Human Auditory Steady-State Responses During Sweeps of Intensity
Notice bibliographique
Résumé
In Brief Objective: To record steady-state responses to amplitude-modulated tones that change their intensity over time and to see how well behavioral thresholds can be estimated from such responses. Design: The intensity of the stimuli used in this experiment increased from 25 to 75 dB SPL for 8 sec and then decreased back to 25 dB HL during the subsequent 8 sec. Responses to this intensity sweep were averaged and then analyzed using a short-time Fast-Fourier Transform to measure how the amplitude and phase of the responses changed with intensity. One experimental condition presented single 2-kHz tones to the left ear; a second condition examined the use of simultaneously presented multiple tones (0.5, 1, 2, and 4 kHz) to the left ear; a third condition used multiple tones presented dichotically; and a fourth condition presented the multiple dichotic tones in masking noise to simulate either low-frequency (less than 1400 Hz) or high-frequency (greater than 1400 Hz) hearing loss. Physiological thresholds were determined using six different algorithms and the relations between physiological and behavioral thresholds were evaluated to see how well behavioral thresholds could be estimated. Results: The amplitude-intensity functions for the 1 and 2 kHz responses both demonstrated a plateau at higher intensities in the multiple-stimulus conditions but not in the single-stimulus condition. The slope of the amplitude-intensity functions varied significantly with the carrier frequency of the stimulus: 1.30 at 500 Hz, 0.87 at 1000 Hz, 0.75 at 2000 Hz, and 1.40 at 4000 Hz. The slope of the phase-intensity function averaged 1.16 degrees per dB and did not vary with carrier frequency. Estimates of latency, however, indicated that latency increased with decreasing carrier frequency and with decreasing intensity. The performance of the threshold estimating algorithms differed between normal hearing and simulated hearing loss, since the amplitude- and phase-intensity functions in the latter condition were not linear. Physiological-behavioral threshold differences were generally greater for normal hearing than for simulated hearing loss. Linear regression provided the least physiological-behavioral difference but was quite variable during simulated hearing loss. Simply defining threshold as the lowest intensity above which all responses were significantly different from residual EEG noise was the most accurate method in terms of yielding the least standard deviation of the physiological-behavioral difference with an average standard deviation of 10 dB, provided EEG noise levels were low enough in the normal hearing condition. Conclusions: Thresholds can be estimated using intensity sweeps with about the same accuracy as recording separate responses to discrete intensities. Sweep recordings provide additional information about the responses at suprathreshold intensities by clearly determining amplitude- and phase- intensity functions at these intensities. This study recorded auditory steady–state responses to amplitude–modulated tones which slowly increased and decreased in intensity. From these sweeps, we could measure how the amplitude and phase of the responses changed with intensity. The accuracy of thresholds estimated from these amplitude–intensity and phase intensity functions were similar to those reported when recording separate responses at discrete intensities. Sweep recordings provide additional information about the responses at supra–threshold intensities by clearly determining amplitude and phase intensity functions at these intensities.
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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 | 0,000 |
| 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 ».