Introduction to the research topic meaning in mind: semantic richness effects in language processing
Bibliographic record
Abstract
The ultimate goal of reading is to extract meaning from printed words.However, the mechanisms that mediate orthography and semantics are not well-understood, and have rarely been implemented in computational models.To address this puzzle, one of the strategies cognitive scientists have begun to use is to examine semantic richness effects.Semantic richness effects refer to the finding that words associated with relatively more semantic information are recognized faster and more accurately, due to their possessing richer, better-specified semantic representations.Importantly, semantic richness is not a unitary concept.Instead, it draws on various theoretical perspectives and can vary along multiple dimensions.Thus, by examining which dimensions of semantic richness influence visual word recognition behavior, we gain insight about which theoretical perspectives seem to be promising descriptions of the process by which meaning is extracted from print.Our goal for this Frontiers Research Topic was to highlight the latest findings regarding semantic richness and theoretical developments on the issue of semantic processing.Our hope was to provide a forum for state-of-the-art research in this field, and to foster new theoretical advances.The 17 contributions that comprise the Research Topic certainly represent the state of the art; methodologies include ERP, fMRI, TMS, and behavioral approaches, and involve both intact and patient populations.Together, these contributions give rise to a number of inferences about semantic richness effects and implications of those effects for our understanding of semantic processing effects in visual word recognition.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".