#李准基[超话]#[米奇比心]#李准基ins#[米奇比心]#李准基2020新剧大发#[米奇比心]@李准基
//2020.6.5李准基Ins更新新剧《恶之》预告片
大半夜营业让我们make some noise看来欧巴是真得很激动，而且一开始还打错新剧英文名被我发现了，这是比我们还激动吧[笑cry][笑cry][笑cry]
不过必须尖叫，为这耐人寻味、越看越帅的笑容尖叫[憧憬][憧憬][憧憬]
//2020.6.5李准基Ins更新新剧《恶之》预告片
大半夜营业让我们make some noise看来欧巴是真得很激动，而且一开始还打错新剧英文名被我发现了，这是比我们还激动吧[笑cry][笑cry][笑cry]
不过必须尖叫，为这耐人寻味、越看越帅的笑容尖叫[憧憬][憧憬][憧憬]
你要学会捂上自己的耳朵，不去听那些熙熙攘攘的声音；这个世界上没有不苦逼的人，真正能治愈自己的，只有你自己。
You have to learn to cover their ears, not to listen to the noise of the bustling; There is no one in this world who is not helpless and painful. Only you can truly heal yourself.
You have to learn to cover their ears, not to listen to the noise of the bustling; There is no one in this world who is not helpless and painful. Only you can truly heal yourself.
#听障儿童早期教育# 听障者的面部表情强度和信号使用
我们生活在一个充满动态多感官信号的世界。健听者可以快速有效地整合多模态信号并解码为生物相关的面部表情。然而，在缺乏听觉感觉通道的情况下，听障成年人如何解码面部表情的机制仍不清楚。因此，我们在一项心理物理任务中比较了早期重度听障者（会手语，n =46）和健听者(不会手语，n =48)对六种基本面部表情的识别表现。我们利用中性表达的图像形态和有干扰到无干扰的图像，考察了被试完成图片识别所需的强度和信号水平。我们使用贝叶斯模型，发现听障者被试需要更多的信号和强度来识别厌恶表情，而其他表情的表现与健听者差不多。我们的研究结果为听障者（面部表情识别）的强度和信号使用提供了一个可靠的基准，并对健听者和听障者面部表情的差异编码有了新的认识。
pS1:这篇文章提出了一个有意思的点，听障者对厌恶表情的识别更差！当然不能断章取义，只是在有干扰的情况与实验状况下，会有这种情况。在日常生活中，听障儿童对面部表情的识别是特别敏感的。
Quantifying Facial Expression Intensity and Signal Use in Deaf Signers
We live in a world of rich dynamic multisensory signals. Hearing individuals rapidly and effectively integrate multimodal signals to decode biologically relevant facial expressions of emotion. Yet, it remains unclear how facial expressions are decoded by deaf adults in the absence of an auditory sensory channel.We thus compared early and profoundly deaf signers (n =46) with hearing nonsigners (n =48) on a psychophysical task designed to quantify their recognition performance for the six basic facial expressions of emotion. Using neutral-to-expression image morphs and noise-to-full signal images, we quantified the intensity and signal levels required by observers to achieve expression recognition. Using Bayesian modeling, we found that deaf observers require more signal and intensity to recognize disgust, while reaching comparable performance for the remaining expressions. Our results provide a robust benchmark for the intensity and signal use in deafness and novel insights into the differential coding of facial expressions of emotion between hearing and deaf individuals.
我们生活在一个充满动态多感官信号的世界。健听者可以快速有效地整合多模态信号并解码为生物相关的面部表情。然而，在缺乏听觉感觉通道的情况下，听障成年人如何解码面部表情的机制仍不清楚。因此，我们在一项心理物理任务中比较了早期重度听障者（会手语，n =46）和健听者(不会手语，n =48)对六种基本面部表情的识别表现。我们利用中性表达的图像形态和有干扰到无干扰的图像，考察了被试完成图片识别所需的强度和信号水平。我们使用贝叶斯模型，发现听障者被试需要更多的信号和强度来识别厌恶表情，而其他表情的表现与健听者差不多。我们的研究结果为听障者（面部表情识别）的强度和信号使用提供了一个可靠的基准，并对健听者和听障者面部表情的差异编码有了新的认识。
pS1:这篇文章提出了一个有意思的点，听障者对厌恶表情的识别更差！当然不能断章取义，只是在有干扰的情况与实验状况下，会有这种情况。在日常生活中，听障儿童对面部表情的识别是特别敏感的。
Quantifying Facial Expression Intensity and Signal Use in Deaf Signers
We live in a world of rich dynamic multisensory signals. Hearing individuals rapidly and effectively integrate multimodal signals to decode biologically relevant facial expressions of emotion. Yet, it remains unclear how facial expressions are decoded by deaf adults in the absence of an auditory sensory channel.We thus compared early and profoundly deaf signers (n =46) with hearing nonsigners (n =48) on a psychophysical task designed to quantify their recognition performance for the six basic facial expressions of emotion. Using neutral-to-expression image morphs and noise-to-full signal images, we quantified the intensity and signal levels required by observers to achieve expression recognition. Using Bayesian modeling, we found that deaf observers require more signal and intensity to recognize disgust, while reaching comparable performance for the remaining expressions. Our results provide a robust benchmark for the intensity and signal use in deafness and novel insights into the differential coding of facial expressions of emotion between hearing and deaf individuals.
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