A paradigm for the visualization of image interpretation systems is described. The paradigm uses visual interactions with the human observer to extract visual knowledge from the user and format it into rules, thresholds and choice of relevant attributes. The detailed description of the algorithm is presented through the analysis of an example of grouping line-segments into straight lines. Two different levels of abstraction are involved in the proposed learning mechanism. One involves the calculation of values and thresholds and is based on a statistical analysis of the user's chosen examples. The other mechanism deals with reformatting and rewriting of rules and is a symbolic process that belongs to the more abstract levels of interpretation. The mechanism for reconsideration incorporates the two concept learning paradigms, extension and correction. The correcting of formerly learned Rules is based on additional examples chosen by the user and invokes a new, low level, calculation of thresholds only as a last resource.