Effects of Prior Exposure on Music Liking and Recognition in Patients with Temporal Lobe Lesions
Bibliographic record
Abstract
Prior exposure to music typically increases liking. This manifestation of implicit memory can be dissociated from explicit memory recognition. To examine the contribution of the medial temporal lobe to musical preference and recognition, we tested patients with either left (LTL) or right (RTL) temporal lobe lesions as well as normal control (NC) participants using the procedure of Peretz et al. The results in the affect task showed that NC and LTL participants preferred the studied over nonstudied melodies, thereby demonstrating an implicit exposure effect on liking judgments, whereas RTL patients failed to exhibit this effect. Explicit recognition was impaired in both LTL and RTL patients as compared to NC participants. On the basis of these findings, we suggest that RTL structures play a critical role in the formation of melody representations that support both priming and memory recognition, whereas LTL structures are more involved in the explicit retrieval of melodies. Furthermore, we were able to test an amnesic patient (PC) with bilateral lesions of the temporal lobe. In this case, the exposure effect on liking was also absent. However, repeated exposure to melodies was found to enhance both liking and recognition judgments. This remarkable sparing of memory observed through melody repetition suggests that extensive exposure may assist both implicit and explicit memory in the presence of global amnesia.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".