Good Face Recognisers Can Learn Faces From Fragments
hubie writes:
Psychologists at UNSW Sydney have challenged the prevailing view that people with exceptional face recognition abilities rely on processing faces holistically.
Instead, they argue, people who are great at learning and remembering new faces - also known as super recognisers - can divide new faces into parts, before storing them in the brain as composite images.
"It's been a long-held belief that to remember a face well you need to have a global impression of the face, basically by looking at the centre and seeing the face as a whole," said study lead author, Dr James Dunn.
"But our research shows that super-recognisers are still able to recognise faces better than others even when they can only see smaller regions at a time. This suggests that they can piece together an overall impression from smaller chunks, rather than from a holistic impression taken in a single glance."
[...] But according to Dr Dunn, the results don't mean that super-recognisers are necessarily doing anything differently than the rest of us.
"It seems that super-recognisers are not processing faces in a qualitatively different way from everyone else," Dr Dunn said. "They are doing similar things to normal people, but they are doing some important things more and this leads to better accuracy."
[...] The researchers said their experiment changes the way we think about why some people are better than others at committing a face to memory.
"We think one of the things they're doing uniquely is exploring the face more to find information that is useful for remembering or recognising a person later. So when super-recognisers learn a face, it is more like putting together pieces in a jigsaw puzzle than taking a single snapshot of the whole face."
If you want to find out whether you are a "super-recognizer," they have a test you can take (won't work on mobile screens).
Journal Reference:
James Daniel Dunn Victor Perrone de Lima Varela Victoria Ida Nicholls, et al., Face information sampling in super-recognizers, 2022. PsyArXiv preprint DOI: 10.31234/osf.io/z2k4a
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