The term process model is widely used but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that four dimensions characterize process models: They specify intermediate stages containing the hypothesized mental information processing. They make predictions not only for the behavior of interest but also for process-related variables. Third, the models’ process predictions can be derived from the input without reverse inference from the output data. Fourth, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Finally, process models require a conceptual scope specifying what the model refers to, that is, the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.
A fascinating but risky case of reverse inference: From measures to emotions!
