干旱区生态环境知识资源中心(Arid): SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network

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DOI	10.1007/s12559-023-10216-6
	SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network
	Sadeghzadeh, Nima; Farajzadeh, Nacer; Dattatri, Novia; Acevedo, Bianca P.
通讯作者	Farajzadeh, N
来源期刊	COGNITIVE COMPUTATION
ISSN	1866-9956
EISSN	1866-9964
出版年	2024
卷号	16 期号:3 页码:1379-1392
英文摘要	Sensory processing sensitivity (SPS) is a biological trait associated with heightened sensitivity and responsivity to the environment. One important question is how those with the trait perceive their environments, thus giving rise to differential responses and outcomes. In this study, we used an artificial intelligence (AI) model-SPS Vision Net-to investigate perceptual differences associated with SPS and to begin to predict sensitivity levels based on a visual perception task. 190 participants (M age = 22.91; 102 (53%) females), completed an online experiment where they rated 100 images from the Open Affective Standardized Image Set (OASIS) on arousal, valence, and visual saliency. They also completed the Highly Sensitive Person (HSP) Scale measure of SPS. Results showed that SPS was positively associated with arousal in response to negative (vs. positive and neutral images), and, namely, sad (vs. happy, neutral, or fear) images. Also, SPS was negatively associated with positive ratings of negative images, specifically those showing frightening images. SPS was unrelated to response times and the number of salient selection blocks made. However, the AI model showed high accuracy (83.31%) in predicting SPS levels (R2 = 0.77). Consistent with theory and research, this study showed that SPS is associated with higher arousal and lower positive ratings in response to the OASIS image rating task. Novel findings showed that a new, accurate AI-backed SPS measurement system, based on a visual selection, was predictive of HSP scores with high accuracy. Finally, the AI model indicates that visual perception differs as a function of SPS.
英文关键词	Sensory processing sensitivity Visual task Perception Emotion OASIS Artificial intelligence
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001095564500001
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS研究方向	Computer Science ; Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403221
推荐引用方式 GB/T 7714	Sadeghzadeh, Nima,Farajzadeh, Nacer,Dattatri, Novia,et al. SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network[J],2024,16(3):1379-1392.
APA	Sadeghzadeh, Nima,Farajzadeh, Nacer,Dattatri, Novia,&Acevedo, Bianca P..(2024).SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network.COGNITIVE COMPUTATION,16(3),1379-1392.
MLA	Sadeghzadeh, Nima,et al."SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network".COGNITIVE COMPUTATION 16.3(2024):1379-1392.