What do Scanpaths Tell Us About Cognitive Processes? An Investigation in a Problem Solving Domain
Bibliographic record
Abstract
Scanpaths are the specific sequence of fixations elicited by someone when viewing a scene or object.Not only do they illustrate which areas of the scene that were fixated on, they also capture the viewer's change in attention overtime.Dynamics of visual attention contain movement patterns that are not otherwise measured with simple fixation methods of eye data analysis.In this thesis, we employ MultiMatch, a scanpath analysis method that provides quantitative measure of similarity between two scanpaths, to examine visual patterns for two different data sets.These data sets came from studies that presented students with math problems and varied instructional material to manipulate student's solving strategies.We apply an analysis method corresponding to grouping scanpaths across and within-conditions to determine whether the MultiMatch analysis method can distinguish between the instructional material presentation formats.We further our analysis by providing initial interpretation guidelines through a brief scanpath simulation model.Results demonstrate a difference in viewing pattern use between conditions in the original studies, which were designed to elicit different solving strategies.My time at the department of Cognitive Science at Carleton University has at once passed in a blur and also felt it could last a lifetime.Given the people; the professors and fellow students alike, I would consider myself lucky for any amount of additional time spent with the department.The duration of my degree has been a time of learning, involvement and growth.Thank you.To all my family and friends, thank you for your continued help and showing up with words (and food) of encouragement.I could not have done it without your making time and space for my vague questions.Thanks especially to my brother, Colin, for coding and moral support and to my partner, Jay, for Latex and additional moral support.To Dr. Kasia Muldner, who has been the most patient of all -thank you for helping make my analysis dreams a reality, for providing endless support, motivation and opportunities along the way.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".