Talking is harder than listening: The time course of dual-task costs during naturalistic conversation.
Bibliographic record
Abstract
Many studies have shown that the cognitive demands of language use are a substantial cause of central dual-task costs, including costs on concurrent driving performance. More recently, several studies have considered whether language production or comprehension is inherently more difficult with respect to costs on concurrent performance, with mixed results. This assessment is particularly difficult given the open question of how one should best equate and compare production and comprehension demands and performance. The present study used 2 very different approaches to address this question. Experiment 1 assessed manual tracking performance concurrently with a conventional labouratory task, comparing dual-task costs with comprehension and verification versus production of category items. Experiment 2 adopted an extreme ecological and functional approach to this question by assessing dual-task manual tracking costs concurrent with continuous, naturalistic, 2-way conversation, allowing event-related analysis of continuous tracking relative to onsets and offsets of natural production and comprehension events. Over both experiments, tracking performance was worse with concurrent production versus comprehension demands. We suggest that by at least 1 important functional metric-performance in natural, everyday conversation-talking is indeed harder than listening. (PsycINFO Database Record
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".