The Jena Voice Learning and Memory Test (JVLMT)
Bibliographic record
Abstract
The JVLMT is a 22 min- standardized voice memory test with a learning phase, a repetition phase and a testing phase. Participants learn 8 voice identities which they later have to recognize among 2 foils (3-alternative forced-choice task). The testing phase includes 22 items with varying difficulty (Rasch-conform). All stimuli contain pseudo speech utterances with similar-to-English phonetics which enable the applicability of the test independent of participants’ native language. Results of each participant will be displayed at the end of the test as well as norms. Single-trial results are also included. The JVLMT is available as programmed in PsychoPy (cf. PDF document "JVLMT User Instructions"). Who is eligible to use the JVLMT? The JVLMT has been developed and validated for research purposes between 2018 – 2020 by researchers of the Friedrich-Schiller University Jena, Germany and the RWTH University of Aachen. The test is free to use in a non-profit manner, without the need to register. For details of the development and validation, please refer to the paper by Humble et al. (2023), which must be cited whenever the JVLMT is used: Humble, D., Schweinberger, S. R., Mayer, A., Jesgarzewsky, T. L., Dobel, C., & Zäske, R. (2023). The Jena Voice Learning and Memory Test (JVLMT): A standardized tool for assessing the ability to learn and recognize voices. Behavior Research Methods, 55(3), 1352-1371. doi:10.3758/s13428-022-01818-3 Acknowledgements: We thank Professor David R. Feinberg (McMaster University, Hamilton, Ontario, Canada) for assistance with the implementation of the online version of the JVLMT.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".