These data describe a combined magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and behavioural study. With these data we investigated how brain responses (measured with MEG) in certain brain areas (measured with fMRI) matched up with participants' perception of form and movement in facial videos (measured behaviourally). Our results are published in the paper 'Low-frequency oscillations employ a general coding of the spatio-temporal similarity of dynamic faces' (see Related Resources). Although a person's facial identity is immutable, faces are dynamic and undergo complex movements which signal critical social cues (viewpoint, eye gaze, speech movements, expressions of emotion and pain). These movements can confuse automated systems, yet humans recognise moving faces robustly. Our objective is to discover the stimulus information, neural representations and computational mechanisms that the human brain uses when recognising social categories from moving faces. We will use human brain imaging to put an existing theory to the test. This theory proposes that recognition of changeable attributes (eg, expression) and facial identity are each recognised separately by two different brain pathways, each in a different part of the temporal lobe of the brain. The evidence we provide might indeed support and fill in many gaps in this theory. Nevertheless, we expect instead to instantiate a new alternative theory. By this new theory, some brain areas can recognise both identities and expressions, using unified representations, with one of the two pathways specialised for representing movement. Thus, the successful completion of our project will provide a new theoretical framework sufficient to motivate improved automated visual systems and advance new directions of research on human social perception.

Data were collected using functional magnetic resonance imaging, magnetoencephalography and behavioural psychophysics (computerised human performance measurement).