Gaze Pattern and Reading Comprehension

Vo, Tan Hung; Mendis, B. Sumudu U.; Gedeon, Tom

doi:10.1007/978-3-642-17534-3_16

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…That assumption is reasonably correct because a normal person can only keep seven pieces of information (±2) in their short-term memory [4]. We had some success in [6] in identifying such a process using gaze-tracking technique. For this experiment, however, we would like to validate the results with EEG.…”

Section: Generalmentioning

confidence: 99%

Reading Your Mind: EEG during Reading Task

Gedeon

2011

Neural Information Processing

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This paper demonstrates the ability to study the human reading behaviors with the use of Electroencephalography (EEG). This is a relatively new research direction because, obviously, gaze-tracking technologies are used specifically for those types of studies. We suspect, EEG, with the capability of recording brain-wave activities from the human scalp, in theory, could exhibit potential attributes to replace gaze-tracking in such research. To prove the concept, in this paper, we organized a BCI experiment and propose a model for effective classifying EEG data in comparison to the accuracy of gaze-tracking. The results show that by using EEG, we could achieve comparable results against the more established methods while demonstrating a potential live EEG applications. This paper also discusses certain points of consideration for using EEG in this work.

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Section: Generalmentioning

confidence: 99%

Reading Your Mind: EEG during Reading Task

Gedeon

2011

Neural Information Processing

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“…Request permissions from Permissions@acm.org. lengths of all saccades on the current AOI including both regression (i.e., back tracking) and skimming saccades (i.e., forward tracking) that can help a reader achieve a better understanding of the information [2,10]. If the dwell time on the current AOI is longer than a predefined Intra-AOI threshold (e.g., 1s), we record the reading speed over this period of dwell time, because a short dwell time often means these are spurious glances and not helpful for reading.…”

Section: Reading Speed On Each Aoimentioning

confidence: 99%

Gaze-Based Annotations for Reading Comprehension

Cheng

Sun

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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We study eye gaze movement behavior during paper reading and generate a series of annotations from a user's reading features: gray shading to indicate reading speed, borders to indicate frequency of re-reading, and lines to indicate transitions between sections of a document. Through a user study, we validate that our SocialReading system that shares teachers' gaze data for an academic paper can improve students' reading comprehension of that paper.

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“…Comprehension of the text can have significant effects on the eye movements observed [2,3]. Eye gaze patterns can be used to differentiate when individuals are reading different types of content [4]. In this application both support vector machines (SVM) and ANN were used to classify eye movement measures as either relevant or irrelevant text for answering a set of questions.…”

Section: Eye Movements During Readingmentioning

confidence: 99%

Fuzzy Output Error as the Performance Function for Training Artificial Neural Networks to Predict Reading Comprehension from Eye Gaze

Copeland

Gedeon

Mendis

2014

Lecture Notes in Computer Science

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Imbalanced data sets are common in real life and can have a negative effect on classifier performance. We propose using fuzzy output error (FOE) as an alternative performance function to mean square error (MSE) for training feed forward neural networks to overcome this problem. The imbalanced data sets we use are eye gaze data recorded from reading and answering a tutorial and quiz. The goal is to predict the quiz scores for each tutorial page. We show that the use of FOE as the performance function for training neural networks provides significantly better classification of eye movements to reading comprehension scores. A neural network with three hidden layers of neurons gave the best classification results especially when FOE was used as the performance function for training. In these cases, upwards of a 19% reduction in misclassification was achieved compared to using MSE as the performance function.

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Gaze Pattern and Reading Comprehension

Cited by 12 publications

References 5 publications

Reading Your Mind: EEG during Reading Task

Reading Your Mind: EEG during Reading Task

Gaze-Based Annotations for Reading Comprehension

Fuzzy Output Error as the Performance Function for Training Artificial Neural Networks to Predict Reading Comprehension from Eye Gaze

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