Do eye movements reflect readers’ thoughts during reading? Evidence from multidimensional experience sampling and eye movements
Bibliographic record
Abstract
While reading narrative texts, readers' attention often fluctuates from the text (e.g., immersion) to text-unrelated thoughts (e.g., mind-wandering). Research on mind-wandering and immersion suggests that they influence the reading process differently. In this article, we examine the types of thoughts readers have while reading a literary text. Specifically, we investigated the effect of immersion and mind-wandering on eye-movement behaviour during reading. Fifty-six participants read extracts from a novel while their eye-movements were monitored. Participants' thoughts were probed using multidimensional experience sampling. We identified four types of thought: Immersion, Mind-wandering, Sub-Vocalization, and Social Episodic Thoughts. We then ran General Additive Mixed Models (GAMMs) to examine the relationship between these thought types and eye movements. Results show that eye movements are influenced by the types of thoughts readers experience while reading literary texts. These results have important implications for the way that mind-wandering is typically investigated, particularly in reading research.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".