Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Biologically Inspired Dynamic Textures for Probing Motion Perception
Authors: Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we apply these textures in order to psychophysically probe speed perception in humans. |
| Researcher Affiliation | Academia | Jonathan Vacher CNRS UNIC and Ceremade Univ. Paris-Dauphine 75775 Paris Cedex 16, FRANCE EMAIL Andrew Isaac Meso Institut de Neurosciences de la Timone UMR 7289 CNRS/Aix-Marseille Universit e 13385 Marseille Cedex 05, FRANCE EMAIL Laurent Perrinet Institut de Neurosciences de la Timone UMR 7289 CNRS/Aix-Marseille Universit e 13385 Marseille Cedex 05, FRANCE EMAIL Gabriel Peyr e CNRS and Ceremade Univ. Paris-Dauphine 75775 Paris Cedex 16, FRANCE EMAIL |
| Pseudocode | No | The paper mentions a 'synthesis algorithm' and 'AR recurrence' but does not include any structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | Yes | The code associated to this work is available at https://jonathanvacher.github.io. |
| Open Datasets | No | The paper describes generating its own stimuli and collecting data from three observers for a psychophysical study. It does not mention using or providing access to a publicly available or open dataset for training/testing. |
| Dataset Splits | No | The paper describes a psychophysical experiment with human observers, detailing how trials were structured. It does not provide explicit training/validation/test dataset splits in the context of machine learning model training. |
| Hardware Specification | Yes | Stimuli were generated on a Mac running OS 10.6.8 and displayed on a 20 Viewsonic p227f monitor with resolution 1024 768 at 100 Hz. |
| Software Dependencies | Yes | Routines were written using Matlab 7.10.0 and Psychtoolbox 3.0.9 controlled the stimulus display. |
| Experiment Setup | Yes | The other parameters are set to the following values σV = 1 t z, θ0 = π 2 , σΘ = π 12, and d Z = 1.0 c/ . Note that σV is thus dependent of the value of z (that is computed from m Z and d Z, see Remark 2 and the supplementary ) to ensure that t = 1 σV z stays constant. |