Listeners are therefore not only required to encode how different people’s voices differ from each other they also need to represent how the different and variable instances of a single person’s voice belong to the same identity. The acoustic and perceptual properties of a individual’s voice can vary dramatically depending on the type of speech or vocalisations produced (shouting vs. While telling different voices apart is one important aspect of how we process voice identity information, the fact that individual voices are highly flexible is often neglected in these studies. These studies therefore focus on between-person variability, and on how listeners discriminate between different identities. Studies exploring prototype- or norm-based coding tend to conceptualise different voice identities as single points in the voice space. For example, some studies find evidence that distinctive voices are more reliably remembered 8, 10, while others find evidence for the opposite pattern, with recognition remaining more reliable after a delay for prototypical voices 8. 9) and an identity’s distance to the prototypical voice affects how well it is remembered and recognised. Within such voice spaces, the location of an individual voice relative to the prototype has perceptual implications: Voices that are further away from this between-person prototype are more perceptually distinctive (see refs. In such a view, the prototype is thought to be a representation of either an average voice, or a very frequently encountered voice 1, 2, 3, 4, 5 see refs. For voices, a prominent view proposes that different identities are encoded on a multidimensional voice space in relation to a prototype voice. The question of how we learn and represent person identity has long been debated. At test, listeners’ accuracy for old/new judgements was higher for stimuli located on an untrained distribution nested around the centre of each ring-shaped distribution compared to stimuli on the trained ring-shaped distribution. Listeners first learned to recognise these identities based on ring-shaped distributions located around the perimeter of within-person voice spaces – crucially, these distributions were missing their centres. We created 3 perceptually distinct voice identities, fully controlling their within-person variability. In two experiments, we show evidence that participants form abstracted representations of individual voice identities based on averages, despite having never been exposed to these averages during learning. While there is some evidence for norm-based coding when learning to discriminate different voices, little is known about how the representation of an individual's voice identity is formed through variable exposure to that voice. Models of voice perception propose that identities are encoded relative to an abstracted average or prototype.
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