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.
Sense and sensibility of ownership : Type of ownership experience and valuation of goods
2015-10, Wang, Xiao-Tian, Ong, Lay See, Tan, Jolene H.
This study examined how the type of ownership experience affects the valuation of a good. We hypothesized that the sense of ownership is a psychological derivative of resource acquisition and allocation. We predicted a valuation order of stable ownership or no-ownership < alternating (interchanging) ownership < sudden reversals in ownership. One hundred and sixty-six participants played an object-acquisition “game”, a computer simulation of gaining or losing the ownership of an object (e.g., a pen, a mug, or a flashlight) with different outcome sequences, preprogramed but unbeknownst to the participants. After each game, the participant valued the target object by indicating their willingness-to-pay price, if the last outcome was a loss, or willingness-to-accept price, if the last outcome was a gain. The valuation of an object was highest after experiencing a final reversal in ownership from losses to a final gain or from gains to a final loss, followed by alternating ownership and stable (patrimonial) ownership or constant non-ownership. Wins or losses are not created equal due to different trajectories in how people come to own (lose) objects. The results also suggest that loss aversion is better understood as a specific result of ownership experience.
A signal-detection approach to modeling forgiveness decisions
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.
Process Modeling in Social Decision Making
2016, Tan, Jolene H.
Understanding how the benefits of cooperation can be reaped while the risks of exploitation from other individuals can be managed has received significant research attention in the past few decades. However, despite its prominence, little is known about how we make these social decisions; it is unclear what decision processes underlie our interactions with others. My goal in this dissertation was to investigate the decision processes of social interactions. I adopted the perspective of the “adapted mind” and “bounded rationality” in order to investigate how humans solve the evolutionarily recurrent problems of social living under limitations of time, information, and computational ability. I combined these theoretical foundations with the methodology of cognitive process modeling, which enabled me to test fine- grained predictions about the underlying decision processes. In the introduction chapter, I provided a brief overview of some controversies in the field so as to provide the backdrop for the rest of the chapters. In the first chapter, I proposed a framework that can be used to qualify what is a cognitive process model. The framework contains necessary conditions that a model needs to fulfill in order to be considered a process model. The “how to” format of the chapter can serve as a guide for building process models. The second chapter is an exemplification of how process models can be used to study social decisions such as forgiveness. I developed and tested two models—the heuristic-based fast-and-frugal trees, and the linear model Franklin’s rule—and found that both models performed similarly well (accuracy of ~80% in description and ~70% in prediction). The third chapter extended the previous by examining how base rate information about the benevolence of the social environment is used in decisions about whether to forgive. I provided evidence that base rate information is used in forgiveness decisions and it is expressed as a level of social trust, a belief about whether people are generally benevolent or malevolent. Taken together, my dissertation advanced understanding about cooperation by specifying and testing the decision processes that underlie social interaction.