How does meaning come to mind? Four broad principles of semantic processing.
Bibliographic record
Abstract
When we see or hear a word, we can rapidly bring its meaning to mind. The process that underlies this ability is quite complex. Over the past 2 decades, considerable progress has been made toward understanding this process. In this article, I offer four broad principles of semantic processing derived from lexical-semantic research. The first principle is that the relationship between form and meaning is not so arbitrary, and I explore that by describing efforts to understand the relationship between form and meaning, highlighting advances from my own lab on the topics of sound symbolism and iconicity. The second principle is that more is better, and I summarise previous research on semantic richness effects and how those effects reveal the nature of semantic representation. The third principle is the many and various properties of abstract concepts. I point to abstract meaning as a challenge for some theories of semantic representation. In response to that challenge, I outline what has been learned about how those meanings are acquired and represented. The fourth principle is that experience matters, and I summarise research on the dynamic and experience-driven nature of semantic processing, detailing ways in which processing is modified by both immediate and long-term context. Finally, I describe some next steps for lexical-semantic research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.025 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".