Confirmation Bias Estimation from Electroencephalography with Machine Learning
AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States
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Cognitive biases are known to plague human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, behavioral methods are employed to measure the level of bias in a decision. However, these measures can be hindered by a multitude of subjective factors and cannot be collected in real-time. This work investigates enhancing the current measures of estimating confirmation bias with additional behavior patterns and physiological variables to explore the viability of real-time bias detection. Confirmation bias in decisions is estimated by modeling the relationship between Electroencephalography EEG signals and behavioral data using machine learning methods.
- Medicine and Medical Research