Testing error-management predictions in forgiveness decisions with cognitive modeling and process-tracing tools

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2018
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Luan, Shenghua
Gonzalez, Tita
Jablonskis, Evaldas
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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.

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ISO 690TAN, Jolene H., Shenghua LUAN, Tita GONZALEZ, Evaldas JABLONSKIS, 2018. Testing error-management predictions in forgiveness decisions with cognitive modeling and process-tracing tools. In: Evolutionary Behavioral Sciences. 2018, 12(3), pp. 206-217. ISSN 2330-2925. eISSN 2330-2933. Available under: doi: 10.1037/ebs0000114
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@article{Tan2018-07Testi-46062,
  year={2018},
  doi={10.1037/ebs0000114},
  title={Testing error-management predictions in forgiveness decisions with cognitive modeling and process-tracing tools},
  number={3},
  volume={12},
  issn={2330-2925},
  journal={Evolutionary Behavioral Sciences},
  pages={206--217},
  author={Tan, Jolene H. and Luan, Shenghua and Gonzalez, Tita and Jablonskis, Evaldas}
}
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