Cognitive Control in Young and Older Adults: Does Mood Matter?
Bibliographic record
Abstract
The dual mechanisms of control framework (DMC) proposes two modes of cognitive control: proactive and reactive control. In anticipation of an interference event, young adults primarily use a more proactive control mode, whereas older adults tend to use a more reactive one during the event, due to age-related deficits in working memory. The current study aimed to examine the effects of mood induction on cognitive control mode in older (ages 65+) compared to young adults (ages 18–30) with a standard letter-cue (Experiment 1) and a modified face-cue AX-CPT (Experiment 2). Mood induction into negative and/or positive mood versus neutral mood was conducted prior to the cognitive control task. Experiment 1 replicated the typical pattern of proactive control use in young adults and reactive control use in older adults. In Experiment 2, older adults showed comparable proactive control to young adults in their response time (RT). Mood induction showed little effect on cognitive control across the two experiments. These results did not reveal consistent effects of mood (negative or positive) on cognitive control mode in young and older adults, but discovered (or demonstrated) that older adults can engage proactive control when dichotomous face cues (female or male) are used in AX-CPT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".