Bibliographic record
Abstract
Emotions have important and powerful effects on cognitive processes. Although it is well established that memory influences liking, we sought to document whether liking influences memory. A series of 6 experiments examined whether liking is related to recognition memory for novel music excerpts. In the general method, participants listened to a set of music excerpts and rated how much they liked each one. After a delay, they heard the same excerpts plus an equal number of novel excerpts and made recognition judgments, which were then examined in conjunction with liking ratings. Higher liking ratings were associated with improved recognition performance after a 10-min (Experiment 1) or 24-hr (Experiment 2) delay between the exposure and test phases. The findings were similar when participants made liking ratings after recognition judgments (Experiments 3 and 6), when possible confounding effects of similarity and familiarity were held constant (Experiment 4), and when a deeper level of processing was encouraged for all the excerpts (Experiment 5). Recognition did not vary as a function of liking for previously unheard excerpts (Experiment 6). The results implicate a direct association between liking and recognition. Considered jointly with previous findings, it is now clear that listeners tend to like music that they remember and to remember music that they like.
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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".