An R toolkit for quality assessment of reverse correlation (RC)
classification images. It bundles per-face-region infoval() analyses
and cross-producer agreement maps across the face (both descriptive
and FWER-controlled inferential tests) into one workflow, for 2IFC
(Dotsch, 2016) and Brief-RC (Schmitz, Rougier & Yzerbyt, 2024)
designs.
Package is not on CRAN, distribution is GitHub-only.
User guide · Installation · Showcase · Citation
From GitHub:
# install.packages("remotes")
remotes::install_github("olivethree/rcisignal")If you ran a 2IFC study, you will also need rcicr (used to compute
the individual CIs):
remotes::install_github("rdotsch/rcicr")Brief-RC users can skip rcicr; the Brief-RC code is fully native to
rcisignal.
Re-install before each fresh analysis.
rcisignalis in an experimental stage and exported functions are still being refined. Re-runningremotes::install_github("olivethree/rcisignal")at the start of an analysis session pulls the latest version; the user guide is kept in sync with new and updated functions.
A re-analysis of the open data from Oliveira, Garcia-Marques, Dotsch & Garcia-Marques (2019), 2IFC reverse correlation on a 256 x 256 grayscale male base face, 20 producers per trait, 300 trials each. Two contrasts are shown: Trustworthy vs Friendly and Dominant vs Competent.
| Trustworthy − Friendly | Dominant − Competent |
 |
 |
| Trustworthy − Friendly | Dominant − Competent |
 |
 |
Per (trait, region) cell. Values are the median producer z-score
and (in parentheses) the number of producers (out of 20) clearing
z >= 1.96, using face-region masks and a
trial-count-matched reference distribution.
| Trait | Full face | Upper face | Eyes | Mouth |
|---|---|---|---|---|
| Trustworthy | +0.50 (3/20) | +0.30 (2/20) | +0.50 (1/20) | +0.53 (3/20) |
| Friendly | +0.97 (5/20) | +0.23 (2/20) | +0.34 (2/20) | +0.75 (4/20) |
| Competent | +0.70 (3/20) | +0.21 (3/20) | +0.36 (2/20) | +0.25 (5/20) |
| Dominant | +0.89 (6/20) | +0.37 (5/20) | +0.91 (3/20) | +0.38 (2/20) |
The user guide walks through every exported function, the interpretation of these maps and the per-region grid, and the full Worked example: Oliveira et al. (2019), Study 1 section.
Several metrics in this package are package-level extensions whose behaviour on social-face RC data has not been independently validated. See §1.2 Validation status in the user guide for the breakdown of validated versus unvalidated metrics, and treat the unvalidated ones as exploratory in published work.
If rcisignal helps your research, please cite it:
Oliveira, M. (2026). rcisignal: Quality checks for reverse-correlation
data and classification images (Version 0.1.1) [R package]. Zenodo.
https://doi.org/10.5281/zenodo.19961180
Run citation("rcisignal") in R for a BibTeX entry.
Please also cite the methodological sources appropriate to your pipeline:
- 2IFC: Dotsch (2016, 2023) for the
rcicrpackage; Brinkman et al. (2019) for infoVal. - Brief-RC: Schmitz, Rougier, and Yzerbyt (2024).
- Brinkman, L., Goffin, S., van de Schoot, R., van Haren, N. E. M., Dotsch, R., & Aarts, H. (2019). Quantifying the informational value of classification images. Behavior Research Methods, 51(5), 2059-2073. https://doi.org/10.3758/s13428-019-01232-2
- Dotsch, R. (2016, 2023). rcicr: Reverse-correlation image-classification toolbox [R package]. https://github.com/rdotsch/rcicr
- Oliveira, M., Garcia-Marques, T., Dotsch, R., & Garcia-Marques, L. (2019). Dominance and competence face to face: Dissociations obtained with a reverse correlation approach. European Journal of Social Psychology. https://doi.org/10.1002/ejsp.2569
- Schmitz, M., Rougier, M., & Yzerbyt, V. (2024). Introducing the brief reverse correlation: An improved tool to assess visual representations. European Journal of Social Psychology. https://doi.org/10.1002/ejsp.3100
Released under the MIT License.
Designed by Manuel Oliveira
Code and documentation were co-built with Claude (Opus 4.6, Anthropic; April-May 2026).
This package builds on the excellent foundational work by Ron Dotsch, Loek Brinkman, Alex Todorov, Mathias Schmitz, Marine Rougier, Vincent Yzerbyt, and their many collaborators across the years. Reverse correlation is no longer a central part of my research, but I still find a lot of enjoyment in working on these side projects. The inspiration to build these tools and tutorials comes mostly from occasional collaborations with my PhD supervisors (Teresa Garcia-Marques, Leonel Garcia-Marques) and all the warm and competent colleagues from the research groups in Lisbon (Goncalo Oliveira, Rui Costa-Lopes and their teams) with whom I have been greatly enjoying working together on this stuff. Hopefully this toolkit will come in handy to all the RC research enthusiasts out there! :)