scholarly journals A cross-linguistic investigation of proactive, similarity-based retrieval interference in sentence comprehension: No support from English, German and Russian eye-tracking data

2020 ◽  
Author(s):  
Daniela Mertzen ◽  
Anna Laurinavichyute ◽  
Brian Dillon ◽  
Ralf Engbert ◽  
Shravan Vasishth
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Large Scale ◽  
Sentence Comprehension ◽  
Tracking Data ◽  
Processing Demands ◽  
Retrieval Mechanism ◽  
Syntactic Dependencies ◽  
Retrieval Interference ◽  
Larger Sample

Cue-based parsing theories posit that dependency resolution during real-time sentence comprehension relies on cue-based retrieval of linguistic items encoded in memory. This retrieval mechanism is prone to similarity-based interference, which can occur when there are items in memory that are similar to the retrieval target. Interference during sentence comprehension seems to be well-established across numerous syntactic dependencies; however, the evidence for interference on within-sentence dependencies from sentence-external lexical items (encoded in memory prior to reading a target dependency) is inconclusive (Van Dyke &McElree, 2006; Van Dyke et al., 2014). The goal of the present study is to provide a large-scale cross-linguistic investigation of retrieval interference from sentence-external distractors under varying processing demands. Three larger-sample eye-tracking studies in English (N=66),German (N=122) and Russian (N=109) show no support for similarity-based interference from sentence-external material during sentence comprehension. We discuss the implications of our findings for cue-based parsing theories.

Comparison of a perfusion simulator to a clinical operating room: evaluation of eye tracking data and subjective perception. A pilot study

Perfusion ◽  
2020 ◽  
pp. 026765912097461
Author(s):  
Cynthia Pawelke ◽  
Frank Merkle ◽  
Dino Kurtovic ◽  
Sina Gierig ◽  
Gisela Müller-Plath
Keyword(s):  
Eye Tracking ◽  
Rating Scales ◽  
Objective Measure ◽  
Subjective Perception ◽  
Tracking Data ◽  
Third Phase ◽  
Heart Lung Machine ◽  
Heart Center ◽  
The Individual ◽  
Larger Sample

Background: With the aim of evaluating the perfusion simulator at the German Heart Center Berlin, similarity between simulation and clinical operation room (OR) was investigated regarding subjective perception and eye movement. Methods: Eight perfusionists performed an operation on the heart-lung machine (HLM) wearing eye tracking glasses, each in real OR and simulator. The three most important phases for perfusionists (going on bypass, cardioplegia administration and coming off bypass) were considered. Additional to eye tracking data as objective measure, questionnaires were completed, and interviews conducted afterwards. Results: The structure of simulator and real OR is perceived as basically the same. Yet there are differences in the HLM-models used and the temporal sequence. Different perception of both situations is reported in interviews and reflected in significant differences in the rating scales (NASA-TLX) on three of six subscales. In eye tracking data, certain AOIs could be identified for the individual phases, both in OR and simulator—an indication of fundamental similarity. However, differences regarding the proportions of the individual AOIs, especially in the first and third phase, are leading to the assumption that the simulator, and especially the simulation process, is only valid to a limited extent regarding subjective perception and eye tracking data. Conclusion: The use of the simulator for (advanced) training is accepted and explicitly requested by perfusionists. Yet further research is needed to identify the decisive factors (like simulation duration or additional tasks) for a valid execution in the simulator. Furthermore, a larger sample size should be regarded to allow statistical analysis.

USING EYE-TRACKING TO INVESTIGATE TOPICS IN L2 ACQUISITION AND L2 PROCESSING

2013 ◽  
Vol 35 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Leah Roberts ◽  
Anna Siyanova-Chanturia
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Language Processing ◽  
Target Language ◽  
Tracking Data ◽  
L2 Acquisition ◽  
Tracking Method ◽  
L2 Processing ◽  
Major Benefit ◽  
The Moment

Second language (L2) researchers are becoming more interested in both L2 learners’ knowledge of the target language and how that knowledge is put to use during real-time language processing. Researchers are therefore beginning to see the importance of combining traditional L2 research methods with those that capture the moment-by-moment interpretation of the target language, such as eye-tracking. The major benefit of the eye-tracking method is that it can tap into real-time (or online) comprehension processes during the uninterrupted processing of the input, and thus, the data can be compared to those elicited by other, more metalinguistic tasks to offer a broader picture of language acquisition and processing. In this article, we present an overview of the eye-tracking technique and illustrate the method with L2 studies that show how eye-tracking data can be used to (a) investigate language-related topics and (b) inform key debates in the fields of L2 acquisition and L2 processing.

scholarly journals Predicting Video Saliency Using Crowdsourced Mouse-Tracking Data

2019 ◽  
Author(s):  
Виталий Людвиченко ◽  
Vitaliy Lyudvichenko ◽  
Дмитрий Ватолин ◽  
Dmitriy Vatolin
Keyword(s):  
Eye Tracking ◽  
Large Scale ◽  
Peripheral Vision ◽  
Tracking Data ◽  
Eye Tracker ◽  
Mouse Tracking ◽  
Saliency Maps ◽  
Video Viewing ◽  
Computer Mouse ◽  
Neural Network Algorithm

This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers’ peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.

scholarly journals Peekbank: Exploring children's word recognition through an open, large-scale repository for developmental eye-tracking data

2021 ◽  
Author(s):  
Martin Zettersten ◽  
Claire Bergey ◽  
Naiti Sanjiv Bhatt ◽  
Veronica Boyce ◽  
Mika Braginsky ◽  
...  
Keyword(s):  
Word Recognition ◽  
Eye Tracking ◽  
Large Scale ◽  
Time Window ◽  
Vocabulary Development ◽  
Target Object ◽  
Tracking Data ◽  
Age Related ◽  
Item Level ◽  
Current Database

The ability to rapidly recognize words and link them to referents in context is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We present two analyses of the current database (N=1,320): (1) capturing age-related changes in infants' word recognition while generalizing across item-level variability and (2) assessing how a central methodological decision -- selecting the time window of analysis -- impacts the reliability of measurement. Future efforts will expand the scope of the current database to advance our understanding of participant-level and item-level variation in children's vocabulary development.

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

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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