https://github.com/computational-psychology/ecvp2025-mlds-mlcm-tutorial
Tutorial ECVP 2025: MLDS and MLCM: two scaling methods to study stimulus appearance
https://github.com/computational-psychology/ecvp2025-mlds-mlcm-tutorial
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Tutorial ECVP 2025: MLDS and MLCM: two scaling methods to study stimulus appearance
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- Owner: computational-psychology
- License: gpl-3.0
- Language: HTML
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Metadata Files
README.md
MLDS and MLCM: two scaling methods to study stimulus appearance
Guillermo Aguilar
Tutorial at the ECVP 2025 (Sun 24. August 2025, 13:30-16:00)
Abstract
Maximum Likelihood Difference Scaling (MLDS) and Maximum Likelihood Conjoint Measurement (MLCM) are two methods used to estimate perceptual scales (Knoblauch & Maloney, 2012). These scales reflect stimulus appearance and characterize the mapping of stimulus dimensions to a perceptual dimension of interest. They can also serve as a basis for comparing computational models of the visual system. In this hands-on tutorial, you will learn how to design a typical MLDS/MLCM experiment and estimate scales using the collected data (in the R programming language). We will also cover the underlying assumptions of the method, how and when these assumptions can be experimentally tested, and provide general recommendations to avoid common pitfalls encountered in practice.
Knowledge on R programming is not strictly required, but attendees should have basic programming skills.
Material used in this workshop
Slides
Online experiments
R notebooks
You can open and edit the notebook files to run the analysis locally.
- tutorial_mlds.Rmd for MLDS analysis
- tutorial_mlcm.Rmd for MLCM analysis
Alternatively you can see the rendered versions of these notebooks, run with the default data:
References
- Knoblauch & Maloney (2012). Modeling Psychophysical Data in R. Chapters 7 and 8.
- Maloney & Yang (2003). Maximum likelihood difference scaling. JoV.
- Ho et al. (2008). Conjoint Measurement of Gloss and Surface Texture. Psych. Science.
- Aguilar & Maertens (2020). Toward reliable measurements of perceptual scales in multiple contexts. JoV.
- Vincent, Maertens & Aguilar (2024). What Fechner could not do: Separating perceptual encoding and decoding with difference scaling. JoV.
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- Name: computational-psychology
- Login: computational-psychology
- Kind: organization
- Location: Berlin
- Website: http://www.psyco.tu-berlin.de
- Repositories: 26
- Profile: https://github.com/computational-psychology
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