{"title":"人脸表征中的语义内容:优秀识别器熟练识别不熟悉面孔的必要条件","authors":"Tong Jiang, Guomei Zhou","doi":"10.1111/cogs.70020","DOIUrl":null,"url":null,"abstract":"<p>Face recognition is adapted to achieve goals of social interactions, which rely on further processing of the semantic information of faces, beyond visual computations. Here, we explored the semantic content of face representation apart from visual component, and tested their relations to face recognition performance. Specifically, we propose that enhanced visual or semantic coding could underlie the advantage of familiar over unfamiliar faces recognition, as well as the superior recognition of skilled face recognizers. We asked participants to freely describe familiar/unfamiliar faces using words or phrases, and converted these descriptions into semantic vectors. Face semantics were transformed into quantifiable face vectors by aggregating these word/phrase vectors. We also extracted visual features from a deep convolutional neural network and obtained the visual representation of familiar/unfamiliar faces. Semantic and visual representations were used to predict perceptual representation generated from a behavior rating task separately in different groups (bad/good face recognizers in familiar-face/unfamiliar-face conditions). Comparisons revealed that although long-term memory facilitated visual feature extraction for familiar faces compared to unfamiliar faces, good recognizers compensated for this disparity by incorporating more semantic information for unfamiliar faces, a strategy not observed in bad recognizers. This study highlights the significance of semantics in recognizing unfamiliar faces.</p>","PeriodicalId":48349,"journal":{"name":"Cognitive Science","volume":"48 11","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Semantic Content in Face Representation: Essential for Proficient Recognition of Unfamiliar Faces by Good Recognizers\",\"authors\":\"Tong Jiang, Guomei Zhou\",\"doi\":\"10.1111/cogs.70020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Face recognition is adapted to achieve goals of social interactions, which rely on further processing of the semantic information of faces, beyond visual computations. Here, we explored the semantic content of face representation apart from visual component, and tested their relations to face recognition performance. Specifically, we propose that enhanced visual or semantic coding could underlie the advantage of familiar over unfamiliar faces recognition, as well as the superior recognition of skilled face recognizers. We asked participants to freely describe familiar/unfamiliar faces using words or phrases, and converted these descriptions into semantic vectors. Face semantics were transformed into quantifiable face vectors by aggregating these word/phrase vectors. We also extracted visual features from a deep convolutional neural network and obtained the visual representation of familiar/unfamiliar faces. Semantic and visual representations were used to predict perceptual representation generated from a behavior rating task separately in different groups (bad/good face recognizers in familiar-face/unfamiliar-face conditions). Comparisons revealed that although long-term memory facilitated visual feature extraction for familiar faces compared to unfamiliar faces, good recognizers compensated for this disparity by incorporating more semantic information for unfamiliar faces, a strategy not observed in bad recognizers. This study highlights the significance of semantics in recognizing unfamiliar faces.</p>\",\"PeriodicalId\":48349,\"journal\":{\"name\":\"Cognitive Science\",\"volume\":\"48 11\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Science\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/cogs.70020\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Science","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cogs.70020","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
Semantic Content in Face Representation: Essential for Proficient Recognition of Unfamiliar Faces by Good Recognizers
Face recognition is adapted to achieve goals of social interactions, which rely on further processing of the semantic information of faces, beyond visual computations. Here, we explored the semantic content of face representation apart from visual component, and tested their relations to face recognition performance. Specifically, we propose that enhanced visual or semantic coding could underlie the advantage of familiar over unfamiliar faces recognition, as well as the superior recognition of skilled face recognizers. We asked participants to freely describe familiar/unfamiliar faces using words or phrases, and converted these descriptions into semantic vectors. Face semantics were transformed into quantifiable face vectors by aggregating these word/phrase vectors. We also extracted visual features from a deep convolutional neural network and obtained the visual representation of familiar/unfamiliar faces. Semantic and visual representations were used to predict perceptual representation generated from a behavior rating task separately in different groups (bad/good face recognizers in familiar-face/unfamiliar-face conditions). Comparisons revealed that although long-term memory facilitated visual feature extraction for familiar faces compared to unfamiliar faces, good recognizers compensated for this disparity by incorporating more semantic information for unfamiliar faces, a strategy not observed in bad recognizers. This study highlights the significance of semantics in recognizing unfamiliar faces.
期刊介绍:
Cognitive Science publishes articles in all areas of cognitive science, covering such topics as knowledge representation, inference, memory processes, learning, problem solving, planning, perception, natural language understanding, connectionism, brain theory, motor control, intentional systems, and other areas of interdisciplinary concern. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers in cognitive science and its associated fields, including anthropologists, education researchers, psychologists, philosophers, linguists, computer scientists, neuroscientists, and roboticists.