Reconciling the influence of task-set switching and motor inhibition processes on stop signal after-effects.

Executive response functions can be affected by preceding events, even if they are no longer associated with the current task at hand. For example, studies utilizing the stop signal task have reported slower response times to "GO" stimuli when the preceding trial involved the presentation of a "STOP" signal. However, the neural mechanisms that underlie this behavioral after-effect are unclear. To address this, behavioral and electroencephalography (EEG) measures were examined in 18 young adults (18-30 years) on "GO" trials following a previously "Successful Inhibition" trial (pSI), a previously "Failed Inhibition" trial (pFI), and a previous "GO" trial (pGO). Like previous research, slower response times were observed during both pSI and pFI trials (i.e., "GO" trials that were preceded by a successful and unsuccessful inhibition trial, respectively) compared to pGO trials (i.e., "GO" trials that were preceded by another "GO" trial). Interestingly, response time slowing was greater during pSI trials compared to pFI trials, suggesting executive control is influenced by both task set switching and persisting motor inhibition processes. Follow-up behavioral analyses indicated that these effects resulted from between-trial control adjustments rather than repetition priming effects. Analyses of inter-electrode coherence (IEC) and inter-trial coherence (ITC) indicated that both pSI and pFI trials showed greater phase synchrony during the inter-trial interval compared to pGO trials. Unlike the IEC findings, differential ITC was present within the beta and alpha frequency bands in line with the observed behavior (pSI > pFI > pGO), suggestive of more consistent phase synchrony involving motor inhibition processes during the ITI at a regional level. These findings suggest that between-trial control adjustments involved with task-set switching and motor inhibition processes influence subsequent performance, providing new insights into the dynamic nature of executive control

How Not to Think About Managed Care

The claim of this Article is that the concept of managed care, like many concepts now prominent in commentary about medical care finance and delivery in the United States, is incoherent and thus a barrier to useful analysis. To demonstrate this conclusion, we first discuss the managerial context in which managed care claims have arisen and outline the diverse trends to which the category is regularly and confusingly applied. We then suggest an alternative approach to characterizing recent changes in medical care and show how this approach alters and deepens our understanding of recent economic and political developments. We conclude by arguing for more neutral categories to make sense of past and projected developments in methods of reimbursement, techniques of management, and organizational structures

Cross-strait colonialism : Marxism and the construction of Taiwanese nationalism

This thesis explores the history of the Taiwanese independence movement, particularly focusing upon the relationship between the creation of a Taiwanese nationalism and the communist movement. This is accomplished via examining the earliest historiographic writing centered on Taiwan and comparing it with the dominant views of Taiwan in Chinese Nationalist Party (KMT) and Chinese Communist Party (CCP) historiography, which demonstrates how the original Taiwanese nationalism was derived from a Marxist historical narrative. The history of this socialist nationalism is then deployed as a foil to critique both KMT and CCP narratives of Taiwan and its place in China’s history. The history of the Taiwanese Communist Party is explored in depth, particularly its relationship with the independence movement, the CCP, and the Japanese Communist Party. The communist origin of Taiwanese nationalism destabilizes the dominant discourse of “One China,” and illustrates how nationalism is sometimes, paradoxically, the only thing that can resist other more oppressive nationalisms.Asian Studie

Challenges in Markov chain Monte Carlo for Bayesian neural networks

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, this paper shows that a non-converged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable predictive posterior of the output of the neural network. Classification examples based on multilayer perceptrons showcase highly accurate predictive posteriors. The postulate of limited scope for MCMC developments in BNNs is partially valid; an asymptotically exact parameter posterior seems less plausible, yet an accurate predictive posterior is a tenable research avenue