Lexical access in mild cognitive impairment
Bibliographic record
Abstract
We examined the use of sentence context in lexical processing in aging and mild cognitive impairment (MCI). Younger and older adults and participants with MCI completed a lexical decision task in which target words were primed by sentences biasing a related or unrelated word (e.g., prime: “The baby put the spoon in his ______”, biased word: “mouth”, related target: “KISS”, unrelated target: “LEASH”). Biased items were of high or low frequency. All participants responded more quickly when the biased word was of high than low frequency, regardless of whether the target and biased word were related. Frequency effects were stronger in related than unrelated stimuli, and MCI participants – but not controls – responded more slowly when the target was related to a low-frequency word than when it was unrelated. We hypothesize that this effect results from slowed lexical activation in MCI: low frequency expected words are not completely activated when the target word is presented, leading to increased competition between the expected and target items, and resultant slowing in lexical decision on the target. These results indicate that MCI participants can use contextual information to make predictions about upcoming lexical items, and that information about lexical associations remains available in MCI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".