 According to Lloyd, the tripartite structure of consciousness raises a unique problem for analyzing fMRI data and designing experiments. The problem stems from the tension between the sameness in the object of experience (e.g., the same tune through its progression) and the temporal fluidity of experience itself (e.g., the transitions between heard notes). At the time Lloyd was writing, one standard means of analyzing fMRI data consisted in averaging several data sets and subtracting an estimate of baseline activation from the composites.  [17]  This is done to filter noise from the task-related hemodynamic response. But as Lloyd points out, this then-common practice ignores much of the data necessary for studying the neural correlates of consciousness. It produces static images that neglect the relationships between data points over the time course. Lloyd instead applies a multivariate approach to studying fMRI data, under the assumption that a recurrent network architecture underlies the temporal processing that gives rise to experienced time. A simple recurrent network has an input layer, an output layer, a hidden layer, and an additional layer that copies the prior activation state of either the hidden layer or the output layer. Allowing the output layer to represent a predicted outcome, the input layer can then represent a current state and the additional layer a prior state. This assignment mimics the tripartite temporal structure of experience in a network architecture. If the neuronal mechanisms underlying conscious experience are approximated by recurrent network architecture, one prediction is that current neuronal states carry information about immediate future and prior states. Applied to fMRI, the model predicts that time points in an image series will carry information about prior and subsequent time points. The results of Lloyd’s (2002) analysis of 21 subjects’ data sets, sampled from the publicly accessible National fMRI Data Center, support this prediction.