Radiological images constitute a special class of images that are captured (or computed) specifically for the purpose of diagnosing patients. However, because these are not "natural" images, radiologists must be trained to interpret them through a process called "perceptual learning". However, because perceptual learning is implicit, experienced radiologists may sometimes find it difficult to explicitly (i.e. verbally) train less experienced colleagues. As a result, current methods of training can take years before a new radiologist is fully competent to independently interpret medical images. We hypothesize that eye tracking technology (coupled with multimedia technology) can be used to accelerate the process of perceptual training, through a Hebbian learning process. This would be accomplished by providing a radiologist-in- training with real-time feedback as he/she is fixating on important regions of an image. Of course this requires that the training system have information about what regions of an image are important - information that could presumably be solicited from experienced radiologists. However, our previous work has suggested that experienced radiologists are not always aware of those regions of an image that attract their attention, but are not clinically significant - information that is very important to a radiologist in training. This paper discusses a study in which local entropy computations were done on scan path data, and were found to provide a quantitative measure of the moment-by-moment interest level of radiologists as they scanned chest x-rays. The results also showed a striking contrast between the moment-by-moment deployment of attention between experienced radiologists and radiologists in training.