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However, whereas the overall pattern 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, several teams (Formisano et al., 2003; Woods et al., 2009; Humphries et al., 2010; Langers et al., 2014a) have obtained strong tonotopic maps by evaluating Bold responses to just a few discrete frequencies using a basic linear mannequin (GLM). However, these models fail to seize the express representation of frequency selectivity in the auditory cortex, which is thought to characterize a wide range of auditory frequencies. Stimulus-particular biases also can alter the frequency desire assigned to a given fMRI voxel. More lately, considerably extra complex modeling approaches have been applied to characterizing the response selectivities of auditory areas. One influential class of models has utilized an strategy whereby natural scene stimuli are parameterized into a characteristic area and regularized linear regression is used to characterize each voxels response choice across this function house (Kay et al., 2008; Naselaris et al., 2011; Nishimoto et al., 2011). The advantage of this strategy is that it attempts to capture 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 house must be appropriate).
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