Language learning as a function of infant directed speech (IDS) in Spanish: Testing neural commitment using the positive-MMR

In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.

The current review constitutes the first comprehensive look at the possibility that the mismatch negativity (MMN, the deflection of the auditory ERP/ERF elicited by stimulus change) might be generated by so-called fresh-afferent neuronal activity. This possibility has been repeatedly ruled out for the past 30 years, with the prevailing theoretical accounts relying on a memory-based explanation instead. We propose that the MMN is, in essence, a latency- and amplitude-modulated expression of the auditory N1 response, generated by fresh-afferent activity of cortical neurons that are under nonuniform levels of adaptation.