Bibliographic record
Abstract
Research Article| September 01 2023 Albert S. Bregman (1936–2023) Stephen McAdams Stephen McAdams McGill University, Montreal, Canada This piece was written by Stephen McAdams, with thoughts taken from Susan Pinker, Howard Steiger, Don Donderi, and Norman White. Search for other works by this author on: This Site PubMed Google Scholar Music Perception (2023) 41 (1): 74–75. https://doi.org/10.1525/mp.2023.41.1.74 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Stephen McAdams; Albert S. Bregman (1936–2023). Music Perception 1 September 2023; 41 (1): 74–75. doi: https://doi.org/10.1525/mp.2023.41.1.74 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentMusic Perception Search Al Bregman worked in Experimental Psychology for nearly 50 years, primarily studying auditory perception, but with occasional forays into visual perception as well. He directed his entire career toward understanding the way human listeners succeed in perceptually organizing the complex acoustic field into distinct sound sources and into the events and event streams they produce. Within this study area, Al made the most significant theoretical contributions of the many scientists working in the sub-discipline of auditory psychology. His conception of perceptual organization drew its main inspiration from the Gestalt psychologists, but he brought many concepts from computer science and artificial intelligence to bear on his theorizing, leading to the development of such concepts as: 1) primitive auditory scene analysis being an heuristic process to which a number of possible acoustic cues and sensory mechanisms contribute, 2) schema-based auditory organization being a selective process that draws information from the complex acoustic... You do not currently have access to this content.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.192 |
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".