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Chi-Hsun Chang, Dan Nemrodov, Andy Lee, Adrian Nestor; Picturing Jonah Hill: memory-based image reconstruction of facial identity. Journal of Vision 2017;17(10):249. doi: 10.1167/17.10.249.
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© ARVO (1962-2015); The Authors (2016-present)
Our memory for human faces has been studied extensively, especially regarding the specific factors that influence face memorability. However, the detailed visual content of the representations underlying face memory remains largely unclear. Additionally, the relationship between face memory and face perception is not well understood given that these two aspects of face processing are typically investigated independently. Accordingly, the current work aimed to examine these issues by adopting an imaging reconstruction approach to estimate the visual appearance of face images from memory and perception-based behavioural data in neurologically healthy adults. Specifically, we used judgements of visual similarity between facial stimuli and between recollections of facial appearance retrieved from memory to construct a joint perceptual-memory face space. The structure of this hybrid representational space was then used to reconstruct the appearance of different facial identities corresponding to both unfamiliar and familiar faces. Specifically, memory-based reconstructions were carried out for newly-learned faces as well as for famous individuals (i.e., media personalities such as Jonah Hill). Our results demonstrated robust levels of memory and perception-based reconstruction performance for all face categories separately in every participant. In particular, we note the ability to reconstruct successfully images corresponding to famous individuals based on information retrieved from long-term memory. Thus, the current work provides a new approach to probing the representational content of face memory. Theoretically, it suggests that memory and perception share a common representational basis and, furthermore, from a translational standpoint, it can open a new path for concrete applications such as computer-based 'sketch artists'.
Meeting abstract presented at VSS 2017
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