越小的孩子越能理解动物叫声的含义

It may sound barking, but 10-year-olds can understand dogs better than people of any other age.

也许在我们听来只是狗叫的声音，在一个十岁的孩子看来却不是，他能更好地领会狗狗说话的含义。

Researchers at Eo?tvo?s Lora?nd University in Budapest found that humans understand a dog’s bark from an early age, but that after 10, are not able to decipher meanings so easily.

在布达佩斯的一所大学中，研究人员发现孩子越小越能理解狗狗说话的含义，但一旦到了10岁，他们就不能那样容易的理解了。

In tests, volunteers found it easiest to distinguish when a dog was angry – but 10-year-olds excelled at interpreting more subtle noises.

实验中，志愿者们都说区分狗狗是否在生气是最容易的，但是10岁左右的孩子更擅长区分狗狗发出来的更细小的声音。

The study’s results came from playing recordings of various bark ‘modes’ - such as warning off a stranger, playing and feeling lonely - to children aged six, eight and 10, and adults, and asking them to pair the noises with corresponding human facial expressions.

实验是通过播放不同的狗叫声来让人辨认的，例如对陌生人的警告，或是独自玩耍的孤独，将这些声音播放出来后，让六岁，八岁，十岁的孩子及成人把这些声音与相对应的人类表情归类。

The authors, Pe?ter Pongra?cz and Csaba Molnár, said: ‘This shows that the ability of understanding basic inner states of dogs on the basis of acoustic signals is present in humans from a very young age.

研究者们说，“实验说明，在人类非常年轻的时候，他们依据听觉信号似乎更能理解狗狗的意思。”

'These results are in sharp contrast with other reports in the literature which showed that young children tend to misinterpret canine visual signals.’

“这个实验结果和之前的说小孩子会误解犬类视觉信号的实验形成了鲜明对比。”

Molnár's other research in the field includes using machine-learning algorithms in an effort to further understand how humans 'listen' to dog barks.

他们的在该领域的其他实验，例如利用机器学习算法来更深入了解人们是怎样聆听狗叫的。

Molnár and colleagues’ tested a computer algorithm’s ability to identify and differentiate the acoustic features of dog barks, and classify them according to different contexts and individual dogs.

研究人员利用计算机算法鉴别和区分的能力的声学特征的狗叫，并根据狗的不同种类将他们分类。

In the first experiment looking at classification of barks into different situations, the software correctly classified the barks in 43 per cent of cases.

在第一个实验中，他们将狗叫根据不同情况进行分类，这个软件的准确率高达43%。

In the second experiment looking at the recognition of individual dogs, the algorithm correctly classified the barks in 52 per cent of cases.

在第二个实验中，是根据狗的不同种类进行区分，这次，该软件的准确率达到了52%。

The software could reliably discriminate among individual dogs while humans cannot, which suggests that there are individual differences in barks of dogs even though humans are not able to recognise them.

该软件能够可靠的辨别人类不能辨别的不同种类的狗，这说明，不同的狗叫声是存在不同的，尽管人类似乎不能分辨出来。

The authors concluded: ‘The use of advanced machine learning algorithms to classify and analyse animal sounds opens new perspectives for the understanding of animal communication. The promising results obtained strongly suggest that advanced machine learning approaches deserve to be considered as a new relevant tool for studying animal behaviour.’

研究人员总结说，“这种分类和分析动物声音的高级机器为人类理解动物之间的沟通打开了新局面。这项非常有成效的结果强有力的证明了，如此先进的机器学习法应该被视为研究动物行为的一种新的相关的工具。”