scholarly journals A Simple(r) Tool For Examining Fixations

2016 ◽  
Vol 9 (4) ◽  
Author(s):  
Francesco Di Nocera ◽  
Claudio Capobianco ◽  
Simon Mastrangelo
Keyword(s):  
Eye Tracking ◽  
Open Source ◽  
Open Source Software ◽  
Time Course ◽  
Nearest Neighbor ◽  
Mental Workload ◽  
Short Paper ◽  
Tracking Data ◽  
Mental Load ◽  
Simple Tool

This short paper describes an update of A Simple Tool For Examining Fixations (ASTEF) developed for facilitating the examination of eye-tracking data and for computing a spatial statistics algorithm that has been validated as a measure of mental workload (namely, the Nearest Neighbor Index: NNI). The code is based on Matlab® 2013a and is currently distributed on the web as an open-source project. This implementation of ASTEF got rid of many functionalities included in the previous version that are not needed anymore considering the large availability of commercial and open-source software solutions for eye-tracking. That makes it very easy to compute the NNI on eye-tracking data without the hassle of learning complicated tools. The software also features an export function for creating the time series of the NNI values computed on each minute of the recording. This feature is crucial given that the spatial distribution of fixations must be used to test hypotheses about the time course of mental load.

scholarly journals Open-source Software for Determining the Dynamic Areas of Interest for Eye Tracking Data Analysis

2021 ◽  
Vol 192 ◽  
pp. 2568-2575
Author(s):  
Leszek Bonikowski ◽  
Dawid Gruszczyński ◽  
Jacek Matulewski
Keyword(s):  
Data Analysis ◽  
Eye Tracking ◽  
Open Source ◽  
Open Source Software ◽  
Tracking Data ◽  
Areas Of Interest

scholarly journals ETRAN: extensible software for eye tracking data visualization and analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 131-144
Author(s):  
P.A. Marmalyuk ◽  
G.A. Yuryev ◽  
A.V. Zhegallo ◽  
B.Yu. Polyakov ◽  
A.S. Panfilova
Keyword(s):  
Data Analysis ◽  
Eye Tracking ◽  
Open Source ◽  
Data Visualization ◽  
Open Source Software ◽  
Software System ◽  
Tracking Data ◽  
Functional Features ◽  
Events Detection ◽  
Extensible Software

This article is devoted to the description of a free, extensible and open source software system designed for eye tracking data analysis. Authors of this article examine not only the main methods and functions of the system core that address gaze data import, data analysis (filtering, smoothing, oculomotor events detection, estimation of events’ characteristics and others) and visualization, but also scheduled improvements of system’s functional features

scholarly journals The chemfp project

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew Dalke
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Similarity Search ◽  
High Performance ◽  
Business Models ◽  
Nearest Neighbor ◽  
Lessons Learned ◽  
Nearest Neighbor Search ◽  
Memory Bandwidth ◽  
Software Project

AbstractThe chemfp project has had four main goals: (1) promote the FPS format as a text-based exchange format for dense binary cheminformatics fingerprints, (2) develop a high-performance implementation of the BitBound algorithm that could be used as an effective baseline to benchmark new similarity search implementations, (3) experiment with funding a pure open source software project through commercial sales, and (4) publish the results and lessons learned as a guide for future implementors. The FPS format has had only minor success, though it did influence development of the FPB binary format, which is faster to load but more complex. Both are summarized. The chemfp benchmark and the no-cost/open source version of chemfp are proposed as a reference baseline to evaluate the effectiveness of other similarity search tools. They are used to evaluate the faster commercial version of chemfp, which can test 130 million 1024-bit fingerprint Tanimotos per second on a single core of a standard x86-64 server machine. When combined with the BitBound algorithm, a k = 1000 nearest-neighbor search of the 1.8 million 2048-bit Morgan fingerprints of ChEMBL 24 averages 27 ms/query. The same search of 970 million PubChem fingerprints averages 220 ms/query, making chemfp one of the fastest CPU-based similarity search implementations. Modern CPUs are fast enough that memory bandwidth and latency are now important factors. Single-threaded search uses most of the available memory bandwidth. Sorting the fingerprints by popcount improves memory coherency, which when combined with 4 OpenMP threads makes it possible to construct an N × N similarity matrix for 1 million fingerprints in about 30 min. These observations may affect the interpretation of previous publications which assumed that search was strongly CPU bound. The chemfp project funding came from selling a purely open-source software product. Several product business models were tried, but none proved sustainable. Some of the experiences are discussed, in order to contribute to the ongoing conversation on the role of open source software in cheminformatics.

scholarly journals iMap4D: an Open Source Toolbox for Statistical Fixation Mapping of Eye-Tracking Data in Virtual Reality

2019 ◽  
Vol 19 (10) ◽  
pp. 127c
Author(s):  
Valentina Ticcinelli ◽  
Peter De Lissa ◽  
Denis Lalanne ◽  
Sebastien Miellet ◽  
Roberto Caldara
Keyword(s):  
Virtual Reality ◽  
Eye Tracking ◽  
Open Source ◽  
Tracking Data

scholarly journals Exploring the Potential of Online Webcam-based Eye Tracking in Decision-Making Research and Influence Factors on Data Quality

2021 ◽  
Author(s):  
Tim Schneegans ◽  
Matthew D. Bachman ◽  
Scott A. Huettel ◽  
Hauke Heekeren
Keyword(s):  
Decision Making ◽  
Eye Tracking ◽  
Data Quality ◽  
Open Source ◽  
Influence Factors ◽  
Frame Rate ◽  
Quality Data ◽  
Tracking Data ◽  
Chin Rest ◽  
Negative Effect

Recent developments of open-source online eye-tracking algorithms suggests that they may be ready for use in online studies, thereby overcoming the limitations of in-lab eye-tracking studies. However, to date there have been limited tests of the efficacy of online eye-tracking for decision-making and cognitive psychology. In this online study, we explore the potential and the limitations of online eye-tracking tools for decision-making research using the webcam-based open-source library Webgazer (Papoutsaki et al., 2016). Our study had two aims. For our first aim we assessed different variables that might affect the quality of eye-tracking data. In our experiment (N = 210) we measured a within-subjects variable of adding a provisional chin rest and a between-subjects variable of corrected vs uncorrected vision. Contrary to our hypotheses, we found that the chin rest had a negative effect on data quality. In accordance with out hypotheses, we found lower quality data in participants who wore glasses. Other influence factors are discussed, such as the frame rate. For our second aim (N = 44) we attempted to replicate a decision-making paradigm where eye-tracking data was acquired using offline means (Amasino et al., 2019). We found some relations between choice behavior and eye-tracking measures, such as the last fixation and the distribution of gaze points at the moment right before the choice. However, several effects could not be reproduced, such as the overall distribution of gaze points or dynamic search strategies. Therefore, our hypotheses only find partial evidence. This study gives practical insights for the feasibility of online eye-tacking for decision making research as well as researchers from other disciplines.

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

2020 ◽  
Vol 11 (4) ◽  
pp. 27-45
Author(s):  
Mahugnon Olivier Avande ◽  
Robin A. Gandhi ◽  
Harvey Siy
Keyword(s):  
Eye Tracking ◽  
Open Source ◽  
Graphical Models ◽  
Open Source Software ◽  
User Engagement ◽  
Interaction Patterns ◽  
Eye Tracker ◽  
Future Studies ◽  
Case Study Design

License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.

Product Body Shapes, Not Features, Provide Fast and Frugal Cues for Environmental Friendliness

2016 ◽  
Author(s):  
Ping Du ◽  
Erin F. MacDonald
Keyword(s):  
Eye Tracking ◽  
Visual Cues ◽  
Body Shape ◽  
Visual Design ◽  
Tracking Data ◽  
Mental Load ◽  
Environmental Friendliness ◽  
Visual Areas ◽  
Body Shapes ◽  
Areas Of Interest

Mental associations between a product’s visual design and its unobservable characteristics aid consumer judgments. It is hypothesized these associations, or cues, allow people to decrease the mental load required to make a decision. This paper investigates the rapid-building of mental associations between visual cues and unobservable attributes. It questions if it is more effective to cue holistically, through body-shape, or by individual features. Subjects participated in an association-building task and were then surveyed for retention of positive and negative cues for environmental friendliness ratings. Results demonstrate retention of body shapes cues but not feature cues. Additionally, eye-tracking data demonstrate that people redistribute their attention to a product after the association-building task, increasing the percentage of attention in the cued visual areas-of-interest. This supports the hypothesis that cues work to distribute mental load more efficiently; subjects’ evaluations became more targeted when judging environmental friendliness.

scholarly journals Mental Workload Analysis Associated with Emotional Design in E-learning Contexts: Combining EEG and Eye-Tracking Data

2021 ◽  
Author(s):  
ChenNan Wu ◽  
Yang Liu ◽  
Xiang Guo ◽  
TianShui Zhu ◽  
WeiFeng Ma
Keyword(s):  
Online Learning ◽  
Eye Tracking ◽  
Mental Workload ◽  
Physiological Data ◽  
Learning Technology ◽  
Tracking Data ◽  
Workload Analysis ◽  
Learning Contexts ◽  
Multimodal Features ◽  
E Learning

In this study, we designed a mental workload induction experiment in the context of online learning, in which EEG and eye-tracking data of participants were synchronously recorded with the aim of investigating the association between different design principles and multimodal physiological features and then applying machine-learning technology to classify mental workload states induced by those principles. This paper systematically reviews three kinds of EEG and eye-tracking features used for mental workload classification, compares the accuracy of mental workload classification between single-modal and multimodal features, modifies the mental workload index proposed by Pope et al. to monitor the variation of mental workload in E-learning contexts, and reduces the dimensions of features for more convenient use in daily life. The results of the experiment demonstrate that (1) The classification ability of wavelet power features and eye-tracking features are better than that of entropy features in E-learning contexts; (2) Multimodal physiological data can significantly improve the accuracy of mental workload classification in E-learning contexts; and (3) Correlation-based feature selection (CFS) was employed to rank all features in descending order, and when the feature dimension is reduced to 30, the optimal average classification accuracy obtained by linear-SVM is 80.2%. Furthermore, the EEG frequency bands that are highly correlated with mental workload were analyzed, and the correlation between different brain areas and mental workload discussed. All these results lay the foundation for continuous monitoring of participants’ mental workload, making it possible to endow computers with the ability to understand mental workload in E-learning contexts, which will in turn remarkably enhance participants’ learning efficiency and performance during the pandemic, and in other circumstances necessitating online learning.

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