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Objective: The ability to hear in background noise is related to the processing of the incoming acoustic signal in the peripheral auditory system as well as the central auditory nervous system (CANS). Electrophysiological tests have the ability to demonstrate the underlying neural integrity of the CANS, but to date a lack of literature exists demonstrating the effects of background noise on auditory cortical potentials. Therefore, the purpose of this investigation was to systematically investigate the effects of white noise on tone burst-evoked late auditory evoked potentials (N1, P2, and P3) in normal hearing young adults. Study Design: Twenty young-adult normal-hearing individuals served as subjects. A comparison of the late auditory evoked potentials (N1, P2, and P3) was made at multiple signal-to-noise ratios (SNRs) (quiet, + 20, + 10, 0). N1, P2, and P3 were elicited and both amplitude and latency were measured for each of the potentials. A standard oddball paradigm with binaural stimulation was used to evoke the potentials. Repeated Measures Analyses of Variance (ANOVA) were conducted for both the experimental factors of amplitude and latency with within subjects factors of condition (quiet, + 20, + 10, 0). Results: Results indicated no significant differences in N1, P2, or P3 amplitude or latency between the quiet and + 20 SNR condition; however, at poorer SNRs significant N1, P2, and P3 amplitude and/or latency differences were observed. Conclusion: The results indicate a change in higher-order neural function related to the presence of increased noise in the environment. © 2012 Informa Healthcare.
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OBJECTIVE: The purpose of the present study was to investigate the value of a new gap detection procedure called Gaps-In-Noise (GIN) for assessment of temporal resolution in a clinical population. DESIGN: The test consists of 0 to 3 silent intervals ranging from 2 to 20 msec embedded in 6-sec segments of white noise. The location, number, and duration of the gaps per noise segment vary throughout the test for a total of 60 gaps presented in each of four lists. The GIN procedure was administered to 50 normal-hearing listeners (group I) and 18 subjects with confirmed neurological involvement of the central auditory nervous system (group II). RESULTS: Results showed mean approximated gap detection thresholds of 4.8 msec for the left ear and 4.9 msec for the right ear for group I. In comparison, results for group II demonstrated a statistically significant increase in gap detection thresholds, with approximated thresholds of 7.8 msec and 8.5 msec being noted for the left and right ears, respectively. Significant mean differences were also observed in the overall performance scores (i.e., the identification of the presence of the gaps within the noise segments) of the two groups of subjects. Finally, psychometric functions, although similar for short and long duration gaps, were highly different for gaps in the 4- to 10-msec range for the two groups. CONCLUSIONS: A variety of psychoacoustic procedures are available to assess temporal resolution; however, the clinical use of these procedures is minimal at best. Results of the present study show that the GIN test holds promise as a clinically useful tool in the assessment of temporal resolution in the clinical arena. Copyright © 2005 by Lippincott Williams & Wilkins.
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PURPOSE: Artificial intelligence (AI) chatbots based on large language models (LLMs) can deliver medical information, but their performance on specialized topics such as central auditory processing disorder (CAPD) remains unexplored. This study evaluated the accuracy and completeness of three AI chatbots (ChatGPT, Gemini, and Claude) in providing CAPD-related information across varying levels of question complexity. METHOD: Forty-four questions, categorized into four difficulty levels (patient level, easy, intermediate, and specialized; n = 11 each), were submitted to each chatbot, generating 132 responses. Seven clinical experts, blinded to chatbot identity, independently rated accuracy and completeness on a 1-5 Likert scale. Data were analyzed with analyses of variance, correlations, and interrater comparisons. RESULTS: Chatbot performance was similar, with mean accuracy below 4.0 and completeness about 3.5. Complex questions often scored below 3.0 across experts. Only three of the 44 questions, primarily patient level or relatively simple, received consistently high expert ratings (≥ 4 for both accuracy and completeness) across all three chatbots. Performance declined with question difficulty, although differences were not statistically significant. Accuracy and completeness were correlated across chatbots. CONCLUSIONS: Current AI chatbots provided generally accurate CAPD information but fell short of clinical standards, particularly on specialized questions. Their limited performance underscores the need for clinician oversight in CAPD assessment and management. Chatbots may serve as helpful adjuncts but should not replace expert evaluation and guidance in clinical settings. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.31975101.
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