# Expression perceptive fields explain individual differences in the recognition of facial emotions
In this study, we use genetic algorithms to explore expression-space and model individuals' expression perceptive fields - probabilistic regions within multi-dimensional expression space, in which the perception of an emotion is likely to occur. We then use these perceptive fields to predict the emotions that individuals might infer from an expression, and compare these predictions to the emotion labels that participants assigned to expressions in a separate task.
This repository contains the data and code required to reproduce the results reported within the associated manuscript.
- **GA_venv.yml** - *conda environment file.*
- **finalEliteBlendshapes.csv** - *.csv file containing the GA data of the 'elite' expressions, from the PNAS study.*
- **CoreBlendshapeListWithFACS.csv** - *.csv file containing a list of 'core' blendshapes used within the main analyses*
- **perceptive_fields.ipynb** - *python notebook to run the main analyses*
- **GA_data.zip** - *compressed directory containing data from original GA task*
- **categorisation_data.zip** - *compressed directory containing data from categorisation task*
## Steps to reproduce:
1. Download files, making sure perceptive_fields.ipynb, finalEliteBlendshapes.csv, CoreBlendshapeListWithFACS.csv, GA_data.zip, and categorisation_data.zip are contained within the same directory
2. Unzip GA_data.zip and cateogisation_data.zip
3. Install anaconda (https://www.anaconda.com/)
4. Use .yml file to set up conda environment (https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file)
5. Open jupyter notebook
6. Run perceptive_fields.ipynb
## Our other work, using the GA toolkit:
Binetti, N., Roubtsova, N., Carlisi, C., Cosker, D., Viding, E., & Mareschal, I. (2022). Genetic algorithms reveal profound individual differences in emotion recognition. *Proceedings of the National Academy of Sciences, 119*(45), e2201380119.
Carlisi, C. O., Reed, K., Helmink, F. G., Lachlan, R., Cosker, D. P., Viding, E., & Mareschal, I. (2021). Using genetic algorithms to uncover individual differences in how humans represent facial emotion. *Royal Society open science, 8*(10), 202251.
Murray, T., Binetti, N., Carlisi, C., Namboodiri, V., Cosker, D., Viding, E., & Mareschal, I. (2024). Genetic algorithms reveal identity independent representation of emotional expressions. *Emotion, 24*(2), 495–505
Roubtsova, N., Parsons, M., Binetti, N., Mareschal, I., Viding, E., & Cosker, D. (2021). EmoGen: Quantifiable emotion generation and analysis for experimental psychology. *arXiv preprint arXiv:2107.00480.*
