Moderating effects of self-perceived knowledge in a relevance assessment task: An EEG study

Pinkosova, Zuzana; McGeown, William J.; Moshfeghi, Yashar

doi:10.1016/j.chbr.2023.100295

Computers in Human Behavior Reports

2023

DOI: 10.1016/j.chbr.2023.100295

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Moderating effects of self-perceived knowledge in a relevance assessment task: An EEG study

Zuzana Pinkosova¹,

William J. McGeown²,

Yashar Moshfeghi³

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2024

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Cited by 3 publications

References 58 publications

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Comparing point‐wise and pair‐wise relevance judgment with brain signals

Zhu,

Xie,

et al. 2024

Asso for Info Science & Tech

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How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments.

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Comparing point‐wise and pair‐wise relevance judgment with brain signals

Zhu,

Xie,

et al. 2024

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

Development and validation of the Health Activation Scale for Children (HAS-C): an important intermediate outcome measure for health promotion initiatives

Ge,

Molina,

Karthigayan

et al. 2024

BMC Health Serv Res

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AI Eye-Tracking Technology: A New Era in Managing Cognitive Loads for Online Learners

Šola,

Qureshi,

Khawaja

2024

Education Sciences

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Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes.

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Moderating effects of self-perceived knowledge in a relevance assessment task: An EEG study

Cited by 3 publications

References 58 publications

Comparing point‐wise and pair‐wise relevance judgment with brain signals

Comparing point‐wise and pair‐wise relevance judgment with brain signals

Development and validation of the Health Activation Scale for Children (HAS-C): an important intermediate outcome measure for health promotion initiatives

AI Eye-Tracking Technology: A New Era in Managing Cognitive Loads for Online Learners

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