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Facial expressions, Charles Darwin argued in The Expression of the Emotions in Man and Animals, are a universal window into emotion. But new research challenges that notion, showing that east Asian people struggle to recognise facial expressions that western Caucasians attribute to fear and disgust. By focusing on eyes and brows, Asians miss subtle cues conveyed via the mouth. (13 August 2009 - New Scientist)

One of the mysteries of gambling is that even when we should know we're going to lose, we somehow think we're going to win. Dr. Luke Clark, from the Department of Experimental Psychology at the University of Cambridge, may have discovered one of the reasons why. Using MRI, he studied brain activity in people gambling, looking particularly at "near misses" in which a loss seems close to a win. He found that the brain activated the same reward system that is activated in a real win, despite the fact that people report that these near misses are unpleasant. (CBC Radio | Quirks & Quarks | February 21, 2009)

another interesting study from the group at Wellcome Center group at University College associated with Ray Dolan - cognitive neuroscience that is directly relevant to our current economic and political reality: (Deric Bownds' MindBlog)

Is it to be full understanding or nothing? I should think there would be some value in enough understanding to make useful predictions. His position is that a scientific understanding of man is undesirable, but I thinnk there is something very desirable about useful predictions.

A new study says so-called "heavy multitaskers" have trouble tuning out distractions and switching tasks compared with those who multitask less. And there's evidence that multitasking may weaken cognitive ability. Stanford University professor Clifford Nass explains the work.

Tsien, based at Princeton University in New Jersey at the time, named his creation Doogie after the teenage genius in the television programme Doogie Howser, MD. The work was one of the earliest examples of neuroscientists using genetic engineering to generate cognitively enhanced animals in a bid to understand memory and learning.

A novel bioinformatics approach can predict vaccine effectiveness for the influenza season, and indicates that the current vaccines are likely to be effective against H3N2 flu viruses in the U.S. 2017/2018 flu season, according to research published online Nov. 29 in F1000 Research. Slobodan Paessler, D.V.M., Ph.D., from the University of Texas Medical Branch, and Veljko Veljkovic, Ph.D., from Biomed Protection, both in Galveston, Texas, used a bioinformatics platform to predict vaccine effectiveness for the 2017/2018 influenza season in the United States. The hemagglutinin HA1 region of 251 and 113 human H3N2 viruses collected in Australia and the United States from July to September 2017 were analyzed. The informational spectrum method-based phylogenetic analysis of H3N2 viruses was performed to serve as a base for predicting vaccine effectiveness. The researchers found that analyses of Australian viruses generated two clusters; the vaccine virus was placed in the smaller group. As a result, the vaccine was predicted not to be efficient against most Australian H3N2 viruses in the 2017 flu season; low vaccine effectiveness was reported in Australia in the 2017 flu season in accordance with this prediction. The U.S. H3N2 viruses were also grouped into two clusters, but the vaccine virus was placed in the largest cluster encompassing 71 percent of analyzed viruses. Consequently, the vaccine effectiveness is expected not to be suboptimal in the United States.