Understanding Relevance: An fMRI Study

Moshfeghi, Yashar; Pinto, Luisa R.; Pollick, Frank E.; Jose, Joemon M.

doi:10.1007/978-3-642-36973-5_2

Cited by 50 publications

(41 citation statements)

References 24 publications

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“…implementing intentions [18]). The wealth of knowledge from various fields underlines the fundamental complexity of relevance, which may be the reason why the question of "how does relevance happen in the brain" remains unanswered [25].…”

Section: Related Workmentioning

confidence: 99%

“…However, the results seem to be contradictory and validated only for image or video stimuli. These media allow measuring users for extended periods of time, which in itself is known to cause emotional responses and more substantial physiological responses [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of information directly from brain signals. Research has begun to build an understanding of the neural activity associated with relevance detection [25], but 1) the exact brain areas associated with relevance judgments remain unknown and 2) the methods to non-intrusively quantify the neuronal activity can be very noisy [19]. Nevertheless, the advantages of using brain signals to predict relevance are 1) that recording brain signals does not require any explicit user interaction, and 2) the signals can be captured with high throughput.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting term-relevance from brain signals

Eugster

Ruotsalo

Spapé

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Term-Relevance Prediction from Brain Signals (TRPB) is proposed to automatically detect relevance of text information directly from brain signals. An experiment with forty participants was conducted to record neural activity of participants while providing relevance judgments to text stimuli for a given topic. High-precision scientific equipment was used to quantify neural activity across 32 electroencephalography (EEG) channels. A classifier based on a multi-view EEG feature representation showed improvement up to 17% in relevance prediction based on brain signals alone. Relevance was also associated with brain activity with significant changes in certain brain areas. Consequently, TRPB is based on changes identified in specific brain areas and does not require user-specific training or calibration. Hence, relevance predictions can be conducted for unseen content and unseen participants. As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest. Our research shows that detecting relevance from brain signals is possible and allows the acquisition of relevance judgments without a need to observe any other user interaction. This suggests that TRPB could be used in combination or as an alternative for conventional implicit feedback signals, such as dwell time or click-through activity.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting term-relevance from brain signals

Eugster

Ruotsalo

Spapé

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…[56]. Although some of them have been explored by IR researchers [47], their impacts on search users' time perception have not been paid enough attention. When a task becomes more complex, people tend to underestimate the actual passage of time [33].…”

Section: Time Perception In Psychologymentioning

confidence: 99%

Does Document Relevance Affect the Searcher's Perception of Time?

Luo

Liu

Sakai

et al. 2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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Time plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted as a measure to quantify users' efforts in search processes. Psychological studies have reported that the time perception of human beings can be affected by many stimuli, such as attention and motivation, which are closely related to many cognitive factors in search. Considering the fact that users' search experiences are affected by their subjective feelings of time, rather than the objective time measured by timing devices, it is necessary to look into the different factors that have impacts on search users' perception of time. In this work, we make a first step towards revealing the time perception mechanism of search users with the following contributions: (1) We establish an experimental research framework to measure the subjective perception of time while reading documents in search scenario, which originates from but is also different from traditional time perception measurements in psychological studies. (2) With the framework, we show that while users are reading result documents, document relevance has small yet visible effect on search users' perception of time. By further examining the impact of other factors, we demonstrate that the effect on relevant documents can be also influenced by individuals and tasks. (3) We conduct a preliminary experiment in which the difference between perceived time and dwell time is taken into consideration in a search evaluation task. We found that the revised framework achieved a better correlation with users' satisfaction feedbacks. This work may help us better understand the time perception mechanism of search users and provide insights in how to better incorporate time factor in search evaluation studies.

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“…Typically, IR systems rely on a progressive satisfaction of the searchers' IN through an interactive and iterative process known as the relevance feedback cycle. Recently, affective and physiological features have been considered as a valid ground to define implicit feedback techniques [25,26]. Yashar asserted that previous works, however, have all investigated the phenomenon of IN indirectly, via some sort of mediator.…”

Section: Invited Talksmentioning

confidence: 99%

Report on the Information Retrieval Festival (IRFest2017)

et al. 2017

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The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017). OverviewOn the 7th of April, 2017, the 2nd SICSA Scottish Information Retrieval (IR) Workshop was held at the University of Glasgow. The workshop was co-organised by the University of Glasgow and the University of Strathclyde, and was funded by the Scottish Informatics and Computing Science Alliance (SISCA), Glasgow School of Computing and supported by ACM SIGIR.The aim of the workshop was to increase awareness and interaction between IR-related researchers working in, across, and with Scottish and UK universities.The workshop had a total of 75 attendees from across the Scottish universities and beyond. The scientific program included keynotes, industry talks, lightening academic talks and a poster/demo session as follows: 2 academic keynotes, 4 industrial talks and 12 invited academic talks from 7 different research institutions

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Understanding Relevance: An fMRI Study

Cited by 50 publications

References 24 publications

Predicting term-relevance from brain signals

Predicting term-relevance from brain signals

Does Document Relevance Affect the Searcher's Perception of Time?

Report on the Information Retrieval Festival (IRFest2017)

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