The Cortical Activity of Graded Relevance

Pinkosova, Zuzana; McGeown, William J.; Moshfeghi, Yashar

doi:10.1145/3397271.3401106

Cited by 21 publications

(29 citation statements)

References 57 publications

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“…Our results show that brain responses can be reliably associated with self-reported preferences and that preferences can be predicted from EEG/ERP responses in a single-trial setting. Our results are also in line with previous findings that the evoked ERPs follow a pattern according to the graded level of the self-reported preferences [50].…”

Section: Discussion and Conclusion 61 Summary Of Contributionssupporting

confidence: 93%

“…Secondly, EEG systems provide more sensitive measurements for inferring preferences as they have been shown to predict graded relevance of stimuli as opposed to mere binary relevance. For example, while early research used simple term detection [21], later studies found ERPs can likewise indicate multiple relevance levels [50] and a deeper sense of topical relevance [28,29]. Such information can also be used beyond simple tagging of relevant material or annotation towards recommendation systems, such as material matching the underlying interest of a user who is reading encyclopedic texts [20].…”

Section: Brain Imaging For Information Retrievalmentioning

confidence: 99%

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Collaborative Filtering with Preferences Inferred from Brain Signals

Davis

Spapé

Ruotsalo

2021

Proceedings of the Web Conference 2021

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Section: Discussion and Conclusion 61 Summary Of Contributionssupporting

confidence: 93%

Section: Brain Imaging For Information Retrievalmentioning

confidence: 99%

Collaborative Filtering with Preferences Inferred from Brain Signals

Davis

Spapé

Ruotsalo

2021

Proceedings of the Web Conference 2021

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“…And they admit that they are right-handed and do not suffer from any neurological disease. The number of participants are similar to previous user studies (e.g., 23 participants in [47] and 20 participants in [1]). It takes about two hours to complete the whole task for each participant, including 40 minutes for preparation, 50 minutes for the main task, and 15 minutes for the questionnaire procedure.…”

Section: Participantssupporting

confidence: 63%

“…In IR scenario, Eugster et al [10] show relevant words would elicit higher P600 amplitudes. Pinkosova et al [47] indicate that the link between higher relevance and P600 amplitude might come from discourse memory in the brain. In our study, sentences have no problem at the syntactic level after we check manually.…”

Section: Erp Componentsmentioning

confidence: 99%

Understanding Human Reading Comprehension with Brain Signals

Ye,

Xie,

Liu

et al. 2021

Preprint

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Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the reading patterns and attention allocation mechanisms in the reading process. However, little is known about what happens in human brain during reading comprehension and how we can utilize this information as implicit feedback to facilitate information acquisition performance. With the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect highprecision brain signals in almost real time.With neuroimaging techniques, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of contents, i.e., contents that can satisfy users' information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, at the micro-time scale during reading comprehension underpin these neural responses. Inspired by these detectable differences in cognitive activities, we construct supervised learning models based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve their performance with brain signals. These findings imply that brain signals are valuable feedback for enhancing human-computer interactions during reading comprehension.

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“…Apart from the use of fMRI, other studies have applied different brain measurement techniques to investigate the concept of IR, especially relevance. For example, EEG has been applied in several studies to investigate the concept of relevance to text information (Gwizdka, Hosseini, Cole, & Wang, 2017; Eugster et al, 2014; Allegretti et al, 2015; Pinkosova, McGeown, & Moshfeghi, 2020). The related technique of Magnetoencephalography (MEG) has also been used to study the relevance of visual information (Kauppi et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Neural Correlates of Realisation of Satisfaction in a Successful Search Process

Paisalnan

Moshfeghi

Pollick

2021

Proceedings of the Association for Information Science and Tech

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In a search process, searchers review documents to gather information relevant to their information need (IN). During this process, searchers may experience the satisfaction of their IN, indicating they have gathered adequate relevant information to answer their need. This complex concept of satisfaction is the ultimate goal of search systems. Most studies in Information Retrieval (IR) have been attempted to understand how searchers' needs are satisfied based on behavioural observation. However, the psychophysiological manifestation during the moment of satisfaction still remains unclear. Here, we use functional Magnetic Resonance (fMRI) to investigate which brain regions are involved during the moment of satisfaction. Twenty-six participants participated in the experiment, designed to represent a search process while being scanned. Our result shows the human brain regions involved during the moment of satisfaction. These findings provide an important step in unravelling the concept of satisfaction in a search process.

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The Cortical Activity of Graded Relevance

Cited by 21 publications

References 57 publications

Collaborative Filtering with Preferences Inferred from Brain Signals

Collaborative Filtering with Preferences Inferred from Brain Signals

Understanding Human Reading Comprehension with Brain Signals

Neural Correlates of Realisation of Satisfaction in a Successful Search Process

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