Representations of uncertainty in sensorimotor control.

Uncertainty is ubiquitous in our sensorimotor interactions, arising from factors such as sensory and motor noise and ambiguity about the environment. Setting it apart from previous theories, a quintessential property of the Bayesian framework for making inference about the state of world so as to select actions, is the requirement to represent the uncertainty associated with inferences in the form of probability distributions. In the context of sensorimotor control and learning, the Bayesian framework suggests that to respond optimally to environmental stimuli the central nervous system needs to construct estimates of the sensorimotor transformations, in the form of internal models, as well as represent the structure of the uncertainty in the inputs, outputs and in the transformations themselves. Here we review Bayesian inference and learning models that have been successful in demonstrating the sensitivity of the sensorimotor system to different forms of uncertainty as well as recent studies aimed at characterizing the representation of the uncertainty at different computational levels.

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.

Capture probability of a survey trawl for red king crab (Paralithodes camtschaticus)

The relative abundance of Bristol Bay red king crab (Paralithodes camtschaticus) is estimated each year for stock assessment by using catch-per-swept-area data collected on the Alaska Fisheries Science Center’s annual eastern Bering Sea bottom trawl survey. To estimate survey trawl capture efficiency for red king crab, an experiment was conducted with an auxiliary net (fitted with its own heavy chain-link footrope) that was attached beneath the trawl to capture crabs escaping under the survey trawl footrope. Capture probability was then estimated by fitting a model to the proportion of crabs captured and crab size data. For males, mean capture probability was 72% at 95 mm (carapace length), the size at which full vulnerability to the survey trawl is assigned in the current management model; 84.1% at 135 mm, the legal size for the fishery; and 93% at 184 mm, the maximum size observed in this study. For females, mean capture probability was 70% at 90 mm, the size at which full vulnerability to the survey trawl is assigned in the current management model, and 77% at 162 mm, the maximum size observed in this study. The precision of our estimates for each sex decreased for juveniles under 60 mm and for the largest crab because of small sample sizes. In situ data collected from trawl-mounted video cameras were used to determine the importance of various factors associated with the capture of individual crabs. Capture probability was significantly higher when a crab was standing when struck by the footrope, rather than crouching, and higher when a crab was hit along its body axis, rather than from the side. Capture probability also increased as a function of increasing crab size but decreased with increasing footrope distance from the bottom and when artificial light was provided for the video camera.

Daily rainfall along the United States Pacific Coast appears to conform to a square-root normal probability distribution

EXTRACT (SEE PDF FOR FULL ABSTRACT): As part of a study of climatic influences on landslide initiation, a statistical analysis of long-term (>40 years) records of daily rainfall from 24 Pacific coastal stations, from San Diego to Cape Flattery, disclosed an unexpected result - the square root of the daily rainfall closely approximates a normal distribution function. ... This paper illustrates the use of the square-root-normal distribution to analyze variations in precipitation along the mainland United States Pacific Coast with examples of orographic enhancement, rain shadows, and increase in precipitation frequency with geographic latitude.

Projective Limit Random Probabilities on Polish Spaces

A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space M(V) of probability measures on a given domain V. In principle, such distributions on the infinite-dimensional space M(V) can be constructed from their finite-dimensional marginals---the most prominent example being the construction of the Dirichlet process from finite-dimensional Dirichlet distributions. This approach is both intuitive and applicable to the construction of arbitrary distributions on M(V), but also hamstrung by a number of technical difficulties. We show how these difficulties can be resolved if the domain V is a Polish topological space, and give a representation theorem directly applicable to the construction of any probability distribution on M(V) whose first moment measure is well-defined. The proof draws on a projective limit theorem of Bochner, and on properties of set functions on Polish spaces to establish countable additivity of the resulting random probabilities.

Construction of a BAC library for Chinese amphioxus Branchiostoma belcheri and identification of clones containing Amphi-pax genes

Amphioxus is a crucial organism for the study of vertebrate evolution. Although a genomic BAC library of Branchiostoma floridae has been constructed, we report here another BAC library construction of its distant relative species Branchiostoma belcheri. The amphioxus BAC library established in present study consists of 45,312 clones arrayed in one hundred and eighteen 384-well plates. The average insert fragment size was 120 kb estimated by Pulsed Field Gel Electrophoresis (PFGE) analysis of 318 randomly selected clones. The representation of the library is about 12 equivalent to the genome, allowing a 99.9995% probability of recovering any specific sequence of interest. We further screened the library with 4 single copied Amphi-Pax genes and identified total of 26 positive clones with average of 6.5 clones for each gene. The result indicates this library is well suited for many applications and should also serve as a useful complemental resource for the scientific community.

Enhanced poisson sum representation for alpha-stable processes

In this paper we present Poisson sum series representations for α-stable (αS) random variables and a-stable processes, in particular concentrating on continuous-time autoregressive (CAR) models driven by α-stable Lévy processes. Our representations aim to provide a conditionally Gaussian framework, which will allow parameter estimation using Rao-Blackwellised versions of state of the art Bayesian computational methods such as particle filters and Markov chain Monte Carlo (MCMC). To overcome the issues due to truncation of the series, novel residual approximations are developed. Simulations demonstrate the potential of these Poisson sum representations for inference in otherwise intractable α-stable models. © 2011 IEEE.

Central representation of dynamics when manipulating handheld objects.

To explore the neural mechanisms related to representation of the manipulation dynamics of objects, we performed whole-brain fMRI while subjects balanced an object in stable and highly unstable states and while they balanced a rigid object and a flexible object in the same unstable state, in all cases without vision. In this way, we varied the extent to which an internal model of the manipulation dynamics was required in the moment-to-moment control of the object's orientation. We hypothesized that activity in primary motor cortex would reflect the amount of muscle activation under each condition. In contrast, we hypothesized that cerebellar activity would be more strongly related to the stability and complexity of the manipulation dynamics because the cerebellum has been implicated in internal model-based control. As hypothesized, the dynamics-related activation of the cerebellum was quite different from that of the primary motor cortex. Changes in cerebellar activity were much greater than would have been predicted from differences in muscle activation when the stability and complexity of the manipulation dynamics were contrasted. On the other hand, the activity of the primary motor cortex more closely resembled the mean motor output necessary to execute the task. We also discovered a small region near the anterior edge of the ipsilateral (right) inferior parietal lobule where activity was modulated with the complexity of the manipulation dynamics. We suggest that this is related to imagining the location and motion of an object with complex manipulation dynamics.