A similar latent variable model was constructed to test the fit of the local information content predictions to the data from Experiments 1 and 2 (see Figure 2). The outcome variables were coded in exactly the same manner as for the Run--Gap Model above. The predictor variables were calculated in terms of redundancy for features of a particular size.
Redundancy can be stated in terms of a ratio of entropies , and has been explored by Redlich  as an active mechanism in visual perception. In the case of segmentation, the quantity which needs to be calculated is a measure of local information content rather than redudancy. The measure of local information content which will be used here is simply 1 - R where R is redundancy.
Consider a repeating sequence of beats. If r is the number of contiguous repetitions with which a feature x of size s has previously occurred, then one possible measure of local surprise upon the reoccurrance of x is
whereas if x does not reoccur then
For each beat in the rhythmic sequence, three local measures of information content were calculated , , and . These three predictor variables have a value for each beat of each rhythmic sequence.
Figure 2: Path diagram of entropy prediction model where the predictors are: , entropy of features of length 1; , entropy of features of length 2; and , entropy of features of length 3.