Ever wondered as to how one takes a decision based on arithmetical possibilities? Well, researchers from Yale University claim to have developed a computer model to clarify how the brain could reach decisions with regards to statistical probabilities.
For example, the researchers want to know as to how a doctor can make a diagnosis, with numerous conflicting test outcomes. A certain scientist suggests that synapses, the connections between neurons, appear to be proficient in working out likelihood from observed signs in order to craft an arithmetical deduction.
Xiao-Jing Wang, professor of neurobiology at Yale School of Medicine and at the Kavli Institute of Neuroscience, commented, “We often need to make probabilistic inference—like deducing which of the numerous foods we ate made us sick, reaching a medical diagnosis based on symptoms and test results, or deciding whether it will rain or shine given a few pieces of information about the atmosphere. Such decisions are based on the calculus of chance or the statistical theory of prediction.”
Wang mentioned, “What’s interesting is that such complicated probabilistic computations and psychological phenomena can now be studied, perhaps explained, in terms of the neural computation in the brain.”
Along with his previous postdoctoral colleague, Alireza Soltani, now at the California Institute of Technology, Wang supposedly developed computer models of neural circuits to examine how such probabilistic choices occur in the brain.
The model supposedly clarifies a phenomenon known as base rate neglect supposedly seen in humans. This base rate neglect could approximately denote that a part of information that seems to be evenly prognostic of two likely results is apparently looked upon by people to be more foretelling for the one that appears to be less feasible.
The findings were published in the journal Nature Neuroscience.