Goto

Collaborating Authors

Unifying the Sensory and Motor Components of Sensorimotor Adaptation

Neural Information Processing Systems

Adaptation of visually guided reaching movements in novel visuomotor environments (e.g.wearing prism goggles) comprises not only motor adaptation but also substantial sensory adaptation, corresponding to shifts in the perceived spatial location of visual and proprioceptive cues. Previous computational modelsof the sensory component of visuomotor adaptation have assumed that it is driven purely by the discrepancy introduced between visual andproprioceptive estimates of hand position and is independent of any motor component of adaptation. We instead propose a unified model in which sensory and motor adaptation are jointly driven by optimal Bayesian estimation of the sensory and motor contributions to perceived errors. Our model is able to account for patterns of performance errors during visuomotor adaptationas well as the subsequent perceptual aftereffects. This unified model also makes the surprising prediction that force field adaptation willelicit similar perceptual shifts, even though there is never any discrepancy between visual and proprioceptive observations. We confirm this prediction with an experiment.


Man Whose Family Was Killed Charged With Causing Disturbance

U.S. News

The Telegram & Gazette reports that according to court documents, Moses Bermudez shouted at staff at the shop near his West Brookfield home Sunday and made sexually suggestive comments to a female employee.


Sparse Linear Models applied to Power Quality Disturbance Classification

arXiv.org Machine Learning

Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other quasi-stationary signals, PQ disturbances can be decomposed into time-frequency dependent components by using time-frequency or time-scale transforms, also known as dictionaries. These dictionaries are used in the feature extraction step in pattern recognition systems. Short-time Fourier, Wavelets and Stockwell transforms are some of the most common dictionaries used in the PQ community, aiming to achieve a better signal representation. To the best of our knowledge, previous works about PQ disturbance classification have been restricted to the use of one among several available dictionaries. Taking advantage of the theory behind sparse linear models (SLM), we introduce a sparse method for PQ representation, starting from overcomplete dictionaries. In particular, we apply Group Lasso. We employ different types of time-frequency (or time-scale) dictionaries to characterize the PQ disturbances, and evaluate their performance under different pattern recognition algorithms. We show that the SLM reduce the PQ classification complexity promoting sparse basis selection, and improving the classification accuracy.


Fats Domino's Career Included 'Racial Disturbance' in 1956

U.S. News

"I remember Fats as being a down-to-earth, kind man," Holcomb wrote in an e-mail exchange. "I actually have some 8 mm footage of the night, including a shot of my then-wife Rita and another pretty lady sitting on Fats' lap, with all three laughing.


The Latest: US Church Groups Stranded by Haiti Disturbances

U.S. News

Chapin United Methodist Church in South Carolina posted online that its mission team is safe but stranded. Marcy Kenny is assimilation minister for the church and told The State newspaper that the group is hoping the unrest will abate enough for them to safely make it to the airport.