unexpectedly, novel scene-target pairings promoted faster and more accurate old-new scene recognition performance compared with fixed and random scene-tone pairings. Our results are consistent with the proposal that auditory memory can guide attention, with a dynamic role for incidental auditory memory-guided attention at encoding and retrieval. We consider these results and the role of prior knowledge, repetition, and prediction in forming and retrieving implicit auditory associations using an instance-based theoretical approach.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".