Human Auditory Steady-State Responses During Sweeps of Intensity
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".