الگوريتم تصميم‌گيري سطح بالا با تحليل سيگنال‌هاي قطر مردمك

High-level decision algorithm with analysis of pupil diameter signals

ﻣﺤﻘﻘﻴﻦ ﺳﻌﻲ ﺩﺍﺭﻧﺪ ﺑﺎ ﭘﻴﺎﺩﻩﺳﺎﺯﻱ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺗﺼﻤﻴﻢﮔﻴﺮﻱ ﻣﺸﺎﺑﻪ ﻋﻤﻠﮑﺮﺩ ﻣﻐﺰ، ﺑﻪ ﻗﺪﺭﺕ ﻗﺎﺑﻞﺗﻮﺟﻪ ﺫﻫﻦ ﺍﻧﺴﺎﻥ ﺩﺳﺖ ﻳﺎﺑﻨﺪ. ﺗﺼﻤﻴﻢﻫﺎﻱ ﺳﻠﺴﻠﻪ ﻣﺮﺍﺗﺒﻲ، ﺗﺼﻤﻴﻤﺎﺕ ﭘﻴﭽﻴﺪﻩﺍﻱ ﻫﺴﺘﻨﺪ ﮐﻪ ﻧﻴﺎﺯ ﺑﻪ ﻣﮑﺎﻧﻴﺰﻡﻫﺎﻱ ﺍﺳﺘﺪﻻﻝ ﻓﺮﺍﺷﻨﺎﺧﺘﻲ ﺩﺭ ﻣﻐﺰ ﺩﺍﺭﻧﺪ. ﺑﺎﺯﺧﻮﺭﺩ ﻣﻨﻔﻲ، ﻗﻄﻌﻴﺖ ﻭ ﻗﺪﺭﺕ ﻣﺤﺮﮎ، ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻳﻲ ﻫﺴﺘﻨﺪ ﮐﻪ ﺩﺭ ﺷﮑﻞﮔﻴﺮﻱ ﺍﻳﻦ ﻧﻮﻉ ﺗﺼﻤﻴﻤﺎﺕ ﻧﻘﺶ ﺩﺍﺭﻧﺪ. ﺩﺭ ﺍﻳﻦ ﭘﮋﻭﻫﺶ ﺑﻪﻣﻨﻈﻮﺭ ﺳﺎﺧﺖ ﻳﮏ ﭼﺎﺭﭼﻮﺏ ﻣﺤﺎﺳﺒﺎﺗﻲ ﻣﺸﺎﺑﻪ ﻋﻤﻠﮑﺮﺩ ﻣﻐﺰ ﺑﺮﺍﻱ ﺳﻴﺴﺘﻢﻫﺎﻱ ﻫﻮﺷﻤﻨﺪ، ﺩﺭﮎ ﻣﺎﻫﻴﺖ ﺑﻴﻮﻟﻮﮊﻱ ﺷﮑﻞﮔﻴﺮﻱ ﺗﺼﻤﻴﻤﺎﺕ ﺳﻄﺢ ﺑﺎﻻ، ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺍﻧﻮﺍﻉ ﺩﻳﮕﺮ ﺩﺍﺩﻩﻫﺎ ﻋﻼﻭﻩ ﺑﺮ ﺩﺍﺩﻩﻫﺎﻱ ﺭﻓﺘﺎﺭﻱ ﻧﻴﺰ ﻣﻬﻢ ﺧﻮﺍﻫﺪ ﺑﻮﺩ. ﺍﺯ ﺁﻧﺠﺎﻳﻲ ﮐﻪ ﭘﺎﺳﺦﻫﺎﻱ ﻏﻴﺮﺍﺭﺍﺩﻱ ﭼﺸﻤﻲ ﺣﺎﺻﻞ ﺍﺯ ﺧﺮﻭﺟﻲ ﺁﺯﻣﺎﻳﺶ ﺭﻭﺍﻥ-ﻓﻴﺰﻳﮏ، ﻧﻤﺎﻳﻨﺪﻩ ﻣﻌﺘﺒﺮﻱ ﺍﺯ ﻋﻤﻠﮑﺮﺩ ﺳﺎﺯ ﻭ ﮐﺎﺭ ﻧﻮﺭﻭﻧﻲ ﻣﻐﺰ ﻣﻲﺑﺎﺷﻨﺪ، ﺩﺭ ﺍﻳﻦ ﭘﮋﻭﻫﺶ ﻋﻼﻭﻩ ﺑﺮ ﺗﺤﻠﻴﻞ ﺩﺍﺩﻩﻫﺎﻱ ﺭﻓﺘﺎﺭﻱ ﺑﻪ ﺍﻳﻦ ﻣﺴﺌﻠﻪ ﭘﺮﺩﺍﺧﺘﻪ ﺷﺪ ﮐﻪ ﺁﻳﺎ ﺑﺎ ﺗﺤﻠﻴﻞ ﺩﺍﺩﻩﻫﺎﻱ ﻏﻴﺮﺍﺭﺍﺩﻱ ﺍﻧﺴﺎﻥ )ﺳﻴﮕﻨﺎﻝﻫﺎﻱ ﭼﺸﻤﻲ( ﻣﻲﺗﻮﺍﻥ ﺑﻪ ﺩﻳﻨﺎﻣﻴﮏ ﺣﺎﮐﻢ ﺑﺮ ﺗﻐﻴﻴﺮﺍﺕ ﺗﺼﻤﻴﻤﺎﺕ ﺳﻄﺢ ﺑﺎﻻ ﭘﻲ ﺑﺮﺩ. ﻣﺸﺎﻫﺪﻩ ﺷﺪﻩ ﮐﻪ ﺍﻧﺪﺍﺯﻩ ﻗﻄﺮ ﻣﺮﺩﻣﮏ، ﺍﺣﺘﻤﺎﻝ ﺗﻐﻴﻴﺮ ﺩﺭ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﺗﺼﻤﻴﻢﻫﺎﻱ ﺳﻄﺢ ﺑﺎﻻ ﺭﺍ ﭘﻴﺶﺑﻴﻨﻲ ﻣﻲﮐﻨﺪ ﻭ ﺑﺎﺯﺗﺎﺏ ﺍﺳﺘﺮﺍﺗﮋﻱ ﺗﺼﻤﻴﻢ ﺳﻄﺢ ﺑﺎﻻﻱ ﻓﺮﺩ ﺗﺤﺖ ﺷﺮﺍﻳﻂ ﭘﻴﭽﻴﺪﻩ ﺍﺳﺖ. ﺳﭙﺲ ﺩﺭ ﺭﺍﺳﺘﺎﻱ ﺗﻮﺳﻌﻪ ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﺸﺎﺑﻪ ﻋﻤﻠﮑﺮﺩ ﻣﻐﺰ ﺩﺭ ﻣﺤﻴﻂﻫﺎﻱ ﭘﻴﭽﻴﺪﻩ، ﭼﺎﺭﭼﻮﺑﻲ ﺑﺮﺍﻱ ﺗﺼﻤﻴﻤﺎﺕ ﺳﻠﺴﻠﻪﻣﺮﺍﺗﺒﻲ ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺍﺳﺖ.

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