Research

Faces are mesmerising sources of social information; from facial movements we are able to recognise an incredible variety of signals, combining them in context to infer what someone is thinking or feeling. Yet our current understanding has been shaped by the use of posed images of emotional expressions, curated to represent discrete, universally-recognised categories such as 'happy', 'sad', and 'angry'.

My colleagues and I suggest this approach overly-simplifies the rich and complex variation in face behaviour we experience outside the lab - indeed, there may be more salient emotional information in non-posed naturalistic faces (Peluso et al., 2025, Emotion) which is perceived as more genuine (Long, Peluso et al., 2023, Nature Scientific Reports).

To further understand how real-world faces communicate social information, this project seeks to identify the key characteristics underlying our mental representations of both static and dynamic 'wild' facial expressions, by combining non-biased, data-driven behavioural tasks with theory-derived representational properties and computational models (such as representational similarity analysis and multidimensional scaling).

Together, we seek to develop more ecologically valid methods to bridge what we know about faces in the lab with how we perceive faces in the real-world.

When people experience emotion, arousal, or stress, the way they breathe changes. But does every person breathe the same way to begin with? While disorders of breathing in clinical populations have been the subject of exhaustive study there is good reason to explore how individual differences in healthy breathing patterns may be consequential for shaping how we respond both physiologically and behaviourally to physical and psychological challenges. This project seeks to examine individual differences in breathing patterns, in order to tease apart the relative contributions of volitional and autonomic control and better characterise respiration as a skilful behaviour.