Statistics of natural movements are reflected in motor errors.

Humans use their arms to engage in a wide variety of motor tasks during everyday life. However, little is known about the statistics of these natural arm movements. Studies of the sensory system have shown that the statistics of sensory inputs are key to determining sensory processing. We hypothesized that the statistics of natural everyday movements may, in a similar way, influence motor performance as measured in laboratory-based tasks. We developed a portable motion-tracking system that could be worn by subjects as they went about their daily routine outside of a laboratory setting. We found that the well-documented symmetry bias is reflected in the relative incidence of movements made during everyday tasks. Specifically, symmetric and antisymmetric movements are predominant at low frequencies, whereas only symmetric movements are predominant at high frequencies. Moreover, the statistics of natural movements, that is, their relative incidence, correlated with subjects' performance on a laboratory-based phase-tracking task. These results provide a link between natural movement statistics and motor performance and confirm that the symmetry bias documented in laboratory studies is a natural feature of human movement.

Sources of age determination errors for sablefish (Anoplopoma fimbria)

This study was undertaken to resolve problems in age determination of sablefish (Anoplopoma fimbria). Aging of this species has been hampered by poor agreement (averaging less than 45%) among age readers and by differences in assigned ages of as much as 15 years. Otoliths from fish that had been injected with oxytetracycline (OTC) and that had been at liberty for known durations were used to determine why age determinations were so difficult and to help determine the correct aging procedure. All fish were sampled from Oregon southwards, which represents the southern part of their range. The otoliths were examined with the aid of image processing. Some fish showed little or no growth on the otolith after eight months at liberty, whereas otoliths from other fish grew substantially. Some fish lay down two prominent hyaline zones within a single year, one in the summer and one in the winter. We classified the otoliths by morphological type and found that certain types are more likely to lay down multiple hyaline zones and other types are likely to lay down little or no zones. This finding suggests that some improvement could be achieved by detailed knowledge of the growth characteristics of the different types. This study suggests that it may not be possible to obtain reliable ages from sablefish otoliths. At the very least, more studies will be required to under-stand the growth of sablefish otoliths.

High penetrance of sequencing errors and interpretative shortcomings in mtDNA sequence analysis of LHON patients

For identifying mutation(s) that are potentially pathogenic it is essential to determine the entire mitochondrial DNA (mtDNA) sequences from patients suffering from a particular mitochondrial disease, such as Leber hereditary optic neuropathy (LHON). Howe

Model-based influences on humans' choices and striatal prediction errors.

The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.