Towards predicting a realisation of an information need based on brain signals

Moshfeghi, Yashar and Triantafillou, Peter and Pollick, Frank E.; (2019) Towards predicting a realisation of an information need based on brain signals. In: The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. ACM, USA, pp. 1300-1309. ISBN 9781450366748 (

[thumbnail of Moshfeghi-etal-WWW2019-Towards-predicting-a-realisation-of-an-information-need-based]
Text. Filename: Moshfeghi_etal_WWW2019_Towards_predicting_a_realisation_of_an_information_need_based.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo

Download (604kB)| Preview


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 interms of prediction accuracy. These encouraging findings make the first fundamental step towards proactive IR engines based on brain signals.