Making things difficult in lexical decision: The impact of pseudohomophones and transposed-letter nonwords on frequency and semantic priming effects.
Bibliographic record
Abstract
Performance in a lexical decision task is crucially dependent on the difficulty of the word-nonword discrimination. More wordlike nonwords cause not only a latency increase for words but also, as reported by Stone and Van Orden (1993), larger word frequency effects. Several current models of lexical decision making can explain these types of results in terms of a single mechanism, a mechanism driven by the nature of the interactions within the lexicon. In 2 experiments, we replicated Stone and Van Orden's increased frequency effect using both pseudohomophones (e.g., BEEST) and transposed-letter nonwords (e.g., JUGDE) as the more wordlike nonwords. In a 3rd experiment, we demonstrated that simply increasing word latencies without changing the difficulty of the word-nonword discrimination does not produce larger frequency effects. These results are reasonably consistent with many current models. In contrast, neither pseudohomophones nor transposed-letter nonwords altered the size of semantic priming effects across 4 additional experiments, posing a challenge to models that would attempt to explain both nonword difficulty effects and semantic priming effects in lexical decision tasks in terms of a single, lexically driven mechanism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".