Revisiting neurological aspects of relevance : an EEG study

Pinkosova, Zuzana and McGeown, William J. and Moshfeghi, Yashar; Nicosia, Giuseppe and Giuffrida, Giovanni and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Pardalos, Panos and Di Fatta, Giuseppe and Umeton, Renato, eds. (2023) Revisiting neurological aspects of relevance : an EEG study. In: Machine Learning, Optimization, and Data Science - 8th International Conference, LOD 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13811 . Springer Science and Business Media Deutschland GmbH, ITA, pp. 549-563. ISBN 9783031258916 (https://doi.org/10.1007/978-3-031-25891-6_41)

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Abstract

Relevance is a key topic in Information Retrieval (IR). It indicates how well the information retrieved by the search engine meets the user's information need (IN). Despite research advances in the past decades, the use of brain imaging techniques to investigate complex cognitive processes underpinning relevance is relatively recent, yet has provided valuable insight to better understanding this complex human notion. However, past electrophysiological studies have mainly employed an event-related potential (ERP) component-driven approach. While this approach is effective in exploring known phenomena, it might overlook the key cognitive aspects that significantly contribute to unexplored and complex cognitive processes such as relevance assessment formation. This paper, therefore, aims to study the relevance assessment phenomena using a data-driven approach. To do so, we measured the neural activity of twenty-five participants using electroencephalography (EEG). In particular, the neural activity was recorded in response to participants' binary relevance assessment (relevant vs. non-relevant) within the context of a Question Answering (Q/A) Task. We found significant variation associated with the user’s subjective assessment of relevant and non-relevant information within the EEG signals associated with P300/CPP, N400 and, LPC components, which confirms the findings of previous studies. Additionally, the data-driven approach revealed neural differences associated with the previously not reported P100 component, which might play important role in early selective attention and working memory modulation. Our findings are an important step towards a better understanding of the cognitive mechanisms involved in relevance assessment and more effective IR systems.

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