dcnieho/humanFixationClassification: V1.1.1

This repository contains the coder settings and event-based agreement score algorithms used and developed for the article Hooge, I.T.C., Niehorster, D.C., Nyström, M., Andersson, R. & Hessels, R.S. (2017). Is human classification by experienced untrained observers a gold standard in fixation detection?  https://doi.org/10.3758/s13428-017-0955-x

When using the algorithms or coder settings in this repository in your work, please cite the Hooge et al. paper.

This repository also contains the eye-movement data used as stimuli in the Hooge et al. paper. When using the infant data files, please cite Hessels, R.S., Hooge, I.T.C., & Kemner, C. (2016). An in-depth look at saccadic search in infancy. Journal of Vision, 16(8), 10. http://doi.org/10.1167/16.8.10.
When using the adult data files, please cite Hooge, I.T.C., Niehorster, D.C., Nyström, M., Andersson, R. & Hessels, R.S. (2017). Is human classification by experienced untrained observers a gold standard in fixation detection? https://doi.org/10.3758/s13428-017-0955-x

For more information or questions, e-mail: i.hooge@uu.nl / dcnieho@gmail.com. The latest version of this repository is available from www.github.com/dcnieho/humanFixationClassification

The algorithms in this repository are licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. The coder settings and eye-movement data used as stimuli are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC NC-BY-SA 4.0) license.

Contents:

• To explore the event based F1 and relative timing offset and difference scores, run the files F1_example.m and RTO_RTD_example.m, respectively.
• To reproduce the F1, RTO and RTD scores from Hooge et al. (2017), run doF1_RTO_RTD_forAll.m
• To graphically explore the settings coders made, run coderSettingPlotter.m
• The coder settings are found in data/coderSettings
• The eye-movement data that were coded are found in data/ETdata. Data were recorded on a 1920x1080 screen that measured 50.9 x 28.6 cm. The viewing distance was approximately 65 cm.

Tested on MATLAB R2012a, R2017a & R2019b

Version History
N.B.: complete details of changes made is available on github: https://github.com/dcnieho/humanFixationClassification
Version 1.0 - changes from 0.9.2

• made F1 score calculation scripts faster and more robust
• ensured correct fixation durations appear in anonymized coding setting files

Version 1.1 - changes from 1.0

• corrected time shifts found in the onset and offsets times of the classified fixations for 14 out of the 70 stimulus files
• updated y-axis labels in coderSettingPlotter.m: deg -> pix
• updated coderSettingPlotter.m to step through data trial by trial instead of per coder per trial
• updated coderSettingPlotter.m to enable viewing a specific coder's settings by clicking on that coder's settings in the scarfplot

Data disclaimer, limitations and conditions of release
By downloading this data set, you expressly agree to the following conditions of release and acknowledge the following disclaimers issued by the authors:

A. Conditions of Release
Data are available by permission of the authors. Use of data in publications, either digital or hardcopy, must be cited as follows: Hooge, I.T.C., Niehorster, D.C., Nyström, M., Andersson, R. & Hessels, R.S. (2017). Is human classification by experienced untrained observers a gold standard in fixation detection? https://doi.org/10.3758/s13428-017-0955-x. When using the infant data files, cite Hessels, R.S., Hooge, I.T.C., & Kemner, C. (2016). An in-depth look at saccadic search in infancy. Journal of Vision, 16(8), 10. http://doi.org/10.1167/16.8.10. When using the adult data files, cite Hooge, I.T.C., Niehorster, D.C., Nyström, M., Andersson, R. & Hessels, R.S. (2017). Is human classification by experienced untrained observers a gold standard in fixation detection? https://doi.org/10.3758/s13428-017-0955-x.

B. Disclaimer of Liability
The authors shall not be held liable for any improper or incorrect use or application of the data provided, and assume no responsibility for the use or application of the data or interpretations based on the data, or information derived from interpretation of the data. In no event shall the authors be liable for any direct, indirect or incidental damage, injury, loss, harm, illness or other damage or injury arising from the release, use or application of these data. This disclaimer of liability applies to any direct, indirect, incidental, exemplary, special or consequential damages or injury, even if advised of the possibility of such damage or injury, including but not limited to those caused by any failure of performance, error, omission, defect, delay in operation or transmission, computer virus, alteration, use, application, analysis or interpretation of data.

C. Disclaimer of Accuracy of Data
No warranty, expressed or implied, is made regarding the accuracy, adequacy, completeness, reliability or usefulness of any data provided. These data are provided "as is." All warranties of any kind, expressed or implied, including but not limited to fitness for a particular use, freedom from computer viruses, the quality, accuracy or completeness of data or information, and that the use of such data or information will not infringe any patent, intellectual property or proprietary rights of any party, are disclaimed. The user expressly acknowledges that the data may contain some nonconformities, omissions, defects, or errors. The authors do not warrant that the data will meet the user’s needs or expectations, or that all nonconformities, omissions, defects, or errors can or will be corrected. The authors are not inviting reliance on these data, and the user should always verify actual data.