Tracking attentional states: Assessing the relationship between sustained and selective focused attention in visual working memory

Arnicane, Andra; Souza, Alessandra S.

doi:10.3758/s13414-021-02394-y

Cited by 5 publications

(4 citation statements)

References 105 publications

(164 reference statements)

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“…Candidate Feedback on Pre-defined Parameters at EBP: After the MCQs, participants' feedback regarding that session on a scale of 1 to 10 will be recorded which will further help with customized recommendations. (7). Candidate Learning Grade Entry at EBP: Participant's learning grade i.e., Excellent, Good, Poor will be stored.…”

Section: Discussionmentioning

confidence: 99%

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Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Pathak,

Kashyap

2023

100

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The COVID-19 pandemic has precipitated an unprecedented surge in the proliferation of online E-learning platforms, designed to cater to a wide array of subjects across all age groups. However, a paucity of these platforms adopts a learner-centric approach or validates user learning, underscoring the need for effective E-learning validation and personalized learning recommendations. This paper addresses these challenges by implementing an innovative approach that leverages real-time electroencephalogram (EEG) signals collected from learners, who don neuro headsets while partaking in online courses. These EEG signals are subsequently classified using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) deep learning models, with the intent of discerning the efficacy of the E-learning process. The proposed models have yielded promising classification accuracies of 68% and 97% for the CNN and LSTM models, respectively, demonstrating their rapidity and precision in classifying E-learning EEG signals. Thus, these models hold substantial potential for application in similar E-learning validation scenarios. Furthermore, this study introduces an automated framework designed to track the learning curve of users and furnish valuable recommendations for E-learning materials. The presented approach, therefore, not only validates the E-learning process but also aids in optimizing the learning experiences on E-learning platforms.

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Section: Discussionmentioning

confidence: 99%

“…But those approaches are not that effective due to the usage of limited EEG datasets and lesser accuracy. Non-usage of various deep learning models also plays a crucial role in this [7][8][9].…”

Section: Student's Low Attention Spanmentioning

confidence: 99%

Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Pathak,

Kashyap

2023

100

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“…Attention aids in prioritizing information processing and in generating or retaining information without external input, which is crucial for processing working memory (WM) [ 1 ]. Working memory is strongly linked to attention, and workers’ sustained attention and the nature of their activity impact their working memory [ 2 ]. This leads to sustained attention, which refers to the ability to actively analyze recurrent stimuli without being distracted by other stimuli that might cause habituation [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Musical Environments on Designers’ Attention: Persistent Music Listening Interferes with Attention

Yu,

Chen

2024

Behavioral Sciences

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Research indicates that music can influence human cognitive functions. Diverse musical settings can affect alertness, orientation, and executive control of attention in various populations. Exploring the relationship between designers with highly creative thinking and music environments can provide new research perspectives for the cognitive field. A total of 94 students, consisting of 61 design majors and 33 non-design majors, completed the Attention Network Test (ANT) on a computer under three test environments: cheerful music, melancholic music, and silence. The study results indicated that the alerting network effect between the design professional group and the control group was marginally significant. However, there were no significant differences between the groups in the orienting subsystem and the executive control subsystem. Within the design professional group, the attentional network data indicated that participants showed improved performance in alerting and orienting attention in a music-free environment compared to cheerful and melancholic music environments (pa = 0.028, po = 0.008). Nevertheless, executive control attention did not show significant differences across the music environments. In conclusion, existing research confirms that designers are more susceptible to distraction from external stimuli; thus, music-free environments assist them in concentrating.

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“…The transition from traditional to distance education forms, including online learning, remains a research issue ( Jammula et al., 2021 ; Arnicane and Souza, 2022 ). Online learning implies a certain adaptation and transformation process of educational systems, tools, forms, and learning technologies.…”

Section: Introductionmentioning

confidence: 99%

Color education: A study on methods of influence on memory

Diachenko

Kalishchuk²,

Zhylin³

et al. 2022

Heliyon

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Tracking attentional states: Assessing the relationship between sustained and selective focused attention in visual working memory

Cited by 5 publications

References 105 publications

Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks

The Effect of Musical Environments on Designers’ Attention: Persistent Music Listening Interferes with Attention

Color education: A study on methods of influence on memory

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