Cognitive Reserve and language processing demand in healthy older adults
Bibliographic record
Abstract
Sonia Montemurroa* , Gonia Jaremab & Sara Mondinicd a IRCCS San Camillo Hospital, Venice, Italyb Institut universitaire de gériatrie de Montréal, Université de Montréal and Research Centre, Montréal, Canadac Dipartimento di filosofia, sociologia, pedagogia e psicologia applicata (FISPPA), University of Padova, Padova, Italyd Human Inspired Technology Research-Centre, University of Padova, Padova, ItalyCONTACT Sonia Montemurro sn.montemurro@gmail.com IRCCS San Camillo Hospital, Via Alberoni, 70, 30126 Lido (Venice), ItalySupplemental data for this article can be accessed at https://doi.org/10.1080/23273798.2021.1896012.ABSTRACTCognitive Reserve (CR) refers to cognitive resources acquired through experiences along the lifespan that allow for flexibility in coping with neurocognitive changes. Investigating the role of CR measures across well-established psycholinguistic features can provide new insight into how CR interplays with cognition. Sixty-five Italian older adults performed a Lexical Decision, a Semantic Matching and a Sentence Reading task. We observed the effects of CR on reaction times and accuracy while varying lexical frequency (high vs low) and lexical semantics (concrete vs abstract) and on reading times of sentences with either syntactic or semantic violations. In the Lexical Decision and Semantic Matching tasks, CR played a role in processing low frequency and abstract words. In the Sentence Reading Task, CR influenced reading times, particularly in the presence of syntactic violations. CR predicts cognitive performance in tasks that require language demands at different levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".