Computational modeling of human isolated auditory word recognition using DIANA
Bibliographic record
Abstract
In recent years, computational modeling has proved to be an essential tool for investigating cognitive processes underlying speech perception (see, e.g., Scharenborg & Boves, 2010). Here we address the question of how an end-to-end computational model that uses the acoustic signal as input simulates behavioral responses of actual participants. We used the Massive Auditory Lexical Decision (MALD) database recordings comprising of 26,800 isolated words produced by a single male native speaker of English. MALD response data came from 232 native speakers of English, with each participant responding to a subset of recorded words in an auditory lexical decision experiment (Tucker et al., submitted). We applied DIANA, a recently developed end-to-end computational model of word perception (Ten Bosch et al., 2013; Ten Bosch et al., 2015) to model the MALD response latency data. DIANA is a model that takes in the acoustic signal as input, activates internal word representations without assuming prelexical categorical decision, and outputs estimated response latencies and lexicality judgements. We report the results of the participant-to-model comparison, and discuss the simulated between-word competition as a function of time in the DIANA model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".