Toward a neural network model of framing with fuzzy traces

In a decision study called the Asian Disease Problem, Tversky and Kahneman [1] found that framing risky health choices in terms of gains or losses of lives leads to radically different choices: risk seeking for losses and risk avoidance for gains. The difference between the two choices is called the framing effect. The authors explained framing effects via psychophysics of the numbers of lives saved or lost. Yet Reyna and Brainerd [2] showed that the strength of the framing effect depended not on the numbers but on whether one of options explicitly contained the possibility of no lives lost or saved. They fit their explanation into fuzzy trace theory whereby decisions are based not on details of the options given but on the gist (underlying meaning) of the options. We discuss how a brain-based neural network model of other decision data [3] that combines fuzzy trace theory with adaptive resonance theory can be extended to these framing data. Simulations are in progress.