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Data, Code and Outputs for our manuscript "In your face: The biased judgement of fear-anger expressions in violent offenders."

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In your face: The biased judgment of fear-anger expressions in violent offenders.

Martin Wegrzyn, Sina Westphal & Johanna Kissler

Published: 12 May 2017
DOI: 10.1186/s40359-017-0186-z

Abstract

Why is it that certain violent criminals repeatedly find themselves engaged in brawls? Many inmates report having felt provoked or threatened by their victims, which might be due to a tendency to ascribe malicious intentions when faced with ambiguous social signals, termed hostile attribution bias.
The present study presented morphed fear-anger faces to prison inmates with a history of violent crimes, a history of child sexual abuse, and to matched controls form the general population. Participants performed a fear-anger decision task. Analyses compared both response frequencies and measures derived from psychophysical functions fitted to the data. In addition, a test to distinguish basic facial expressions and questionnaires for aggression, psychopathy and personality disorders were administered.
Violent offenders present with a reliable hostile attribution bias, in that they rate ambiguous fear-anger expressions as more angry, compared to both the control population and perpetrators of child sexual abuse. Psychometric functions show a lowered threshold to detect anger in violent offenders compared to the general population. This effect is especially pronounced for male faces, correlates with self-reported aggression and presents in absence of a general emotion recognition impairment.
The results indicate that a hostile attribution, related to individual level of aggression and pronounced for male faces, might be one mechanism mediating physical violence.

About

This is a repository containing the full data and code of our paper about facial expression recognition in violence offenders.

Table of Contents

Requirements

Data analysis was performed with Python 2.7 www.python.org using mainly numpy, scipy, pandas, scikit-learn, matplotlib, seaborn and jupyter.

To run all the scipts, you can create a virtual environment, by first installing virtualenv

pip install  virtualenv

Then you can create a virtual environment in the folder into which you cloned this repository

virtualenv venv

and then install all modules using pip

venv/bin/pip install -r requirements.txt

The main experiment was written and rendered with PsychoPy. The AFAS questionnaire was rendered as a html site using Flask for the back-end.

Contact

For questions or comments please write to martin.wegrzyn@uni-bielefeld.de