Different rules for binocular combination of luminance flicker in cortical and subcortical pathways

Segala, Federico G.; Bruno, Aurelio; Aung, Myat T.; Wade, Alex; Baker, Daniel H.

doi:10.7554/elife.87048

“…The final study, by Segala et al (2023), investigated binocular combination of light signals and involved analysis of several diverse data types. These included EEG data, pupillometry data, and psychophysical data, spread across four experiments, and all analysed using R. The raw data for the entire study required around 48GB of storage space, and took many hours to analyse from scratch.…”

Section: Notes On Individual Papersmentioning

confidence: 99%

ReproduceMe: lessons from a pilot project on computational reproducibility

Baker,

Berg,

Hansford

et al. 2023

Preprint

0

View full text Add to dashboard Cite

If a scientific paper is computationally reproducible, the analyses it reports can be repeated independently by others. At the present time most papers are not reproducible. However, the tools to enable computational reproducibility are now widely available, using free and open source software. We conducted a pilot study in which we offered ‘reproducibility as a service’ within a UK psychology department for a period of 6 months. Our rationale was that most researchers lack either the time or expertise to make their own work reproducible, but might be willing to allow this to be done by an independent team. Ten papers were converted into reproducible format using R markdown, such that all analyses were conducted by a single script that could download raw data from online platforms as required, generate figures, and produce a pdf of the final manuscript. For some studies this involved reproducing analyses originally conducted using commercial software. The project was an overall success, with strong support from the contributing authors who saw clear benefit from this work, including greater transparency and openness, and ease of use for the reader. Here we describe our framework for reproducibility, summarise the specific lessons learned during the project, and discuss the future of computational reproducibility. Our view is that computationally reproducible manuscripts embody many of the core principles of open science, and should become the default format for scientific communication.

show abstract

“…Traditional book and voice information are being gradually displaced by images and videos. With the fast-paced advancements in display technology, three-dimensional (3D) stereoscopic displays are increasingly being utilized within the video game entertainment, visual research, remote device operation, medical imaging, virtual reality (VR), augmented reality (AR) and many other fields 1,2 . Stereoscopic display technology is founded upon the stereoscopic vision of the human eye 3 .…”

Section: Introductionmentioning

confidence: 99%

Human binocular color fusion model based on BP Neural Networks prediction

Zhu

2024

AETR

0

View full text Add to dashboard Cite

Stereoscopic display vision is significantly impacted by the color distortion of left and right eye images. When the human eye receives a specific range of dissimilar color information separately, the visual system combines them into a single color through binocular color fusion. In this study, we present experimental findings which compare the accuracy of a common binocular color-fusion model that was trained utilizing both linear fitting and back-propagation neural networks. Patient binocular color contrast test data was collected by eye care professionals working in private eye clinics. The results indicated that the back-propagation neural network produced RMSE errors of 0.9819 and 0.9662 for predicting binocular contrast, which were superior to the linear fitting method with errors of approximately 0.5. The BP neural network algorithm employed demonstrates predictive capabilities and lessens the occurrence of color redundancy. This reduction in redundancy holds the potential to decrease expenses associated with stereo imaging in future applications.

show abstract

ReproduceMe: Lessons from a pilot project on computational reproducibility

Baker,

Berg,

Hansford

et al. 2024

MP

0

View full text Add to dashboard Cite

If a scientific paper is computationally reproducible, the analyses it reports can be repeated independently by others. At the present time most papers are not reproducible. However, the tools to enable computational reproducibility are now widely available, using free and open source software. We conducted a pilot study in which we offered ‘reproducibility as a service’ within a UK psychology department for a period of 6 months. Our rationale was that most researchers lack either the time or expertise to make their own work reproducible, but might be willing to allow this to be done by an independent team. Ten papers were converted into reproducible format using R markdown, such that all analyses were conducted by a single script that could download raw data from online platforms as required, generate figures, and produce a pdf of the final manuscript. For some studies this involved reproducing analyses originally conducted using commercial software. The project was an overall success, with strong support from the contributing authors who saw clear benefit from this work, including greater transparency and openness, and ease of use for the reader. Here we describe our framework for reproducibility, summarise the specific lessons learned during the project, and discuss the future of computational reproducibility. Our view is that computationally reproducible manuscripts embody many of the core principles of open science, and should become the default format for scientific communication.

show abstract

Different rules for binocular combination of luminance flicker in cortical and subcortical pathways

Cited by 3 publications

References 58 publications

ReproduceMe: lessons from a pilot project on computational reproducibility

ReproduceMe: lessons from a pilot project on computational reproducibility

Human binocular color fusion model based on BP Neural Networks prediction

ReproduceMe: Lessons from a pilot project on computational reproducibility

Contact Info

Product

Resources

About