STEMorph: Morphed Emotional Face Stimuli

Abstract

Emotion recognition through facial expressions is crucial for interpreting social cues. However, it is often influenced by biases, i.e., systematic recognition advantages for particular emotions. Nevertheless, these biases are inconsistently reported across studies, likely due to methodological variations which underline the necessity for a standardized approach. Traditional face morphing methods can create unnatural-looking stimuli, and may confound the interpretation of emotions. To address this issue, we here introduce STEMorph, a validated stimulus set based on the NimStim set. We employed neutral-anchored morphing and neural-network-generated masks to reduce morphing artifacts and preserve the coherence of the depicted expressions. We validated our stimulus set by having participants rate each face on a 9-point scale ranging from angry to happy, assessing the perceived emotional intensity. STEMorphs validity was confirmed through linear mixed-effects modelling, showing a strong association between subjective ratings and intended morph level while accounting for other effects. Moreover, aligning with previous research highlighting gender as a key factor in emotion recognition, STEMorph also showed variation across participant-gender dimension. STEMorphs reliability was confirmed through a two-week follow-up rating session with a subgroup of the same participants. By introducing a controlled and empirically evaluated stimulus set of morphed emotional faces, STEMorph provides a useful resource for future investigations of facial emotion recognition.