Improving Search Engines via Large-Scale Physiological Sensing

White, Ryen W.; Ma, Ryan

doi:10.1145/3077136.3080669

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Previous work on measuring neurophysiology for user modeling has mainly focused on correlating physiological states to specific cognitive functions: emotional or affective responses [5], [39], [64], cognitive load [29], and relevance [3], [8], [18], [30]. The computer science community has been focusing on understanding how information needs arise [47], [49] and how relevance or binary preferences are manifested in EEG [33], [63], MEG [36] and fMRI data [46].…”

Section: Estimating Preferences From Physiologymentioning

confidence: 99%

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Davis

Spapé

Ruotsalo

2023

IEEE Trans. Affective Comput.

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We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experiment where brain activity collected from 31 participants produced in response to viewing images is associated with their self-reported preferences. First, we show that brain responses present a graded response to preferences, and that brain responses alone can be used to train classifiers that reliably estimate preferences. Second, we show that brain responses reveal additional preference information that correlates with group preference, even when participants self-reported having no such preference. Our analysis of brain responses carries significant implications for researchers in general, as it suggests an individual's explicit preferences are not always aligned with the preferences inferred from their brain responses. These findings call into question the reliability of explicit and behavioral signals. They also imply that additional, multimodal sources of information may be necessary to infer reliable preference information.

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Section: Estimating Preferences From Physiologymentioning

confidence: 99%

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Davis

Spapé

Ruotsalo

2023

IEEE Trans. Affective Comput.

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Towards Defining a Quality-Metric for Affective Feedback in an Intelligent Environment

Saha

Martin

Knapp

2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Efficient User Guidance for Validating Participatory Sensing Data

Cong

Tâm

Yin

et al. 2019

ACM Trans. Intell. Syst. Technol.

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Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely Generalised Auto Regressive Conditional Heteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.

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Improving Search Engines via Large-Scale Physiological Sensing

Cited by 5 publications

References 17 publications

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Towards Defining a Quality-Metric for Affective Feedback in an Intelligent Environment

Efficient User Guidance for Validating Participatory Sensing Data

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