Do visible semantic primes preactivate lexical representations?
Bibliographic record
Abstract
Considerable research effort has been devoted to investigating semantic priming effects, particularly, the locus of those effects. Semantically related primes might activate their target's lexical representation (through automatic spreading activation at short stimulus onset asynchronies (SOAs), or through generation of words expected to follow the prime at longer SOAs). Alternately, semantically related primes might aid responding after target identification (i.e., postlexically). In contrast, masked orthographic priming effects appear to be lexical and automatic. Lexical processing of targets is facilitated by orthographically similar nonword primes and often inhibited by orthographically similar word primes (Davis & Lupker, 2006). Using the lexical-decision task (LDT), we found additivity between the facilitative effects of visible semantic primes and the facilitative effects of masked orthographically similar nonword primes at long and short SOAs, consistent with a postlexical locus of the semantic priming effects. Also consistent with this conclusion, semantic primes affected the skew of the distribution (larger effects on longer latency trials), whereas masked orthographic primes did not. In a final experiment, visible primes that were semantically related to the masked orthographic word primes did not make those primes more effective lexical inhibitors of orthographically similar targets (independent of SOA). Taken together, our findings suggest that the impact of a semantic prime is not to increase the lexical activation of related concepts. Rather, they suggest that the locus of semantic priming effects in LDTs is postlexical, in that discovering the existence of a relationship between the prime and target biases participants to make a "word" response. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".