Effects of Age on Measures of Complex Working Memory Span in the Beagle Dog (<i>Canis familiaris</i>) Using Two Versions of a Spatial List Learning Paradigm
Bibliographic record
Abstract
The present study used two versions of a spatial list learning (SLL) paradigm to examine the effects of increased cognitive load on visuospatial working memory processes in young and old beagle dogs. In the first experiment, young, and a select group of old dogs were first presented with one item, then two, and then three, and were rewarded for responding to the novel position. The dogs were able to learn the task at short delays, but compared with young dogs, old dogs performed worse at delays of 10 sec, and could not reach longer delays. Analysis of errors indicated that memory was best for end items in the spatial list and that within sessions, the number of errors in later trials was greater than the number of errors in earlier trials. A second version of the task, a modified SLL (mSLL) was developed to control for the use of non-mnemonic strategies on the SLL task. In this version, the first two items were presented individually. Acquisition and maximal memory performance were better in the young relative to the old dogs. Similar to the original SLL design, memory for early list items was worse than memory for later list items in both young and old dogs. The within-session pattern of errors however, did not change from trial to trial on the mSLL. The present results suggest that multiple working memory processes are engaged during complex tests of visuospatial function and the neuroanatomical substrates controlling these processes are affected differentially by age in the beagle dog.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".