Working Memory Load Affects Processing Time in Spoken Word Recognition: Evidence from Eye-Movements
Bibliographic record
Abstract
In daily life, speech perception is usually accompanied by other tasks that tap into working memory capacity. However, the role of working memory on speech processing is not clear. The goal of this study was to examine how working memory load affects the timeline for spoken word recognition in ideal listening conditions. We used the "visual world" eye-tracking paradigm. The task consisted of spoken instructions referring to one of four objects depicted on a computer monitor (e.g., "point at the candle"). Half of the trials presented a phonological competitor to the target word that either overlapped in the initial syllable (onset) or at the last syllable (offset). Eye movements captured listeners' ability to differentiate the target noun from its depicted phonological competitor (e.g., candy or sandal). We manipulated working memory load by using a digit pre-load task, where participants had to retain either one (low-load) or four (high-load) spoken digits for the duration of a spoken word recognition trial. The data show that the high-load condition delayed real-time target discrimination. Specifically, a four-digit load was sufficient to delay the point of discrimination between the spoken target word and its phonological competitor. Our results emphasize the important role working memory plays in speech perception, even when performed by young adults in ideal listening conditions.
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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 | 0.004 |
| 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) | 0.002 | 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".