Computational modeling of moral decision-making data
SHOCKS
In the moral decision task, we modeled participants’ choices in the Pre- and Post-training session separately with a computational model reported in a previous study using the same moral decision-making taskThe harm aversion parameter (κ) can be understood as the exchange rate between Δm and Δs. When κ is close to 1, the Decider becomes extremely harm averse, which means they will refuse to increase the number of shocks even when the extra money they can get is very large. In contrast, when κ approaches 0, the Decider is minimally harm averse and will accept any number of shocks in exchange for a small amount of money. In this model, the harm aversion parameter was allowed to vary across conditions. Estimating a separate harm aversion for the Self and the Other condition has been an established practice for this type of task, which have been replicated in a number of studies to dateAs a comparison, we estimated a model where we combined the data from both sessions in the same model and included dummy variables to indicate the pre- and post-training session (one for the Self condition and one for the Other condition). This model, however, had a higher BIC (8592) and a lower explanatory power (pseudo R