The Effect of Residual Acoustic Hearing and Adaptation to Uncertainty on Speech Perception in Cochlear Implant Users
Notice bibliographique
Résumé
In Brief Objectives: While outcomes with cochlear implants (CIs) are generally good, performance can be fragile. The authors examined two factors that are crucial for good CI performance. First, while there is a clear benefit for adding residual acoustic hearing to CI stimulation (typically in low frequencies), it is unclear whether this contributes directly to phonetic categorization. Thus, the authors examined perception of voicing (which uses low-frequency acoustic cues) and fricative place of articulation (s/∫, which does not) in CI users with and without residual acoustic hearing. Second, in speech categorization experiments, CI users typically show shallower identification functions. These are typically interpreted as deriving from noisy encoding of the signal. However, psycholinguistic work suggests shallow slopes may also be a useful way to adapt to uncertainty. The authors thus employed an eye-tracking paradigm to examine this in CI users. Design: Participants were 30 CI users (with a variety of configurations) and 22 age-matched normal hearing (NH) controls. Participants heard tokens from six b/p and six s/∫ continua (eight steps) spanning real words (e.g., beach/peach, sip/ship). Participants selected the picture corresponding to the word they heard from a screen containing four items (a b-, p-, s- and ∫-initial item). Eye movements to each object were monitored as a measure of how strongly they were considering each interpretation in the moments leading up to their final percept. Results: Mouse-click results (analogous to phoneme identification) for voicing showed a shallower slope for CI users than NH listeners, but no differences between CI users with and without residual acoustic hearing. For fricatives, CI users also showed a shallower slope, but unexpectedly, acoustic + electric listeners showed an even shallower slope. Eye movements showed a gradient response to fine-grained acoustic differences for all listeners. Even considering only trials in which a participant clicked “b” (for example), and accounting for variation in the category boundary, participants made more looks to the competitor (“p”) as the voice onset time neared the boundary. CI users showed a similar pattern, but looked to the competitor more than NH listeners, and this was not different at different continuum steps. Conclusion: Residual acoustic hearing did not improve voicing categorization suggesting it may not help identify these phonetic cues. The fact that acoustic + electric users showed poorer performance on fricatives was unexpected as they usually show a benefit in standardized perception measures, and as sibilants contain little energy in the low-frequency (acoustic) range. The authors hypothesize that these listeners may overweight acoustic input, and have problems when this is not available (in fricatives). Thus, the benefit (or cost) of acoustic hearing for phonetic categorization may be complex. Eye movements suggest that in both CI and NH listeners, phoneme categorization is not a process of mapping continuous cues to discrete categories. Rather listeners preserve gradiency as a way to deal with uncertainty. CI listeners appear to adapt to their implant (in part) by amplifying competitor activation to preserve their flexibility in the face of potential misperceptions. We examined speech categorization in cochlear implant (CI) users and normal-hearing (NH) listeners using both phoneme categorization and eye-tracking. CI users showed shallower categorization slopes for voicing and fricative continua. While residual acoustic hearing did not affect voicing slopes, it led to even shallower functions for fricatives, despite the fact that fricatives do not contain information in the frequencies they receive acoustically. Eye-movements showed that both groups of listeners show a highly gradient response to fine-grained phonetic detail. Moreover, CI users exhibited heightened activation for competing words. This could be useful for preserving flexibility in the face of potential misperceptions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».