Sonographic Alteration of Lenticular Nucleus in Focal Task-Specific Dystonia of Musicians

2012 ◽  
Vol 9 (2) ◽  
pp. 99-103 ◽  
Author(s):  
Uwe Walter ◽  
Franziska Buttkus ◽  
Reiner Benecke ◽  
Annette Grossmann ◽  
Dirk Dressler ◽  
...  
Stroke ◽  
2006 ◽  
Vol 37 (9) ◽  
pp. 2242-2247 ◽  
Author(s):  
Qing Miao ◽  
Timo Paloneva ◽  
Seppo Tuisku ◽  
Susanna Roine ◽  
Minna Poyhonen ◽  
...  
Keyword(s):  

2000 ◽  
Vol 15 (2) ◽  
pp. 348-350 ◽  
Author(s):  
Steven Frucht ◽  
Stanley Fahn ◽  
Blair Ford
Keyword(s):  

Author(s):  
Lisa Vangsness ◽  
Michael E. Young

Recent publications have encouraged researchers to consider how metacognition affects users’ judgments of usability and workload by integrating metacognitive assessments with traditional testing paradigms. However, the repercussions of collecting these measures concurrently are unknown. We used a visual search task to determine how the frequency of metacognitive assessments affected metacognitive accuracy and performance. Frequent assessments did not impact performance on the focal task; however, they did reduce the accuracy of participants’ metacognitive judgments by about 7%. This finding suggests that researchers should consider context when selecting a metacognitive assessment strategy.


1996 ◽  
Vol 11 (6) ◽  
pp. 665-670 ◽  
Author(s):  
Valerie L. Soland ◽  
Kailash P. Bhatia ◽  
Maria A. Volonte ◽  
C. David Marsden
Keyword(s):  

2014 ◽  
Vol 121 (10) ◽  
pp. 1273-1279 ◽  
Author(s):  
Uwe Walter ◽  
Marta Skowrońska ◽  
Tomasz Litwin ◽  
Grażyna Maria Szpak ◽  
Katarzyna Jabłonka-Salach ◽  
...  

2021 ◽  
Author(s):  
Paula Ríos López ◽  
Andreas Widmann ◽  
Aurélie Bidet-Caulet ◽  
Nicole Wetzel

Everyday cognitive tasks are rarely performed in a quiet environment. Quite on the contrary, very diverse surrounding acoustic signals such as speech can involuntarily deviate our attention from the task at hand. Despite its tight relation to attentional processes, pupillometry remained a rather unexploited method to measure attention allocation towards irrelevant speech. In the present study, we registered changes in pupil diameter size to quantify the effect of meaningfulness of background speech upon performance in an attentional task. We recruited 41 native German speakers who had neither received formal instruction in French nor had extensive informal contact with this language. The focal task consisted of an auditory oddball task. Participants performed an animal sound duration discrimination task containing frequently repeated standard sounds and rarely presented deviant sounds while a story was read in German or (non-meaningful) French in the background. Our results revealed that, whereas effects of language meaningfulness on attention were not detectable at the behavioural level, participants’ pupil dilated more in response to the sounds of the auditory task when background speech was played in non-meaningful French compared to German, independent of sound type. This could suggest that semantic processing of the native language required attentional resources, which lead to fewer resources devoted to the processing of the sounds of the focal task. Our results highlight the potential of the pupil dilation response for the investigation of subtle cognitive processes that might not surface when only behaviour is measured.


2021 ◽  
Author(s):  
Alexander Moiseev ◽  
Anthony T Herdman ◽  
Urs Ribary

In MEG and EEG brain imaging research two popular approaches are often used for spatial localization of focal task- or stimuli-related brain activations. One is a so called MUSIC approach applied in the form of RAP or TRAP MUSIC algorithms. Another one is the beamformer approach, specifically multiple constrained minimum variance (MCMV) beamformer when dealing with significantly correlated activations. Either method is using its own source localizer functions. Considering simplicity, accuracy and computational efficiency both approaches have their advantages and disadvantages. In this study we introduce a novel set of so called Subspace MCMV (or SMCMV) beamformers whose localizer functions combine MUSIC and MCMV localizers. We show that in ideal situations where forward modeling, data recording and noise measurements are error-free, SMCMV localizers allow precise identification of n arbitrarily correlated sources irrespective to their strength in just n scans of the brain volume using RAP MUSIC type algorithm. We also demonstrate by extensive computer simulations that with respect to source localization errors and the total number of identified sources SMCMV outperforms both the TRAP MUSIC and MIA MCMV (which is the most accurate MCMV algorithm to our knowledge) in non-ideal practical situations, specifically when the noise covariance cannot be estimated precisely, signal to noise ratios are small, source correlations are significant and larger numbers of sources are involved.


2017 ◽  
Vol 7 (0) ◽  
pp. 365
Author(s):  
Amar S. Patel ◽  
Lucian Sulica ◽  
Steven J. Frucht
Keyword(s):  

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