The Einstellung effect in anagram problem solving: evidence from eye movements
Bibliographic record
Abstract
The Einstellung effect is the counterintuitive finding that prior experience or domain-specific knowledge can under some circumstances interfere with problem solving performance. This effect has been demonstrated in several domains of expertise including medicine and chess. In the present study we explored this effect in the context of a simplified anagram problem solving task. Participants solved anagram problems while their eye movements were monitored. Each problem consisted of six letters: a central three-letter string whose letters were part of the solution word, and three additional individual letters. Participants were informed that one of the individual letters was a distractor letter and were asked to find a five-letter solution word. In order to examine the impact of stimulus familiarity on problem solving performance and eye movements, the central letter string was presented either as a familiar three-letter word, or the letters were rearranged to form a three-letter nonword. Replicating the classic Einstellung effect, overall performance was better for nonword than word trials. However, participants' eye movements revealed a more complex pattern of both interference and facilitation as a function of the familiarity of the central letter string. Specifically, word trials resulted in shorter viewing times on the central letter string and longer viewing times on the individual letters than nonword trials. These findings suggest that while participants were better able to encode and maintain the meaningful word stimuli in working memory, they found it more challenging to integrate the individual letters into the central letter string when it was presented as a word.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".