Tan, Jolene H.
Testing error-management predictions in forgiveness decisions with cognitive modeling and process-tracing tools
2018-07, Tan, Jolene H., Luan, Shenghua, Gonzalez, Tita, Jablonskis, Evaldas
We investigated the forgiveness decision as an error-management task and demonstrated how tools from decision science can facilitate testing precise predictions about bias and its cognitive implementation. We combined decision modeling (using a weighting-and-adding model and a lexicographic heuristic) with process-tracing tools that track response times as well as the pattern of information acquisition. Our modeling results indicate that individuals adopted a decision bias commensurate with the relative cost of errors and that they also adjusted their bias after the perceived costs of errors were experimentally manipulated. Even though the 2 decision models were accurate in fitting the decisions (accuracies of around 85%), they were less successful in fitting the process measures. Our process-tracing results do not support either model—response times were in favor of the heuristic, whereas information-acquisition patterns favored the linear model, albeit slightly. Nevertheless, our methodology used to investigate the forgiveness decision can be a seen as a “blueprint” of how the cognitive processes of other error-management tasks can be investigated and how a more detailed mapping of the adapted mind can be achieved.