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.
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