Scientometrics Research Journal

Scientometrics Research Journal

Identifying Conceptual Clusters and Key Players in Attention Assessment Research: An Analysis of Co-Authorship Networks and Keyword Co-Occurrence

Document Type : Research Paper

Authors
Gholamrezahamed Manouchehrian 1
Shahnaz Pournaseri 1
Bahram Saleh Sedghpour 2
1 Architecture, Architectural Engineering & Urban Design, Shahid Rajaee Teacher Training University, Tehran, Iran.
2 Faculty of Educational Sciences, University of Humanities, Shahid Rajaee Teacher Training University, Tehran, Iran
10.22070/rsci.2025.20191.1791
Abstract
Purpose: This research aims to explore and analyze trends in studies related to "attention measurement." Specifically, it examines the co-occurrence patterns of keywords and the co-authorship networks of researchers in this field.
Methodology: This research employs a documentary approach, specifically utilizing bibliometric analysis. The data for this study were extracted from the reputable and comprehensive Web of Science database, covering the period from 2000 to 2025. For data analysis and pattern extraction related to the research topic, specialized bibliometric software such as VOSviewer and Biblioshiny were used.
Findings: This study includes 1,519 documents with an average age of 8.75 years, spanning 474 publications. Each document has been cited an average of 30.61 times, and a total of 5,652 authors have contributed to their creation. The results indicate that research in this field is expanding. Studies in Iran began in 2016 and peaked in 2024, reflecting the growing interest of domestic researchers in this area. The top five countries publishing these documents are the United States, China, Germany, Australia, and the United Kingdom, with Iran ranking 15th with 39 articles. The most significant international collaborations involve China, the United States, Germany, the United Kingdom, and Australia. Of the 1,519 studies conducted between 2000 and 2025, the majority of documents fall into four main research areas: psychiatry, neuroscience, clinical neurology, and psychology. The leading authors, based on the number of articles published, are affiliated with the University of Wollongong and the University of London. Most of their work has appeared in the journals "Clinical Neurophysiology" and the "International Journal of Psychophysiology". Regarding frequently used terminology, the most commonly mentioned topics include measurement methods, electroencephalography, brain modeling, event-related potentials, brain-computer interfaces, deep learning, continuous performance tests, convolutional neural networks, and neuropsychology. This indicates the researchers' use of these methods in their studies and underscores their reliability. The emergence of terms such as "deep learning," "machine learning, neural networks," and "binary neural networks" reflects the recent global trend in research focusing on these advanced computational techniques.
Conclusion: Based on the clustering of extracted keywords, nine distinct conceptual categories of documents were identified. These categories include concepts related to attention measurement and neuroscience, computational models of the brain and cognition, behavioral and cognitive studies, neurocognitive assessments, attention deficit/hyperactivity disorder, executive functions and attention, cognitive functions and emotions, auditory attention, and neurocognitive disorders. The most frequently repeated keywords pertain to measurement methods and tests in the fields of cognition and attention. These include electroencephalography, brain modeling, event-related potentials, brain-computer interfaces, deep learning, continuous performance tests, convolutional neural networks, and neuropsychology. Recent studies indicate a global shift in research focus toward deep learning and machine learning, highlighted by the rise of terms such as deep learning, machine learning, convolutional neural networks, and binary neural networks. The research suggests that Iranian researchers collaborate with international experts in attention assessment to enhance research quality and global impact. Such partnerships would enable them to learn new methodologies, utilize advanced equipment, and access extensive data, ultimately improving their scientific progress and credibility. Policymakers and scientific managers should evaluate the strengths and weaknesses of attention assessment research to develop fair policies that support researchers. These policies should provide both material and moral support to outstanding researchers, enhance research impact, and strengthen the country's scientific reputation by encouraging publication in reputable journals and promoting international collaborations. Future research should evaluate Iran's scientific productivity, competence, and network analyses in the field of attention assessment, comparing these metrics with those of developed and regional countries using citation databases such as Scopus and PubMed. Employing co-word analysis methods, the intellectual structure of the attention assessment field should be examined and compared using two types of keywords: abstract keywords and author-assigned keywords. Additionally, the factors facilitating and hindering scientific collaboration among Iranian researchers in the field of attention assessment should be investigated. The co-citation networks of Iranian researchers studying attention assessment should be analyzed, and the scientific outputs related to attention measurement tools must be reviewed within citation databases. This review should emphasize influential factors affecting attention as well as research focused on attention disorders. Furthermore, examining the impact of social and cultural factors on research design facilitates the adaptation of methods to diverse populations and highlights cultural variability. Investigating ethical considerations in research encourages responsible practices and the development of ethical guidelines. Additionally, understanding the genetic factors associated with attention and its disorders offers valuable insights into their underlying causes, with relevant keywords aiding in the comparison of research findings.
Keywords
Attention assessment
Syntactic analysis
Co-Event analysis
Scientometrics
Web of Science
Subjects
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