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为什么较长的歌曲元素更容易被察觉:大山雀的阈限-时长函数及其与人类数据的比较。

Why longer song elements are easier to detect: threshold level-duration functions in the Great Tit and comparison with human data.

机构信息

Animal Physiology and Behaviour Group, Fakultät V, IBU, and Research Center Neurosensory Science, Carl von Ossietzky University Oldenburg, Carl-von-Ossietzky Str. 9-11, 26129, Oldenburg, Germany.

出版信息

J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2013 Mar;199(3):239-52. doi: 10.1007/s00359-012-0789-z. Epub 2013 Jan 22.

DOI:10.1007/s00359-012-0789-z
PMID:23338560
Abstract

Our study estimates detection thresholds for tones of different durations and frequencies in Great Tits (Parus major) with operant procedures. We employ signals covering the duration and frequency range of communication signals of this species (40-1,010 ms; 2, 4, 6.3 kHz), and we measure threshold level-duration (TLD) function (relating threshold level to signal duration) in silence as well as under behaviorally relevant environmental noise conditions (urban noise, woodland noise). Detection thresholds decreased with increasing signal duration. Thresholds at any given duration were a function of signal frequency and were elevated in background noise, but the shape of Great Tit TLD functions was independent of signal frequency and background condition. To enable comparisons of our Great Tit data to those from other species, TLD functions were first fitted with a traditional leaky-integrator model. We then applied a probabilistic model to interpret the trade-off between signal amplitude and duration at threshold. Great Tit TLD functions exhibit features that are similar across species. The current results, however, cannot explain why Great Tits in noisy urban environments produce shorter song elements or faster songs than those in quieter woodland environments, as detection thresholds are lower for longer elements also under noisy conditions.

摘要

我们运用操作性条件反射程序来估计大山雀(Parus major)对不同时长和频率的音调的觉察阈限。我们使用的信号涵盖了该物种通讯信号的时长和频率范围(40-1010 毫秒;2、4、6.3 千赫兹),并且我们在安静环境以及行为相关的环境噪声条件下(城市噪声、林地噪声)测量了阈限时长函数(将阈限与信号时长联系起来)。随着信号时长的增加,觉察阈限降低。在任何给定时长下的阈限都是信号频率的函数,并且在背景噪声中会升高,但大山雀阈限时长函数的形状与信号频率和背景条件无关。为了能够将我们的大山雀数据与其他物种的数据进行比较,首先用传统的漏泄积分器模型来拟合阈限时长函数。然后,我们应用概率模型来解释在阈限时信号幅度和时长之间的权衡。大山雀的阈限时长函数表现出跨物种相似的特征。然而,当前的结果并不能解释为什么在嘈杂的城市环境中,大山雀比在安静的林地环境中产生更短的鸣唱元素或更快的鸣唱,因为在噪声环境下,较长的元素的检测阈限也更低。

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