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多音随机频率掩蔽下的音调检测中的陡峭时间整合。

Steep temporal integration for tone detection in a multi-tone, random-frequency masker.

机构信息

Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, Arizona 85721, USA.

出版信息

J Acoust Soc Am. 2023 Nov 1;154(5):3429-3437. doi: 10.1121/10.0022512.

Abstract

In the detection of a tone burst, masking by several tones with random frequencies can produce steep temporal integration. This feature was evaluated for nine normal-hearing adults for 1000-Hz tone bursts presented in a continuous train of four-tone masker bursts. Masker frequencies were randomly selected (250-4000 Hz) for each burst, with the proviso that all tones were separated by ≥0.2 oct. Bursts were 80-ms in duration; when present, signal bursts were gated synchronously with masker bursts. The observed mean temporal-integration function was exceptionally steep-thresholds improved by 26 dB as signal duration increased from 1 to 8 bursts. The results also showed that the individual differences were large, and that the mean psychometric function was exceptionally shallow, spanning a range of 35 dB between 0.6 and 0.9 proportion correct responses, consistent with previous reports. These findings were interpreted in the context of three signal-detection models, one based on the absolute-level cue, and two based on the relative-level cue via template matching; all cues were derived from the excitation patterns of the stimuli. Template-matching models were able to predict the shallow psychometric functions as observed, but all models fall short in the steepness of the observed temporal integration.

摘要

在探测音爆发时,随机频率的多个音的掩蔽可以产生陡峭的时间整合。对于 9 名正常听力成年人,在连续四音掩蔽爆发的音爆发中,评估了这种特性。对于每个爆发,掩蔽器频率随机选择(250-4000 Hz),但前提是所有音都间隔≥0.2 倍频程。爆发持续 80 毫秒;当信号爆发存在时,与掩蔽爆发同步门控。观察到的平均时间整合函数非常陡峭-随着信号持续时间从 1 到 8 个爆发的增加,阈值提高了 26 dB。结果还表明,个体差异很大,平均心理物理函数非常浅,在 0.6 到 0.9 个比例正确反应之间的范围为 35 dB,与之前的报告一致。这些发现是在三个信号检测模型的背景下解释的,一个基于绝对水平线索,两个基于模板匹配的相对水平线索;所有线索均源自刺激的激发模式。模板匹配模型能够预测到观察到的浅心理物理函数,但所有模型在观察到的时间整合的陡峭度方面都不足。

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