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.

2017-01, Tan, Jolene H., Luan, Shenghua, Katsikopoulos, Konstantinos

Whether to forgive is a key decision supporting cooperation. Like many other evolutionarily recurrent decisions, it is made under uncertainty and requires the trade-off of costs and benefits. This decision can be conceptualized as a signal detection or error management task: Forgiving is adaptive if a relationship with the “harmdoer” will be fitness enhancing and not adaptive if the relationship will be fitness reducing, and the decision should be biased toward lowering the likelihood of the more costly error, which depending on the context may be either erroneously not forgiving or forgiving. Building on such conceptualization, we developed two cognitive models and examined how well they described participants' forgiveness decisions in hypothetical scenarios and predicted their decisions in recalled real-life incidents. We found that the models performed similarly and generally well—around 80% in describing and 70% in prediction. Moreover, this modeling approach allowed us to estimate the decision bias of each participant; we found that the biases were generally consistent with the prescriptions of signal detection theory and were directed at reducing the more costly error. In addition to testing mechanistic models of the forgiveness decision, our study also contributes to forgiveness research by applying a novel experimental method that studied both hypothetical and real-life decisions in tandem. These models and experimental methods could be used to study other evolutionarily recurrent problems, advancing understanding of how they are solved in the mind.