Perceptual decisions are accompanied by a level of confidence that tends to track our decisional accuracy. However, this correspondence can be dissociated in noisy and atypical environments or in some clinical populations. This raises an important question: what are the neural computations of perceptual metacognition if their output can diverge from perceptual decisions themselves? In a recent paper, it was argued that tuned inhibition — i.e., the degree to which a neuron is inhibited by neighboring neurons with opposing tuning preferences, which varies from neuron to neuron — is a crucial part of the underlying mechanism. In this dissertation, we aimed to investigate the neural mechanisms underlying perceptual metacognition by seeking evidence of tuned inhibition via functional magnetic resonance imaging (fMRI). We first explored how we might validate the tuned inhibition model using fMRI data, by simulating the activity of ‘voxels’ of different compositions in the presence of evidence for and against a perceptual decision in a decision and confidence task. We showed that it is possible to quantify how a voxel’s level of tuned inhibition dictates its predictive power for confidence judgments, thus providing support for use of these stimuli and analyses in fMRI data to validate the model. The observed relationships from our model simulations were then applied to human fMRI data. We identified evidence of the model within decision-making and density-sensitive regions of the brain using fMRI. Finally, we provided further evidence supporting tuned inhibition as a model of confidence by decoding high- versus low-confidence responses on a trial-by-trial basis from voxels within higher order regions, such as the dorsal prefrontal cortex.