Bibliographic record
Abstract
Many students listen to music while studying, but are these conditions optimal for cognitive performance? Previous research has revealed inconsistent findings because the effect depends on several factors (e.g., type of music, type of task, individual differences). The current study examined the effect of background music on reading comprehension, a task that is most similar to studying, to explore three questions: 1) Is music with lyrics more distracting than instrumental music? 2) Does the emotional valence of the music (e.g., happy, sad) contribute to distraction? and 3) Do individual differences in personality, working memory, and empathy influence distractibility? Although several studies have examined the effect of lyrics and extraversion, few have explored our other factors of interest. We first conducted a pilot study (N = 60) to select popular songs that were unambiguously happy or sad, and to select three reading comprehension tasks that were equally difficult. In the main experiment, each participant completed reading comprehension tasks in three conditions, 1) while listening to original pop songs with lyrics, 2) while listening to instrumental versions of different pop songs, and 3) while sitting in silence. Half of the participants listened to happy-sounding music in the music conditions, and half listened to sad-sounding music. Participants then completed self-report measures of personality, working memory, and empathy. We expect that individuals who are least distracted by background music will be those who 1) are high in extraversion, 2) are low in empathy, and 3) have high working memory capacities. Discipline: Psychology Honours Faculty Mentor: Dr. Kathleen Corrigall
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".