Visual Data Cleansing of Low-Level Eye-Tracking Data

2017 ◽  
pp. 199-216
Author(s):  
Christoph Schulz ◽  
Michael Burch ◽  
Fabian Beck ◽  
Daniel Weiskopf
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Visual Data ◽  
Data Cleansing ◽  
Low Level

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Attentional Bias to Untrustworthy Faces: Evidence From Eye Tracking Data

2015 ◽  
Vol 23 (9) ◽  
pp. 1508
Author(s):  
Qiandong WANG ◽  
Qinggong LI ◽  
Kaikai CHEN ◽  
Genyue FU
Keyword(s):  
Eye Tracking ◽  
Attentional Bias ◽  
Tracking Data

scholarly journals Mining reading patterns from eye-tracking data: method and demonstration

2019 ◽  
Vol 19 (2) ◽  
pp. 345-369 ◽  
Author(s):  
Constantina Ioannou ◽  
Indira Nurdiani ◽  
Andrea Burattin ◽  
Barbara Weber
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Reading Patterns

scholarly journals Classifying Eye-Tracking Data Using Saliency Maps

2021 ◽  
Author(s):  
Shafin Rahman ◽  
Sejuti Rahman ◽  
Omar Shahid ◽  
Md. Tahmeed Abdullah ◽  
Jubair Ahmed Sourov
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Saliency Maps
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