Modeling automatic threat detection: development of a face-in-the-crowd task

An angry face is expected to be detected faster than a happy face because of an early, stimulus-driven analysis of threat-related properties. However, it is unclear to what extent results from the visual search approach-the face-in-the-crowd task-mirror this automatic analysis. The paper outlines a model of automatic threat detection that combines the assumption of a neuronal system for threat detection with contemporary theories of visual search. The model served as a guideline for the development of a new face-in-the-crowd task. The development involved three preliminary studies that provided a basis for the selection of angry and happy facial stimuli resembling each other in respect to perceptibility, homogeneity, and intensity. With these stimuli a signal detection version of the search task was designed and tested. For crowds composed of neutral faces, the sensitivity measure d' proved the expected detection advantage of angry faces compared to happy faces. However, the emotional expression made no difference if a neutral face had to be detected in crowd composed of either angry or happy faces. Results are in line with the assumption of a stimulus-driven shift of attention giving rise to the superior detection of angry target faces.