Differences in Working Memory Capacity Affect Online Spoken Word Recognition: Evidence From Eye Movements
Bibliographic record
Abstract
Individual differences in working memory capacity have been gaining recognition as playing an important role in speech comprehension, especially in noisy environments. Using the visual world eye-tracking paradigm, a recent study by Hadar and coworkers found that online spoken word recognition was slowed when listeners were required to retain in memory a list of four spoken digits (high load) compared with only one (low load). In the current study, we recognized that the influence of a digit preload might be greater for individuals who have a more limited memory span. We compared participants with higher and lower memory spans on the time course for spoken word recognition by testing eye-fixations on a named object, relative to fixations on an object whose name shared phonology with the named object. Results show that when a low load was imposed, differences in memory span had no effect on the time course of preferential fixations. However, with a high load, listeners with lower span were delayed by ∼550 ms in discriminating target from sound-sharing competitors, relative to higher span listeners. This follows an assumption that the interference effect of a memory preload is not a fixed value, but rather, its effect is greater for individuals with a smaller memory span. Interestingly, span differences affected the timeline for spoken word recognition in noise, but not offline accuracy. This highlights the significance of using eye-tracking as a measure for online speech processing. Results further emphasize the importance of considering differences in cognitive capacity, even when testing normal hearing young adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".