Artificial Neural Networks can Detect Human Ambiguity

Artificial Neural Networks (ANNs) computational model based on the structure and functions of biological neural networks, it became a strong tool for researching artificial intelligence and information analysis and are utilised in robotics, social sciences and neuroscience for classification, prediction and pattern recognition. A global scientific team which incorporates scientists from Russia has created an artificial neural network that detects human ambiguity. They assist to classify neural signals, observe pathological activity of the brain (for example, with epilepsy), and neurodegenerative diseases.

ANNs have three layers that are interconnected. The primary layer consists of input neurons. Those neurons send information on to the second layer that successively sends the output neurons to the third layer. Training an artificial neural network involves selecting from allowed models for which there are several associated algorithms.

In this analysis, the scientists showed the potency of application of the ANN for categorizing human MEG trials corresponding to the sensitivity of bistable visual stimuli with totally different degrees of ambiguity. They were ready acknowledge and characterize the state of uncertainty of human choice in detail at the time the decision was made by the brain and were able measure the time period accurately during which a person hesitates and cannot make a decision. With these results obtained at the time of research, they described the possible application of ANNs for detection of bistable brain activity related to difficulties within the decision-making method.

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