Bayesian inference using a norm based face coding scheme in humans (360G-Wellcome-094792_Z_10_Z)

£259,443

Recently large amounts of evidence have accrued suggesting that the human brain often supports optimal Bayesian inference, but it has not been shown that this is so in face recognition. In parallel a leading hypothesis for the encoding of faces is the so-called norm coding scheme, whereby each face is defined by its position in face-space. In face-space the average face sits at the origin, and, in a Bayesian interpretation of inference in this context, the peak of the prior probability distribution should correspond to this rather than another face. By using a multivariate analysis, sensitive to the fine structure of fMRI responses, we hope to characterise these implicit probability distributions inthe aIT (anterior inferotemporal cortex) of human subjects, and show that whensubjects hallucinate a face in noisy stimuli this is the average face ratherthan another. This supports a Bayesian interpretation of the norm based scheme, since otherwise any face might be hallucinated . Finally, we aim to show that while face and anti-face (its opposite in face space) can be differentiated in aIT (the face identifier), this cannot be donein the fusiform face area (the face detector).

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Grant Details

Amount Awarded 259443
Applicant Surname O'Keeffe
Approval Committee Neurosciences And Mental Health
Award Date 2010-09-21T00:00:00+00:00
Financial Year 2009/10
Grant Programme: Title PhD Training Fellowship for Clinicians
Internal ID 094792/Z/10/Z
Lead Applicant Dr Jonathan O'Keeffe
Partnership Value 259443
Planned Dates: End Date 2014-09-30T00:00:00+00:00
Planned Dates: Start Date 2010-10-01T00:00:00+00:00
Recipient Org: Country United Kingdom
Region East of England
Sponsor(s) Prof David Lomas, Prof Krishna Chatterjee