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Towards predicting a realisation of an information need based on brain signals
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Moshfeghi, Yashar, Triantafillou, Peter and Pollick, Frank (2019) Towards predicting a realisation of an information need based on brain signals. In: WWW '19 The World Wide Web Conference, San Francisco CA USA, 13-17 May 2019. Published in: WWW '19 The World Wide Web Conference pp. 1300-1309. ISBN 9781450366748. doi:10.1145/3308558.3313671
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Official URL: https://doi.org/10.1145/3308558.3313671
Abstract
The goal of Information Retrieval (IR) systems is to satisfy searchers' Information Need (IN). Our research focuses on next-generation IR engines, which can proactively detect, identify, and serve INs without receiving explicit queries. It is essential, therefore, to be able to detect when INs occur. Previous research has established that a realisation of INs physically manifests itself with specific brain activity. With this work we take the next step, showing that monitoring brain activity can lead to accurate predictions of a realisation of IN occurrence. We have conducted experiments whereby twenty-four participants performed a Q/A Task, while their brain activity was being monitored using functional Magnetic Resonance Imaging (fMRI) technology. The questions were selected and developed from the TREC-8 and TREC 2001 Q/A Tracks. We present two methods for predicting the realisation of an IN, i.e. Generalised method (GM) and Personalised method (PM). GM is based on the collective brain activity of all twenty-four participants in a predetermined set of brain regions known to be involved in representing a realisation of INs. PM is unique to each individual and employs a 'Searchlight' analysis to locate brain regions informative for distinguishing when a “specific” user realises an information need. The results of our study show that both methods were able to predict a realisation of an IN (statistically) significantly better than chance. Our results also show that PM (statistically) significantly outperformed GM in terms of prediction accuracy. These encouraging findings make the first fundamental step towards proactive IR engines based on brain signals.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Journal or Publication Title: | WWW '19 The World Wide Web Conference | ||||||
Publisher: | ACM Press | ||||||
ISBN: | 9781450366748 | ||||||
Official Date: | May 2019 | ||||||
Dates: |
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Page Range: | pp. 1300-1309 | ||||||
DOI: | 10.1145/3308558.3313671 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | ** Article version: VoR ** From Crossref via Jisc Publications Router ** Licence for VoR version of this article starting on 13-05-2019: http://www.acm.org/publications/policies/copyright_policy#Background | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | WWW '19 The World Wide Web Conference | ||||||
Type of Event: | Conference | ||||||
Location of Event: | San Francisco CA USA | ||||||
Date(s) of Event: | 13-17 May 2019 | ||||||
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