Cavanagh, P. | Search bioRxiv

bioRxiv2018

Idiosyncratic, retinotopic bias in face identification modulated by familiarity

The perception of gender and age of unfamiliar faces is reported to vary idiosyncratically across retinal locations such that, for example, the same androgynous face may appear to be male at one location but female at another. Here we test spatial heterogeneity for the recognition of the identity of personally familiar faces in human participants. We found idiosyncratic biases that were stable within participants and that varied more across locations for low as compared to high familiar faces. These data suggest that like face gender and age, face identity is processed, in part, by independent populations of neurons monitoring restricted spatial regions and that the recognition responses vary for the same face across these different locations. Moreover, repeated and varied social interactions appear to lead to adjustments of these independent face recognition neurons so that the same familiar face is eventually more likely to elicit the same recognition response across widely separated visual field locations. We provide a mechanistic account of this reduced retinotopic bias based on computational simulations.\n\nSignificance statementIn this work we tested spatial heterogeneity for the recognition of personally familiar faces. We found retinotopic biases that varied more across locations for low as compared to highly familiar faces. The retinotopic biases were idiosyncratic and stable within participants. Our data suggest that, like face gender and age, face identity is processed by independent populations of neurons monitoring restricted spatial regions and that recognition may vary for the same face at these different locations. Unlike previous findings, our data and computational simulation address the effects of learning and show how increased familiarity modifies the representation of face identity in face-responsive cortical areas. This new perspective has broader implications for understanding how learning optimizes visual processes for socially salient stimuli.

neuroscience↗

bioRxiv2018

A laboratory demand optimisation project in primary care

BackgroundThere is evidence of increasing use of laboratory tests with substantial variation between clinical teams which is difficult to justify on clinical grounds. The aim of this project was to assess the effect of a demand optimisation intervention on laboratory test requesting in primary care.\n\nMethodsThe intervention comprised educational initiatives, feedback to 55 individual practices on test request rates with ranking relative to other practices, and a small financial incentive for practices to engage and reflect on their test requesting activity. Data on test request numbers were collected from the laboratory databases for consecutive 12 month periods; pre-intervention 2011-12, intervention 2012-13, 2013-14, 2014-15, and post-intervention 2015-16.\n\nResultsThe intervention was associated with a 3.6% reduction in the mean number of profile test requests between baseline and 2015-16, although this was seen only in rural practices. In both rural and urban practices, there was a significant reduction in-between practice variability in request rates. The mean number of HbA1c requests increased from 1.9 to 3.0 per practice patient with diabetes. Variability in HbA1c request rates increased from 23.8% to 36.6%. At all considered time points, test request rates and variability were higher in rural than in urban areas.\n\nConclusionsThe intervention was associated with a reduction in both the volume and between practice variability of profile test requests, with differences noted between rural and urban practices. The increase in HbA1c requests may reflect a more appropriate rate of diabetes monitoring and also the adoption of HbA1c as a diagnostic test.\n\nStrengths & limitations of the studyO_LIWe assessed the effect of a laboratory demand optimisation intervention both on the value and between GP practice variability in laboratory test requesting.\nC_LIO_LIThe changes in laboratory test requesting were separately evaluated for rural and urban GP practices.\nC_LIO_LIOther factors (GP practice organisation, characteristics of general practitioners) potentially affecting between practice differences in laboratory test ordering were not taken into account due to data unavailability.\nC_LIO_LIThe demand management initiative was not accompanied by the cost-effectiveness analysis.\nC_LIO_LIThe demand optimisation intervention was conducted in a Northern Ireland (NI) Western Health and Social Care Trust and the findings have not been independently replicated in any other NI trusts.\nC_LI

pathology↗