Neuronal decision making model

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The personal mechanism of decision making is based on evaluative neural network (ENN). ENN has two or more paths for reaching a goal. For every node in a path, there are several implications. Every implication has a value which is represented by the reward system. the value can very from positive to negative. The strength of the reward will be composed by the intensity of the reward that the reward system cell produce, and the closeness of the reward to the action, where immediate reward will increase the strength of the reward to the implication, while delayed reward will result reduced connection between implication and reward. The strength will be produced according to the learning rules of LTP and STP.

Elements Influencing ENN

FFFF and exploitation will heighten the threshold for using paths, therefore only strongest path will be used. If there aren't very strong paths available, no path will be selected, and the decision making system will freeze. The strongest path will be produced by conducting more repetitions in this path relative to other paths. PFC and exploration on the other side of the scale will reduce the threshold, and therefore will enable a larger variety of options. the option that will be selected will be the one with the most rewards in non-self control mode. In self-control mode, a grater rewards in the future will be preferred to immediate high rewards.

RPE will produce lower the threshold even more, which will result even higher diversity in the paths.

Inducing changes in the Threshold of the Paths

When a person is in a feeling of threat, it will activate FFFF, that will lower the threshold. Any attack on the ego, can produce feeling of attack.