High-level decision algorithm with analysis of pupil diameter signals

Researchers are trying to achieve the power of the human mind by implementing decision-making algorithms similar to brain function. Hierarchical decisions are complex decisions that require metacognitive reasoning mechanisms in the brain. Negative feedback, certainty, and motion strength are the parameters that play a role in shaping such decisions. In this study, in order to design a computational framework similar to brain function for intelligent systems, it will be important to understand the biology nature of high-level decision-making, using other types of data in addition to behavioral data. Since involuntary eye responses resulting from the output of psychophysical experiments are a reliable representative of the function of the neuronal mechanism in the brain, in this study addition to the analysis of behavioral data, this issue has been addressed whether it is possible to understand the dynamics of changes in high-level decisions by analyzing involuntary human data (eye signals). We found that pupil diameter size predicts the likelihood of changes in the parameters of high-level decisions, and reflects the individual's high-level decision strategy under complex conditions. Then, in order to design systems similar to brain function in complex environments, we provide a framework for hierarchical decisions.