06/02/2026
A recent study published by researchers at the University of Pisa in Italy investigated whether horses are capable of discriminating human-like emotional facial expressions based on visual cues alone, using an android robot as a controlled stimulus.
The robot, FACE (Facial Automaton for Conveying Emotions), was programmed to display four facial expressions: neutral, happy, angry, and surprised and was then presented to a group of horses in the absence of any human presence, isolating them from other sensory modalities such as vocal, postural, or olfactory cues.
Physiological data were collected via heart rate variability (HRV), behavioural responses were observed and coded, and equine facial movements were analysed using the Equine Facial Action Coding System (EquiFACS). A machine learning approach was also applied to explore multivariate response patterns across conditions.
The results indicated that exposure to the robot produced measurable autonomic arousal across all conditions relative to baseline, suggesting that horses registered the presence of the stimulus.
However, no consistent differentiation was found between emotional expressions in terms of physiological or behavioural responses.
What this tells us is that horses rely on a combination of voice, posture, context, movement, smell, and visual facial cues in isolation are insufficient for horses to recognise the emotional valence of human-like expressions.
However, this does not mean that robots and technology have no value in the equine world. Since one of the persistent challenges in equine behavioural research is that the presence of a human experimenter itself often influences the horse's responses, using robotic or automated stimuli can reduce this confound, allowing researchers to study horses in conditions that are more reflective of their natural emotional baseline rather than their response to a specific person.
📑 Face to FACE® – Investigating horses' perception of facial expressions performed by an android robot — Baragli, Frassineti, Felici, Ricci-Bonot, Galotti, Sgorbini, Palagi, Cominelli, Scilingo, Scopa & Lanatà , University of Pisa, 2026.
