Deficits in sensory prediction are related to delusional ideation in healthy individuals.

Motor control strongly relies on neural processes that predict the sensory consequences of self-generated actions. Previous research has demonstrated deficits in such sensory-predictive processes in schizophrenic patients and these low-level deficits are thought to contribute to the emergence of delusions of control. Here, we examined the extent to which individual differences in sensory prediction are associated with a tendency towards delusional ideation in healthy participants. We used a force-matching task to quantify sensory-predictive processes, and administered questionnaires to assess schizotypy and delusion-like thinking. Individuals with higher levels of delusional ideation showed more accurate force matching suggesting that such thinking is associated with a reduced tendency to predict and attenuate the sensory consequences of self-generated actions. These results suggest that deficits in sensory prediction in schizophrenia are not simply consequences of the deluded state and are not related to neuroleptic medication. Rather they appear to be stable, trait-like characteristics of an individual, a finding that has important implications for our understanding of the neurocognitive basis of delusions.

Domains beyond the grain boundary

Thin films of inorganic materials are used in diverse applications, typically in polycrystalline form due to their relatively simple production. We have used enhanced piezoresponse force microscopy to investigate the domain distribution within neighbouring grains in thin polycrystalline films of the ferroelectric-ferroelastic system lead zirconate titanate (PZT). We demonstrate that domains are organized into areas with a correlated alignment of the ferroelastic and ferroelectric domains, spanning multiple grain boundaries. We present five typical arrangements of such structures: azimuthal, radial, gradient, and short- and long- range linear domain organizations. Moreover, we discuss the mechanical and electrical constraints that dictate these structures. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.