A biologically inspired cognitive skills measurement approach

Cognitive Skills (CS) are essential for job interviews and government policymaking. We have no existing work that can predict CS during interviews and policymaking. The current work proposes CS measurement method that simulates the nonlinear relationship between CS and Basic Human Factor (BHF) (aging, infection, emotions, awareness, personality, education, and experience). Firstly, the method obtains conditional probabilities of CS with respect to BHF using training data set. Secondly, particular domains and ranges are define for BHF. Based on the conditional probabilities of CS, the technique divide training data set into three partitions that result in three model equations for CS measurement method. Moreover, the propose method divides into three algorithms. The first algorithm estimates values for BHF. The second algorithm verifies the estimated values of BHF while the third algorithm predicts CS values by using the estimated values of BHF. During the experiment, the propose method test on test data set. We achieve the prediction accuracy of the method through Mean Forecast Error (MFE), Mean Absolute Deviation (MAD) and Tracking Signal (TS) measures. The results show that the accuracy of the method is ^{_{91 %}} . Finally, we discuss these results as well as the comparison of the current method with competitive methods.