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However, while the overall sample of frequency gradients is very replicable, the accuracy with which these maps have modeled the precise frequency preferences of particular person voxels is unclear. For instance, a number of teams (Formisano et al., 2003; Woods et al., 2009; Humphries et al., 2010; Langers et al., 2014a) have obtained robust tonotopic maps by evaluating Bold responses to only a few discrete frequencies using a basic linear mannequin (GLM). However, these fashions fail to capture the explicit representation of frequency selectivity within the auditory cortex, which is thought to signify a variety of auditory frequencies. Stimulus-particular biases may alter the frequency desire assigned to a given fMRI voxel. More recently, considerably extra complex modeling approaches have been applied to characterizing the response selectivities of auditory areas. One influential class of fashions has utilized an method whereby pure scene stimuli are parameterized right into a feature house and regularized linear regression is used to characterize each voxels response choice throughout this function house (Kay et al., 2008; Naselaris et al., 2011; Nishimoto et al., 2011). The benefit of this approach is that it makes an attempt to seize the complexity of cortical processing without explicitly imposing a preselected mannequin (e.g., Gaussian tuning) upon the response selectivity profile for a given voxel (although the parameterization of the stimulus space should be acceptable).
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