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

• the participant data
  • AFAS
  • PPI-R
  • SCID-II
  • basic expressions and morphed expressions
• the experiment
  • AFAS as online questionnaire
  • face recognition experiment
• the code
  • Analysis of Questionnaires
  • Analysis of basic expressions recognition
  • Importing morph data
  • Basic analyses of morph data
  • Curve Fitting/Psychophysics

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