Reading detection in real-time

Kelton, Conor; Wei, Zijun; Ahn, Seoyoung; Balasubramanian, Aruna; Das, Samir R.; Samaras, Dimitris; Zelinsky, Gregory J.

doi:10.1145/3314111.3319916

Cited by 19 publications

(18 citation statements)

References 10 publications

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“…Furthermore, Biedert et al ( 2012 ) developed a real-time classifier able to distinguish reading from skimming patterns. In a related study, Kelton et al ( 2019 ) investigated the influence of different content and tasks on the performance to determine whether subjects are reading or skimming a news article. Other neuroimaging methods such as fMRI have been combined with eye-tracking to examine the neural basis of sentence comprehension (e.g., Bonhage et al, 2015 ) or the discrimination between normal and non-word text (Choi et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

Hollenstein

Tröndle²,

Płomecka³

et al. 2023

Front. Psychol.

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Section: Discussionmentioning

confidence: 99%

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

Hollenstein

Tröndle²,

Płomecka³

et al. 2023

Front. Psychol.

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show abstract

“…Furthermore, Biedert et al (2012) developed a real-time classifier able to distinguish reading from skimming patterns. In a related study, Kelton et al (2019) investigated the influence of different content and tasks on the performance to determine whether subjects are reading or skimming a news article. Other neuroimaging methods such as fMRI have been combined with eye-tracking to examine the neural basis of sentence comprehension (e.g., Bonhage et al, 2015) or the discrimination between normal and non-word text (Choi et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

Hollenstein¹,

Tröndle

Płomecka

et al. 2022

Preprint

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We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.HighlightsWe present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research.We provide an interface to evaluate new approaches with an accompanying public leaderboard.The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading.The data is based on the Zurich Cognitive Language Processing Corpus of simultaneous eye-tracking and EEG signals from natural reading.

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“…A user's expectation of application quality is also shaped by the quality of recent web/video sessions [29,23], which has been studied under the framework of cognitive biases. Similarly, models of human visual attention are increasingly used in 360°videos [36,35] and recently in web optimization [31,38,13]. In short, we posit that using the concepts of expectation and attention, high QoE can be interpreted as having less violation of expectation within the region of attention.…”

Section: Designing a Visual Rendering-based Qoe Functionmentioning

confidence: 96%

“…In particular, WebGaze tracks gaze while a web loading process is replayed, which is similar to a visual rendering. Some follow-up work automatically derives user gaze from web content (e.g., [31,38]), and similar techniques are also used to track user saliency in panoramic videos (e.g., [35,50]). However, these efforts use gaze or saliency as another feature in traditional QoE models (e.g., to reweight web objects or video pixels/chunks).…”

Section: Similar Concepts In Prior Workmentioning

confidence: 99%

A New Abstraction for Internet QoE Optimization

Jiang,

Sen

2020

Preprint

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A perennial quest in networking research is how to achieve higher quality of experience (QoE) for users without incurring more resources. This work revisits an important yet often overlooked piece of the puzzle: what should the QoE abstraction be? A QoE abstraction is a representation of application quality that describes how decisions affect QoE.The conventional wisdom has relied on developing handcrafted quality metrics (e.g., video rebuffering events, web page loading time) that are specialized to each application, content, and setting. We argue that in many cases, it maybe fundamentally hard to capture a user's perception of quality using a list of handcrafted metrics, and that expanding the metric list may lead to unnecessary complexity in the QoE model without a commensurate gain. Instead, we advocate for a new approach based on a new QoE abstraction called visual rendering. Rather than a list of metrics, we model the process of quality perception as a user watching a continuous "video" (visual rendering) of all the pixels on their screen. The key advantage of visual rendering is that it captures the full experience of a user with the same abstraction for all applications. This new abstraction opens new opportunities (e.g., the possibility of end-to-end deep learning models that infer QoE directly from a visual rendering) but it also gives rise to new research challenges (e.g., how to emulate the effect on visual rendering of an application decision). This paper makes the case for visual rendering as a unifying abstraction for Internet QoE and outlines a new research agenda to unleash its opportunities.

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Reading detection in real-time

Cited by 19 publications

References 10 publications

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

The ZuCo Benchmark on Cross-Subject Reading Task Classification with EEG and Eye-Tracking Data

A New Abstraction for Internet QoE Optimization

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