A neural network model of decisions on the Asian Disease Problem

Tversky and Kahneman [1] found that human decisions can be inconsistent across descriptions of the options. An example is the Asian Disease Problem, whereby preferences between two public health programs are different when options are framed in terms of deaths versus lives saved. Several variants of the Asian Disease paradigm and an analogous problem were run [2, 3]: the results showed that the strength of the framing effect depended on whether one of options explicitly contained the possibility of no lives lost or saved. This result was explained by fuzzy trace theory whereby decisions are based not on details of the options but on their gist (underlying meaning). We simulate these results using a neural network model that blends fuzzy trace theory with adaptive resonance theory and contains analogs of orbitofrontal cortex, amygdala, anterior cingulate, and striatum.