Going beyond rote auditory learning: Neural patterns of generalized auditory learning
Notice bibliographique
Résumé
Abstract The ability to generalize rapidly across specific experiences is vital for robust recognition of new patterns, especially in speech perception considering acoustic-phonetic pattern variability. Behavioral research has demonstrated that listeners are rapidly able to generalize their experience with a talker’s speech and quickly improve understanding of a difficult-to-understand talker without prolonged practice, e.g., even after a single training session. Here, we examine the differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker using a Pretest-Posttest design with electroencephalography (EEG). Participants were trained using either (1) a large inventory of words where no words repeated across the experiment (generalized learning) or (2) a small inventory of words where words repeated (rote learning). Analysis of long-latency auditory evoked potentials at Pretest and Posttest revealed that while rote and generalized learning both produce rapid changes in auditory processing, the nature of these changes differed. In the context of adapting to a talker, generalized learning is marked by an amplitude reduction in the N1-P2 complex and by the presence of a late-negative (LN) wave in the auditory evoked potential following training. Rote learning, however, is marked only by temporally later source configuration changes. The early N1-P2 change, found only for generalized learning, suggests that generalized learning relies on the attentional system to reorganize the way acoustic features are selectively processed. This change in relatively early sensory processing (i.e. during the first 250ms) is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to adaptively tune early auditory processing sensitivity. Statement of Significance Previous research on perceptual learning has typically examined neural responses during rote learning: training and testing is carried out with the same stimuli. As a result, it is not clear that findings from these studies can explain learning that generalizes to novel patterns, which is critical in speech perception. Are neural responses to generalized learning in auditory processing different from neural responses to rote learning? Results indicate rote learning of a particular talker’s speech involves brain regions focused on the memory encoding and retrieving of specific learned patterns, whereas generalized learning involves brain regions involved in reorganizing attention during early sensory processing. In learning speech from a novel talker, only generalized learning is marked by changes in the N1-P2 complex (reflective of secondary auditory cortical processing). The results are consistent with the view that robust speech perception relies on the fast adjustment of attention mechanisms to adaptively tune auditory sensitivity to cope with acoustic variability.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».