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Record W2074213764 · doi:10.1167/12.9.413

Reading unsegmented text: The impact on fixation location and duration

2012· article· en· W2074213764 on OpenAlexaff
H. Sheridan, E. M. Reingold

Bibliographic record

VenueJournal of Vision · 2012
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFixation (population genetics)Word lists by frequencyComputer sciencePsychologySpeech recognitionMathematicsNatural language processingMedicinePopulation

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.109

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.307
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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