Look into my eyes: What can eye-based measures tell us about the relationship between physical activity and cognitive performance?
Bibliographic record
Abstract
BACKGROUND: There is a growing interest to understand the neurobiological mechanisms that drive the positive associations of physical activity and fitness with measures of cognitive performance. To better understand those mechanisms, several studies have employed eye-based measures (e.g., eye movement measures such as saccades, pupillary measures such as pupil dilation, and vascular measures such as retinal vessel diameter) deemed to be proxies for specific neurobiological mechanisms. However, there is currently no systematic review providing a comprehensive overview of these studies in the field of exercise-cognition science. Thus, this review aimed to address that gap in the literature. METHODS: To identify eligible studies, we searched 5 electronic databases on October 23, 2022. Two researchers independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX scale, for interventional studies) and the critical appraisal tool from the Joanna Briggs Institute (for cross-sectional studies). RESULTS: Our systematic review (n = 35 studies) offers the following main findings: (a) there is insufficient evidence available to draw solid conclusions concerning gaze-fixation-based measures; (b) the evidence that pupillometric measures, which are a proxy for the noradrenergic system, can explain the positive effect of acute exercise and cardiorespiratory fitness on cognitive performance is mixed; (c) physical training- or fitness-related changes of the cerebrovascular system (operationalized via changes in retinal vasculature) are, in general, positively associated with cognitive performance improvements; (d) acute and chronic physical exercises show a positive effect based on an oculomotor-based measure of executive function (operationalized via antisaccade tasks); and (e) the positive association between cardiorespiratory fitness and cognitive performance is partly mediated by the dopaminergic system (operationalized via spontaneous eye-blink rate). CONCLUSION: This systematic review offers confirmation that eye-based measures can provide valuable insight into the neurobiological mechanisms that may drive positive associations between physical activity and fitness and measures of cognitive performance. However, due to the limited number of studies utilizing specific methods for obtaining eye-based measures (e.g., pupillometry, retinal vessel analysis, spontaneous eye blink rate) or investigating a possible dose-response relationship, further research is necessary before more nuanced conclusions can be drawn. Given that eye-based measures are economical and non-invasive, we hope this review will foster the future application of eye-based measures in the field of exercise-cognition science.
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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 | 0.041 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".