Prediction of the Realisation of an Information Need: An EEG Study

TL;DR

This study demonstrates that EEG data can accurately predict the realization of information needs in real-time during a question-answering task, bridging neuroscience and information retrieval.

Contribution

It introduces a method for predicting information need realization using EEG data, showing high accuracy and identifying key neural regions involved.

Findings

EEG data predicts information need realization with 73.5% accuracy across subjects.

Per-subject prediction accuracy reaches 90.1%.

Optimal EEG feature combinations enhance prediction performance.

Abstract

One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN). Understanding how INs physically manifest has long been a complex and elusive process. However, recent studies utilising Electroencephalography (EEG) data have provided real-time insights into the neural processes associated with INs. Unfortunately, they have yet to demonstrate how this insight can practically benefit the search experience. As such, within this study, we explore the ability to predict the realisation of IN within EEG data across 14 subjects whilst partaking in a Question-Answering (Q/A) task. Furthermore, we investigate the combinations of EEG features that yield optimal predictive performance, as well as identify regions within the Q/A queries where a subject's realisation of IN is more pronounced. The findings from this work demonstrate that EEG data is sufficient for the real-time prediction of the realisation of an IN across all subjects with an accuracy of 73.5% (SD 2.6%) and on a per-subject basis with an accuracy of 90.1% (SD 22.1%). This work helps to close the gap by bridging theoretical neuroscientific advancements with tangible improvements in information retrieval practices, paving the way for real-time prediction of the realisation of IN.