General approach for the model-based diagnostics using a likelihood procedure. The model function is used to generate the look-up table y, and it is assumed that this function works in a same way as the subject. The subject data is obtained experimentally. For illustration, a folder represents all data from either a subject or from a specific model instance. All possible combinations of model parameters make up the folder shelf, representative of the model table y. Consider that for the respective folder, each new measurement generates a new sheet of paper containing the stimulus information and the response. The goal is to find the model instance (folder) that has the maximum likelihood of having generated the experimental data. The resulting likelihood surface (left side of purple rectangular) has the same dimension as the folder shelf. In addition to the likelihood of each instance, confidence intervals can be calculated for each model parameter, which represent the model-based diagnosis.