Effects of Temporal Light Modulation on Cognitive Performance,Eye Movements, and Brain Function
Bibliographic record
Abstract
Temporal light modulation (TLM) describes variations in light output from a light source or lighting system and can be a characteristic of their normal operation. TLM at 100 or 120 Hz can disrupt eye movements and reduce visual performance compared to 40 kHz, but little is known of the effects between those frequencies. Such evidence could provide the basis for lighting product and application standards to support the development of energy-efficient lighting systems. This repeated-measures experiment compared the effects of three stimuli (no TLM [DC], 100 Hz, and 500 Hz [both rectangular-wave, 100% modulation depth, 50% duty cycle]) on state anxiety, eye movements (blinks, saccades, fixations, and pupil size), cognitive performance (Stroop task and sentence reading speed), and visual event-related potentials (ERPs). Post-processing of EEG data included dipole source analysis and estimates of source dipole strength. TLM at 100 Hz was associated with larger pupil sizes and greater ERP dipole strength during 100 Hz exposure than no TLM, particularly in the right hemisphere. Cognitive interference was lower for 500 Hz TLM than 100 Hz. State anxiety was unaffected. The addition of TLM to the light source signal increased arousal and altered brain activity; at the highest frequency tested here this reduced cognitive interference. Arousal theory and stochastic facilitation can provide possible explanations.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".