In order to combat overfitting in computational encoding models, one method is to reduce the number of fit parameters. In population receptive field (pRF) mapping, a haemodynamic response function (HRF) is parameterized from the fMRI timeseries, and this may inflate model fits (r2). Here, we show that measured HRF estimates from a brief set of separate HRF measurement scans can be included in the pRF fitting procedure, reducing the degrees of freedom. We show that model fits between our “HRF-informed” pRF and the traditional fitted HRFs are comparable, suggesting that this method is more representative of the ground truth.
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