Exploring the dynamic interplay between Learning and Working Memory within various cognitive contexts
Bibliographic record
Abstract
The intertwined relationship between reinforcement learning and working memory in the brain is a complex subject and has been researched widely across various domains in neuroscience. A main part of the research is focused on identifying brain areas responsible for these two functions, understanding their contributions in accomplishing the related tasks and examining how they adapt when faced with changed conditions such as a cognitive impairment or ageing. Numerous models have been introduced to formulate either these two subsystems of reinforcement learning and working memory separately or their combination and relationship in executing cognitive tasks. In this paper RLWM model is selected as the computational framework that models the behavioral parameters of subjects with different cognitive abilities due to age or cognitive status. In this sense, related RLWM task is used to test a group of subjects in different ages and different cognitive abilities based on their scores in the Montreal Cognitive Assessment tool (MoCA). The results show that the overall performance accuracy and speed decline as the age group (young vs. middle-aged) differs. They also differ significantly on some model parameters such as learning rate, WM decay and decision noise. Additionally, among middle-aged group, subjects that were categorized as normal vs. MCI based on MoCA test, differs in speed and performance accuracy as well as decision noise as one of the RLWM model’s parameter that was higher in MCI middle-aged subjects.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".