We investigate whether it is possible to infer from implicit feedback what is relevant for a user in an information retrieval task. Eye movement signals are measured; they are very noisy but potentially contain rich hints about the current state and focus of attention of the user. In the experimental setting relevance is controlled by giving the user a specific search task, and the modeling goal is to predict from eye movements which of the given titles are relevant. We extract a set of standard features from the signal, and explore the data with statistical information visualization methods including standard self-organizing maps (SOMs) and SOMs that learn metrics. Relevance of document titles to the processing task can be predicted with reasonable accuracy from only a few features, whereas prediction of relevance of specific words will require new features and methods.