In the early days of neuroimaging, brain function was investigated by averaging across voxels within a region, stimuli within a category, and individuals within a group. These three forms of averaging discard important neuroscientific information. Recent studies have explored analyses that combine the evidence in better-motivated ways. Multivariate pattern analyses enable researchers to reveal representations in distributed population codes, honouring the unique information contributed by different voxels (or neurons). Condition-rich designs more richly sample the stimulus space and can treat each stimulus as a unique entity. Finally, each individual's brain is unique and recent studies have found ways to model and analyse the inter-individual representational variability, and relate an individual’s brain representational geometries to his or her individual perceptual idiosyncrasies. Beyond basic science, the characterisation of individually unique brain function is likely to contribute to our understanding of disorders and of the continuous variation across patients. If functional brain imaging is to become useful in the diagnosis and monitoring of patients, we will need to develop a rich repertoire of methods for characterising subtle functional differences between individual brains and minds. In my talk, I will describe how combining neuroimaging, psychophysics and computational modelling can enable us to better understand the idiosyncratic nature of an individual’s internal world, with the example case of the Autism Spectrum Condition.