Phase discrimination ability in Mongolian gerbils provides evidence for possible processing mechanism of mistuning detection.

Compared to humans, Mongolian gerbils (Meriones unguiculatus) are much more sensitive at detecting mistuning of frequency components of a harmonic complex (Klinge and Klump. J Acoust Soc Am 128:280-290, 2010). One processing mechanism suggested to result in the high sensitivity involves evaluating the phase shift that gradually develops between the mistuned and the remaining components in the same or separate auditory filters. To investigate if this processing mechanism may explain the observed sensitivity, we determined the gerbils' thresholds to detect a constant phase shift in a component of a harmonic complex that is introduced without a frequency shift. The gerbils' detection thresholds for constant phase shifts were considerably lower for a high-frequency component (6,400 Hz) than for a low-frequency component (400 Hz) of a 200-Hz harmonic complex and increased with decreasing stimulus duration. Compared to the phase shifts calculated from the mistuning detection thresholds, the detection thresholds for constant phase shifts were similar to those for gradual phase shifts for the low-frequency harmonic but considerably lower for the high-frequency harmonic. A simulation of the processing of harmonic complexes by the gerbil's peripheral auditory filters when components are phase shifted shows waveform changes comparable to those assessed for mistuning detection Klinge and Klump (J Acoust Soc Am 128:280-290, 2010) and provides evidence that detection of the gradual phase shifts may underlie mistuning detection

A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such as Montenegrin