Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter.
Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter.
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DOI：
10.3389/fpsyg.2015.01921
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发表时间：
2015
影响因子：
3.8
通讯作者：
Malik A
中科院分区：
文献类型：
--
作者：
Babiker A;Faye I;Prehn K;Malik A
瞳孔直径（PD）被认为是识别个体情绪状态的可靠参数。在本文中，我们介绍了一种学习机器技术来检测和区分积极和消极情绪。我们给30名参与者正面和负面的声音刺激，并记录瞳孔的反应。结果表明，在处理消极和积极声音刺激时，瞳孔扩张显著增加，而在处理消极声音刺激时瞳孔扩张增加更大。我们还发现，在试验结束时，与积极刺激相比，消极刺激的扩张更持久，这被用于使用机器学习方法区分积极和消极情绪，该方法的准确性为96.5%，灵敏度为97.93%，特异性为98%。所获得的结果用另一个为另一项研究设计的数据集进行了验证，该数据集是在30名参与者处理带有积极和消极情绪的单词对时记录下来的。
Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual’s emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.