Bilateral Eye Movements, Attentional Flexibility and Metaphor Comprehension: The Substrate of REM Dreaming?
Bibliographic record
Abstract
Explanations for the effects of the rapid eye movements induced during Eye Movement Desensitization Reprocessing (EMDR; Shapiro, 2001) have drawn upon an analogy with the eye movements of REM sleep (Kuiken, Bears, Miall, and Smith, 2002). An extension of that analogy posits two orienting systems, one involving threat-fear related mnemonic contextualization and another involving loss-pain related monitoring of conflicting response alternatives. In a study involving individuals who had recently experienced significant loss or trauma, we found that experimentally induced saccadic eye movements decreased reaction times to unexpected stimuli among those reporting traumatic distress (characterized by hyperarousal and intrusive thoughts) and increased reaction times among those reporting separation distress (characterized by vivid reminiscences and the sense of a foreshortened future). Also, we found that saccadic eye movements increased the perceived strikingness of metaphoric sentence endings among those reporting amnesia for events related to either loss or trauma. The eye movements of both EMDR and REM sleep may differently affect the attentional and cognitive reorienting activity of those living with the consequences of loss or trauma. These differences may be evident in their waking reflections and in their dreams.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".