Computational modeling of dynamic decision making using connectionist networks

Authors
Hoda Mansourian 1
Azadeh Nesari 1
Alireza Moradi 2
Babak Mohamadi 3
1 IRICSS
2 Kharazmi University
3 Aja University, Teharan, Iran.
Abstract
In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decision making the role of Amygdala is so important and we expect that a model with two parts (thalamus and Amygdala) can have the best result in modeling participants decisions without considering any part for cortex process. For this purpose 56 participants composed of 20 men and 36 women wanted to do Iowa Gambling Task. Results show that the networks related to two parts model predict 62.57 Percent’s of participant’s decisions and the 3parts model has 68.46 Percent’s of that. In conclusion it can be said that three parts modeling has been more success than mathematical two parts model in predicting the performance of participants and the difference is significant. In other words cortex role in this kind of decision making is quite important.
Keywords
computational modeling
dynamic decision making
connectionist networks
Iowa Gambling Task
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