Gaze fixation improves the stability of expert juggling

Novice and expert jugglers employ different visuomotor strategies: whereas novices look at the balls around their zeniths, experts tend to fixate their gaze at a central location within the pattern (so-called gaze-through). A gaze-through strategy may reflect visuomotor parsimony, i.e., the use of simpler visuomotor (oculomotor and/or attentional) strategies as afforded by superior tossing accuracy and error corrections. In addition, the more stable gaze during a gaze-through strategy may result in more accurate movement planning by providing a stable base for gaze-centered neural coding of ball motion and movement plans or for shifts in attention. To determine whether a stable gaze might indeed have such beneficial effects on juggling, we examined juggling variability during 3-ball cascade juggling with and without constrained gaze fixation (at various depths) in expert performers (n = 5). Novice jugglers were included (n = 5) for comparison, even though our predictions pertained specifically to expert juggling. We indeed observed that experts, but not novices, juggled significantly less variable when fixating, compared to unconstrained viewing. Thus, while visuomotor parsimony might still contribute to the emergence of a gaze-through strategy, this study highlights an additional role for improved movement planning. This role may be engendered by gaze-centered coding and/or attentional control mechanisms in the brain

Herbal Medicines for Parkinson's Disease: A Systematic Review of Randomized Controlled Trials

OBJECTIVE: We conducted systematic review to evaluate current evidence of herbal medicines (HMs) for Parkinson's disease (PD). METHODS: Along with hand searches, relevant literatures were located from the electronic databases including CENTRAL, MEDLINE, EMBASE, CINAHL, AMED, PsycInfo, CNKI, 7 Korean Medical Databases and J-East until August, 2010 without language and publication status. Randomized controlled trials (RCTs), quasi-randomized controlled trials and randomized crossover trials, which evaluate HMs for idiopathic PD were selected for this review. Two independent authors extracted data from the relevant literatures and any disagreement was solved by discussion. RESULTS: From the 3432 of relevant literatures, 64 were included. We failed to suggest overall estimates of treatment effects on PD because of the wide heterogeneity of used herbal recipes and study designs in the included studies. When compared with placebo, specific effects were not observed in favor of HMs definitely. Direct comparison with conventional drugs suggested that there was no evidence of better effect for HMs. Many studies compared combination therapy with single active drugs and combination therapy showed significant improvement in PD related outcomes and decrease in the dose of anti-Parkinson's drugs with low adverse events rate. CONCLUSION: Currently, there is no conclusive evidence about the effectiveness and efficacy of HMs on PD. For establishing clinical evidence of HMs on PD, rigorous RCTs with sufficient statistical power should be promoted in future

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient