Short-term/working memory : the Second Quebec Conference on Short-Term/Working Memory
Bibliographic record
Abstract
1 MS 004. M. Duncan, S. Lewandowsky, The Time Course of a Response Suppression: No Evidence for a Gradual Release from Inhibition. 2 MS 015. G.J. Hitch, M.C. Fastame, B. Flude, How Is the Serial Order of a Verbal Sequence Coded? Some Comparisons Between Models. 3 MS 008. B. Murdock, Storage and Retrieval of Serial-order Information. 4 MS 026. A.M. Suprenant, M.R. Kelley, L.A. Farley, I. Neath, Fill-in and Infill Errors in Order Memory. 5 MS 012. K. Haberlandt, J.G. Thomas, H. Lawrence, T. Krohn, Transposition Asymmetry in Immediate Serial Recall. 6 MS 006. S. Lewandowsky, G.D.A. Brown, Serial Recall and Presentation Schedule: A Micro-analysis of Local Distinctiveness. 7 MS 001. N. Cowan, T.D. Johnson, J.S. Saults, Capacity Limits in List Item Recognition: Evidence from Proactive Interference. 8 MS 005. D.J.K. Mewhort, E. E. Johns, Sharpening the Echo: An Iterative-resonance Model for Short-term Recognition Memory. 9 MS 009. G. Ward, S.E. Avons, L. Melling, Serial Position Curves in Short-term Memory: Functional Equivalence Across Modalities. 10 MS 020. P. Gupta, Primacy and Recency in Nonword Repetition. 11 MS 022. J. Saint-Aubin, M. Poirer, Word Frequency Effects in Immediate Serial Recall: Item Familiarity and Item Co-occurrence Have the Same Effect. 12 MS 023. E.B. Lange, K. Oberauer, Overwriting of Phonemic Features in Serial Recall. 13 MS 028. E. Service, S. Maury, E. Luotoniemi, Forgetting and Redintegration of Consonants and Vowels in Pseudoword Lists. 14 MS 002. A.B. Fallon, E. Mak G. Tehan, Lexicality and Phonological Similarity: A Challenge for the Retrieval-based Account of Serial Recall? 15 MS 030. S. Tremblay, A.P. Nicholls, F.B.R. Parmentier, D.M. Jones, Visual Distraction and Visuo-spatial Memory: A Sandwich Effect. 16 MS 018. L. Hendry, An Item/order Tradeoff Explanation of Word Length and Generation Effects. 17 MS 014. G.A. Tolan, G. Tehan, Is Spoken Duration a Sufficient Explanation of the Word Length Effect? 18 MS 034. M. Poirier, R. Schweickert, Silent Reading Rate and Memory Span. 19 MS 003. J.A. Kole, A.F. Healy, C.J. Buck-Gengler, Does Number Data Entry Rely on the Phonological Loop? 20 MS 011. I. Neath, C. Fordin, Is the Interference Between Memory Processing and Timing Specific to the Use of Verbal Material. 21 MS 033. G. Waters, D. Caplan, The Relationship Between Age, Processing Speed, Working Memory Capacity, and Language Comprehension. 22 MS 017. D.M. Bayliss, C. Jarrold, A.D. Baddeley, D.M. Gunn. The Relationship Between Short-term Memory and Working Memory: Complex Span Made Simple? 23 MS 016. E.V. Masoura, S.E. Gathercole, Contrasting Contributions of Phonological Short-term Memory and Long-term Knowledge to Vocabulary Learning in a Foreign Language. 24 MS 032. R.H. Logie, S. Della Sala, N. Beschin, M. Denis, Dissociating Mental Transformations and Visuo-spatial Storage in Working Memory: Evidence from Representational Neglect. 25 MS 024. S. Della Sala, N. Cowan, N. Beschin, M. Perini, Lying There, Remembering: Improving Recall of Prose in Amnesic Patients with Mild Cognitive Impairment by Minimizing Interference. 26 MS 027. C. Lustig, M.S. Matell, W.H. Meck, Not Just a Coincidence: Frontal-striatal Interactions in Working Memory and Interval Timing.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.019 |
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".