Modelling the Effects of Working Memory Demands on Accuracy Rates of Relational Reasoning Problems

Relational reasoning is a core cognitive ability necessary for intelligent behaviour as it evaluates relationships between mental representations. Laboratory-based tasks such as relational reasoning problems have long been used to investigate how individuals make inferences about such problems, with theories of mental models arguing that to solve such problems, individuals construct an integrated mental model based on the provided premises to generate or verify conclusions. Computational models of relational reasoning offer insights into how individuals generate such mental models and why some cognitive strategies may be preferred over others. However, many of these models do not directly account for what is often cited as a primary reason for the difficulty of different problems, the effects of increased working memory demand. In this paper, we present four ACT-R models that simulate the negative relationship between accuracy rates and relational problem complexity and demonstrate how different memory errors of omission and commission can account for qualitatively different reasoning processes. Our cognitive models demonstrate the importance of future work to consider individual differences in working memory processing, micro-strategy preferences, and the effects of different memory errors on the reasoning process.