The human visual system has the inherent capability of using selective attention to rapidly process visual information across visual scenes. Early models of visual saliency are purely feature-based and compute visual attention for static scenes. However, to model the human visual system, it is important to also consider temporal change that may exist within the scene when computing visual saliency. We present a biologically-plausible model of dynamic visual attention that computes saliency as a function of proto-objects modulated by an independent motion-sensitive channel. This motion-sensitive channel extracts motion information via biologically plausible temporal filters modeling simple cell receptive fields. By using KL divergence measurements, we show that this model performs significantly better than chance in predicting eye fixations. Furthermore, in our experiments, this model outperforms the Itti, 2005 dynamic saliency model and insignificantly differs from the graph-based visual dynamic saliency model in performance.