Why are background telephone conversations distracting?
Bibliographic record
Abstract
Telephone conversation is ubiquitous within the office setting. Overhearing a telephone conversation-whereby only one of the two speakers is heard-is subjectively more annoying and objectively more distracting than overhearing a full conversation. The present study sought to determine whether this "halfalogue" effect is attributable to unexpected offsets and onsets within the background speech (acoustic unexpectedness) or to the tendency to predict the unheard part of the conversation (semantic [un]predictability), and whether these effects can be shielded against through top-down cognitive control. In Experiment 1, participants performed an office-related task in quiet or in the presence of halfalogue and dialogue background speech. Irrelevant speech was either meaningful or meaningless speech. The halfalogue effect was only present for the meaningful speech condition. Experiment 2 addressed whether higher task-engagement could shield against the halfalogue effect by manipulating the font of the to-be-read material. Although the halfalogue effect was found with an easy-to-read font (fluent text), the use of a difficult-to-read font (disfluent text) eliminated the effect. The halfalogue effect is thus attributable to the semantic (un)predictability, not the acoustic unexpectedness, of background telephone conversation and can be prevented by simple means such as increasing the level of engagement required by the focal task. (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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".