Speech Perception and The Role of Semantic Richness in Processing
Bibliographic record
Abstract
The richness of meaning associated with specific words has been found to influence word recognition. Such findings, however, have come largely from studies based on visual word recognition, and related studies focusing on the acoustic signal and speech perception are less common. The present work recognizes that any effects observed may vary across modalities, and explores semantic richness effects as they may pertain to the perception and processing of spoken language. Goh et al. (2016) describe an auditory lexical decision experiment where concreteness, valence, arousal, semantic neighborhood density, and semantic diversity are found to affect spoken word recognition. The stimuli used in their study were limited to a set of fewer than 500 words, most of which were concrete nouns. In our study, we expand the scope of the analysis to include 9,086 words taken from the Massive Auditory Lexical Decision database (MALD; Tucker et al., 2019), each with corresponding values for each of the semantic variables of interest. In complement to the results described by Goh and colleagues, generalized additive mixed modelling indicates significant effects of concreteness, valence, arousal, and semantic neighborhood density on response latency. No effect was observed for semantic diversity. These results suggest that the processing of acoustic signals is influenced by top down semantic effects, even in decontextualized environments. While the specifics of these effects differ by semantic variable, it appears that increased semantic richness facilitates spoken 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".