Parsing action and cognition: Using the lateralized readiness potential to quantify perceptual/cognitive slowing in older adults
Bibliographic record
Abstract
Detection of the onset of motion is essential to navigate the world. Driving a car or crossing the street becomes perilous as our reaction times to motion onset decline with age. It has been established that older adults are slower to react to motion onset in random dot patterns (Ball & Sekuler, 1986; Trick & Silverman, 1991), as well as sinusoidal gratings (Porciatti, Fiorentini, Morrone, & Burr, 1999). Porciatti et al. showed that not all of the increase in reaction times in older adults could be attributed to a decrement in motor activity, implying that at least some of the increase can be attributed to slower perceptual and/or cognitive processes. The question remains, however, as to just how much of the increase can be attributed to non-motoric processes. In our study, participants (younger and older adults) pressed a key with either their right or left hands at the onset of up or down movement in a coherent motion display. We replicated the finding that older adults are slower to respond to motion onset. More importantly, using the lateralized readiness potential (LRP), we quantified the extent to which these slower reaction times arose from slower motor response (response-locked LRP) and slower perceptual/cognitive processing (stimulus-locked LRP). In the future, this methodology could be used to establish the relative contributions of motoric and non-motoric factors to age-related decrements in performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".