We present results from a user study of task performance on streamtube visualizations, such as those used in three-dimensional (3D) vector and tensor field visualizations. This study used a tensor field sampled from a full-brain diffusion tensor magnetic resonance imaging (DTI) dataset. The independent variables include illumination model (global illumination and OpenGL-style local illumination), texture (with and without), motion (with and without), and task. The three spatial analysis tasks are: (1) a depth-judgment task: determining which of two marked tubes is closer to the user's viewpoint, (2) a visual-tracing task: marking the endpoint of a tube, and (3) a contact-judgment task: analyzing tube-sphere penetration. Our results indicate that global illumination did not improve task completion time for the tasks we measured. Global illumination reduced the errors in participants' answers over local OpenGLstyle rendering for the visual-tracing task only when motion was present. Motion contributed to spatial understanding for all tasks, but at the cost of longer task completion time. A high-frequency texture pattern led to longer task completion times and higher error rates. These results can help in the design of lighting model, such as flow or diffusion-tensor field visualizations and identify situations when the lighting is more efficient and accurate.