N400 effects of semantic richness can be modulated by task demands.
Bibliographic record
Abstract
Semantic richness is a multidimensional construct that can be defined as the amount of semantic information associated with a concept. OBJECTIVE: To investigate neurophysiological correlates of semantic richness information associated with words and its interaction with task demands. METHOD: Two different dimensions of semantic richness (number of associates and number of semantic neighbors) were investigated using event-related potentials (ERPs) in lexical decision (LDT) and semantic categorization tasks (SCT) using the same stimuli in 2 groups of participants (24 in each group). RESULTS: The amplitude of the N400 ERP component, which is associated with semantic processing, was smaller for words with a high number of associates (p = .003 at fronto-centro-parietal sites) or semantic neighbors (p < .03 at centro-parietal sites) than for words with a low number of associates or number of semantic neighbors, in the LDT but not the SCT. CONCLUSIONS: These results suggest that the effects of semantic richness vary with task demands and may be used in a top-down manner to accommodate the current context. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".