Sleep Soundly! Sleep Deprivation Impairs Perception of Spoken Sentences in Challenging Listening Conditions
Why this work is in the frame
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Bibliographic record
Abstract
Speech perception, a daily task crucial for social interaction, is often performed after sleep deprivation (SD). However, there is only scant research on the effects of SD on real-life speech tasks. Speech-processing models (FUEL, ELU) suggest that challenging listening conditions require a greater allocation of cognitive resources, while ideal listening conditions (speech in quiet) require minimal resources. Therefore, SD, which reduces cognitive reserve, may adversely affect speech perception under challenging, but not ideal, conditions. The goal of this study was to test this, manipulating the extent of available resources (with/without SD) and task difficulty in three conditions: sentences presented in (a) quiet, (b) background noise, and (c) with emotional prosody, where participants identified the emotions conveyed by the speaker. The performance of young adults ( n = 41) was assessed twice, after nocturnal sleep and after a 24-hr SD in three tasks: (a) sentence repetition in quiet, and (b) noise, and (c) emotion identification of spoken sentences. Results partially supported our hypotheses. The perception of spoken sentences was impaired by SD, but noise-level did not interact with SD effect. Results suggest that 24-hr SD reduces cognitive resources, which in turn impairs listeners’ ability (or motivation) to perform daily functions of speech perception. Theoretically, findings directly relate SD to speech perception, supporting current theoretical speech models. Clinically, we suggest that SD should be considered in daily clinical settings, e.g., hearing tests. Finally, professions that require shift work, such as health care, should consider the negative effects of SD on spoken communication.
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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 | 0.000 |
| 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) | 0.000 | 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 it