EFFECTS AND RELATIONSHIPS OF RECEIVING INFORMATION AMOUNT ON EYE-MOVEMENT FEATURES
Bibliographic record
Abstract
The vast development of technology in this era encourages researchers \nto study about the interrelationship of the amount of information with human \ncognitive functions. This study was aimed to test the hypothesis of whether the \namount of information can affect human cognitive function analyzed from the \nresponses of human eye-movement features, as well as the relationships between \ninformation amount and eye-movement features. Six students from a Yuan Ze \nUniversity were involved in playing a game that stimulated a different amount of \ninformation. The participants’ eye-movements were recorded using a screenbased \n \neye-tracker (GP3 HD GazepointTM Canada) while playing ZType game. \nThere were nineteen generated traditional features from the experiment. These \ntraditional features were then being processed as complexity features. The \nanalysis of variance (ANOVA) was done to know which features that were affected \nby the amount of information. The results showed that there were four traditional \nfeatures comprising left and right pupil diameter, amount of blink, and saccade \nmagnitude that were significantly affected by the amount of information. Moreover, \nthe amount of information also affected the thirteen complexity features from \nfixation (duration and coordinates), pupil (diameter and coordinates), and saccade \n(magnitude and direction) elements. The linear regression analysis was done to \nknow which features are the critical features, which later can be used to build the \nAI model. The results showed that there were three traditional features comprising \nleft and right pupil diameter, and amount of blink that have negative and positive \ncorrelation respectively, with the information amount. This study indicates that the \namount of information is influencing the eyes’ response that is related to the human \ncognitive function. Moreover, the complexity analysis can help researchers to \ngenerate more eye-movement features from the traditional features.
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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 | 0.013 |
| 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 | 0.001 |
| 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".