Auditory temporal contextual cueing
Bibliographic record
Abstract
When conducting a visual search task participants respond faster to targets embedded in a repeated array of visual distractors compared to targets embedded in a novel array, an effect referred to as contextual cueing. There are no reports of contextual cueing in audition, and generalizing this effect to the auditory domain would provide a new paradigm to investigate similarities, differences, and interactions in visual and auditory processing. In 4 experiments, participants identified a numerical target embedded in a sequence of alphabetic letter distractors. The training phase (Epochs 1, 2, and 3) of all experiments contained repeated sequences, and the testing phase (Epoch 4) contained novel sequences. Temporal contextual cueing was measured as slower response times in Epoch 4 than in Epoch 3. Repeated context was defined by the order of distractor identities and the rhythmic structure of the portion of the sequence immediately preceding the target digit, either together (Experiments 1 and 2) or separately (Experiments 3 and 4). An auditory temporal contextual cueing effect was obtained in Experiments 1, 2, and 4. This is the first report of an auditory temporal contextual cueing effect and, thus, it extends the contextual cueing effect to a new modality. This new experimental paradigm could be useful in furthering our understanding of fundamental auditory processes and could eventually be used to aid in diagnosing language deficits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".