Premise annotation in mental model construction: An ACT-R approach to processing indeterminacy in spatial relational reasoning

Reasoning about inference problems that allow for multiple interpretations requires maintaining intermediate representations that, if necessary, may be reconsidered at a later stage of the solution process. In that respect we describe the process of annotating premises in spatial relational reasoning that allows for the derivation of alternative representations. Furthermore, we show how ACT-R's subsymbolic processing principles substantially contribute to the underlying theoretical framework of the Preferred Mental Model Theory as they add a powerful component making precise accuracy predictions possible, a feature that in previous symbolic approaches has been neglected. In addition, we implemented and compared two strategies to investigate the persistence of the outcomes of the reasoning process. Furthermore, we examined how well data and predictions meet the central assumption that reasoning difficulty increases with the number of mental operations necessary to validate a putative conclusion.