We explore the utility of functional Near Infra Red (fNIR) technology in providing both empirical support and a basis for assessing and predicting dynamic changes in cognitive workload within the theoretical context of computational cognitive modeling (CCM). CCM has had many successes and in recent years has expanded from a tool for basic research to one that can tackle more complex real-world tasks. As a tool for basic research it seeks to provide a model of cognitive functionality; as a tool for cognitive engineering it seeks applications in monitoring and predicting real-time performance. With this powerful theoretical tool we combine the empirical power of fNIR technology. The fNIR technology is used to non-invasively monitor regional hemodynamic activities, namely blood volume changes and oxygenation dynamics. We examined a simple auditory classification task in four different workload conditions. We monitored the blood activity in the prefrontal cortex region of the frontal lobe during the performance of the task to assess the patterns of activity as workload changes. We associated patterns of model activity with patterns of the hemodynamic data. We used ACT-R for creating the computational cognitive model. For the fNIR analysis, we used a generalized linear regression model along with time series clustering. We found that in the highest workload condition the model predicts a cognitive 'overload', which correlated well with the fNIR cluster and classification analysis, as this condition differs significantly from the other three conditions. Linear regression on a subset of the data where workload increases monotonically shows that apart from the overload condition, there was a positive relationship between increase in workload and increase in blood volume activation. In addition, individual variations in hemodynamic response suggest that individuals differ in how they process different workload levels.