Reading unsegmented text: The impact on fixation location and duration
Bibliographic record
Abstract
Decisions about when and where to move the eyes are commonly assumed to be made independently (e.g., Findlay & Walker, 1999). Consequently, the influence of an experimental manipulation on fixation times in reading is typically analyzed without considering the possible impact of this manipulation on fixation locations. However, during reading, it is well-known that fixation location within the word influences fixation durations. Specifically, first-fixation duration is longer for fixations located near the center of the word than for fixations near the beginning or the end of the word. Thus, it is necessary to establish the extent to which location effects can interact with lexical variables such as word frequency, to rule out the possibility that lexical effects are at least in part due to subtle differences in fixation location. In a previous study (Reingold, Reichle, Glaholt, & Sheridan, accepted), we demonstrated that location effects on fixation duration were largely independent of the impact of word frequency (high vs. low) and parafoveal preview validity (valid vs. invalid preview). Extending this research, the present study directly manipulated a variable that was expected to influence fixation location. Specifically, we contrasted a normal text condition with a modified text condition that contained random numbers between 2 and 9 instead of spaces (e.g., John3decided8to5sell9the7table). Relative to the normal text condition, the modified text condition produced landing positions that were closer to the beginning of the word, as well as shorter saccades, longer fixations, and slower overall reading times. However, the normal and modified text conditions showed a similar pattern of word frequency and preview validity effects. The present results indicate that location effects during reading are largely independent of the influence of lexical variables, and more generally, our findings support the assumption that when and where decisions constitute independent factors in eye-movement control in reading. Meeting abstract presented at VSS 2012
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".