2022年河南省顶级名校高考英语真题重组试卷(附答案详解).docxVIP

2022年河南省顶级名校高考英语真题重组试卷(附答案详解).docx

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第 =page 2 2页,共 =sectionpages 2 2页 第 =page 1 1页,共 =sectionpages 1 1页 2022年河南省顶级名校高考英语真题重组试卷 一、阅读理解(本大题共15小题,共30.0分) A If you ever get the impression that your dog can tell whether you look content or annoyed,you may be onto something.Dogs may indeed be able to distinguish between happy and angry human faces,according to a new study. Researchers trained a group of 11 dogs to distinguish between images(图像)of the same person making either a happy or an angry face.During the training stage,each dog was shown only the upper half or the lower half of the persons face.The researchers then tested the dogs ability to distinguish between human facial expressions by showing them the other half of the persons face or images totally different from the ones used in training.The researchers found that the dogs were able to pick the angry or happy face by touching a picture of it with their noses more often than one would expect by random chance. The study showed the animals had figured out how to apply what they learned about human faces during training to new faces in the testing stage. We can rule out that the dogs simply distinguish between the pictures based on a simple cue,such as the sight of teeth, said study author Corsin Muller. Instead,our results suggest that the successful dogs realized that a smiling mouth means the same thing as smiling eyes,and the same rule applies to an angry mouth having the same meaning as angry eyes. With our study,we think we can now confidently conclude that at least some dogs can distinguish human facial expressions, Muller told Live Science. At this point,it is not clear why dogs seem to be equipped with the ability to recognize different facial expressions in humans. To us,the most likely explanation appears to be that the basis lies in their living with humans,which gives them a lot of exposure to human facial expressions, and this exposure has provided them with many chances to learn to distinguish between them,Muller said. The ne

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