Cross-Modal Somatosensory Repetition Priming and Speech Processing
Bibliographic record
Abstract
BACKGROUND: Motor speech treatment approaches have been applied in both adults with aphasia and apraxia of speech and children with speech-sound disorders. Identifying links between motor speech intervention techniques and the modes of action (MoA) targeted would improve our understanding of how and why motor speech interventions achieve their effects, along with identifying its effective components. The current study focuses on identifying potential MoAs for a specific motor speech intervention technique. OBJECTIVES: We aim to demonstrate that somatosensory inputs can influence lexical processing, thus providing further evidence that linguistic information stored in the brain and accessed as part of speech perception processes encodes information related to speech production. METHODS: In a cross-modal repetition priming paradigm, we examined whether the processing of external somatosensory priming cues was modulated by both word-level (lexical frequency, low- or high-frequency) and speech sound articulatory features. The study participants were divided into two groups. The first group consisted of twenty-three native English speakers who received somatosensory priming stimulation to their oro-facial structures (either to labial corners or under the jaw). The second group consisted of ten native English speakers who participated in a control study where somatosensory priming stimulation was applied to their right or left forehead as a control condition. RESULTS: The results showed significant somatosensory priming effects for the low-frequency words, where the congruent somatosensory condition yielded significantly shorter reaction times and numerically higher phoneme accuracy scores when compared to the incongruent somatosensory condition. Data from the control study did not reveal any systematic priming effects from forehead stimulation (non-speech related site), other than a general (and expected) tendency for longer reaction times with low-frequency words. CONCLUSIONS: These findings provide further support for the notion that speech production information is represented in the mental lexicon and can be accessed through exogenous Speech-Language Pathologist driven somatosensory inputs related to place of articulation.
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 | 0.001 |
| 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) | 0.004 | 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".