Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory
Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this rel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1058609/full |
| _version_ | 1811210638830075904 |
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| author | Xiang Guo Yang Liu Yuzhong Zhang Chennan Wu |
| author_facet | Xiang Guo Yang Liu Yuzhong Zhang Chennan Wu |
| author_sort | Xiang Guo |
| collection | DOAJ |
| description | Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks. |
| first_indexed | 2024-04-12T04:58:10Z |
| format | Article |
| id | doaj.art-7b3fd9019ddf4a71bc9fef3d93db71f3 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-12T04:58:10Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7b3fd9019ddf4a71bc9fef3d93db71f32022-12-22T03:47:03ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-12-011610.3389/fnins.2022.10586091058609Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memoryXiang Guo0Yang Liu1Yuzhong Zhang2Chennan Wu3School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaSchool of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, CanadaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, ChinaAlthough theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks.https://www.frontiersin.org/articles/10.3389/fnins.2022.1058609/fullprogramming abilityfNIRSworking memoryconvolutional neural networkattention mechanism |
| spellingShingle | Xiang Guo Yang Liu Yuzhong Zhang Chennan Wu Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory Frontiers in Neuroscience programming ability fNIRS working memory convolutional neural network attention mechanism |
| title | Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory |
| title_full | Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory |
| title_fullStr | Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory |
| title_full_unstemmed | Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory |
| title_short | Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory |
| title_sort | programming ability prediction applying an attention based convolutional neural network to functional near infrared spectroscopy analyses of working memory |
| topic | programming ability fNIRS working memory convolutional neural network attention mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1058609/full |
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