We argue that the S =1/ 2 kagome antiferromagnet undergoes a quantum phase transition when the Dzyaloshinskii-Moriya coupling is increased. For D Ͻ D c the system is in a moment-free phase, and for D Ͼ D c the system develops antiferromagnetic long-range order. The quantum critical point is found to be D c Ӎ 0.1J using exact diagonalizations and finite-size scaling. This suggests that the kagome compound ZnCu 3 ͑OH͒ 6 Cl 3 may be in a quantum critical region controlled by this fixed point.

Clustering and switching are hypothesised to reflect the automatic and controlled components in category fluency, respectively, but how they are associated with cognitive functions has not been fully elucidated, due to several uncertainties. (1) The conventional scoring method that segregates responses by semantic categories could not optimally dissociate the automatic and controlled components. (2) The temporal structure of individual responses, as characterised by mean retrieval time (MRT) and mean switching time (MST), has seldom been analysed alongside the more well-studied variables, cluster size (CS) and number of switches (NS). (3) Most studies examined only one to a few semantic categories, raising concerns of generalisability. This study built upon a distance-based automatic clustering procedure, referred to as temporal–semantic distance procedure, to thoroughly characterise the category fluency performance. Linear mixed-effects (LME) modelling was applied to re-examine the differential associations of clustering and switching with cognitive functions with a sample of 80 university students. Our results revealed that although lexical retrieval speed (LRS) is clearly the determining factor for effective clustering and switching, matrix reasoning and processing speed also have significant roles to play, possibly in the processes of identifying and validating the semantic relationships. Interestingly, total fluency score was accurately predicted by the four clustering/switching indices alone; including the cognitive variables did not significantly improve the prediction. These findings underline the importance of the clustering and switching indices in explaining the category fluency performance and the cognitive demands in category fluency.

A visual speller is a brain-computer interface that empowers users with limited motor functionality to input text into a computer by measuring their electroencephalographic responses to visual stimuli. Most prior research on visual spellers has focused on input of alphabetic text. Adapting a speller for other types of segmental or syllabic script is straightforward because such scripts comprise sufficiently few characters that they may all be displayed to the user simultaneously. Logographic scripts, such as Chinese hanzi, however, impose a challenge: How should the thousands of Chinese characters be displayed to the user? Here, we present a visual speller, based on Farwell and Donchin's P300 Speller, for Chinese character input. The speller uses a novel shape-based method called the First-Last, or FLAST, method to encode more than 7,000 Chinese characters. Characters are input by selecting two components, from a set of 56 distinct components, that match the shape of the target character, followed by selection of the character itself. At the input speed of one character per 107 s, 24 able-bodied participants achieved mean online accuracy of 82.8% per component selection and 63.5% per character input. At the faster input speed of one character per 77 s, mean online accuracy was 59.4% per component selection and 33.3% per character input.

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