Prediction of Successful Memory Encoding from fMRI Data.

In this work, we explore the use of classification algorithms in predicting mental states from functional neuroimaging data. We train a linear support vector machine classifier to characterize spatial fMRI activation patterns. We employ a general linear model based feature extraction method and use the t-test for feature selection. We evaluate our method on a memory encoding task, using participants' subjective prediction about learning as a benchmark for our classifier. We show that the classifier achieves better than random predictions and the average accuracy is close to subject's own prediction performance. In addition, we validate our tool on a simple motor task where we demonstrate an average prediction accuracy of over 90%. Our experiments demonstrate that the classifier performance depends significantly on the complexity of the experimental design and the mental process of interest.

Superiority of entangled measurements over all local strategies for the estimation of product coherent states.

It is shown that the ensemble {P(alpha),|alpha|alpha;{*}}, where P(alpha) is a Gaussian distribution of finite variance and |alpha is a coherent state, can be better discriminated with an entangled measurement than with any local strategy supplemented by classical communication. Although this ensemble consists of products of quasiclassical states without any squeezing, it thus exhibits a purely quantum feature. This remarkable effect is demonstrated experimentally by implementing the optimal local strategy on coherent states of light together with a global strategy that yields a higher fidelity.