Neural activity associated with the passive prediction of ambiguity and risk for aversive events.

In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity (uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are completely unknown and unknowable. We modified previously learned pavlovian CS+ stimuli such that they became an ambiguous cue and contrasted evoked brain activity both with an unmodified predictive CS+ (risky cue), and a cue that conveyed no information about outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search for hidden information during outcome anticipation is both necessary and meaningful.

The influence of stacks on flow patterns and stratification associated with natural ventilation

We investigate the steady state natural ventilation of an enclosed space in which vent A, located at height hA above the floor, is connected to a vertical stack with a termination at height H, while the second vent, B, at height hB above the floor, connects directly to the exterior. We first examine the flow regimes which develop with a distributed source of heating at the base of the space. If hBhB>hA, then two different flow regimes may develop. Either (i) there is inflow through vent B and outflow through vent A, or (ii) the flow reverses, with inflow down the stack into vent A and outflow through vent B. With inflow through vent A, the internal temperature and ventilation rate depend on the relative height of the two vents, A and B, while with inflow through vent B, they depend on the height of vent B relative to the height of the termination of the stack H. With a point source of heating, a similar transition occurs, with a unique flow regime when vent B is lower than vent A, and two possible regimes with vent B higher than vent A. In general, with a point source of buoyancy, each steady state is characterised by a two-layer density stratification. Depending on the relative heights of the two vents, in the case of outflow through vent A connected to the stack, the interface between these layers may lie above, at the same level as or below vent A, leading to discharge of either pure upper layer, a mixture of upper and lower layer, or pure lower layer fluid. In the case of inflow through vent A connected to the stack, the interface always lies below the outflow vent B. Also, in this case, if the inflow vent A lies above the interface, then the lower layer becomes of intermediate density between the upper layer and the external fluid, whereas if the interface lies above the inflow vent A, then the lower layer is composed purely of external fluid. We develop expressions to predict the transitions between these flow regimes, in terms of the heights and areas of the two vents and the stack, and we successfully test these with new laboratory experiments. We conclude with a discussion of the implications of our results for real buildings.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Entrepreneurial innovation problems associated with the dynamic growth of university spin-outs in China: A capabilities perspective

This research aims to develop a capabilities-based conceptual framework in order to study the stage-specific innovation problems associated with the dynamic growth process of university spin-outs (hereafter referred to as USOs) in China. Based on the existing literature, pilot cases and five critical cases, this study attempts to explore the interconnections between the entrepreneurial innovation problems and the configuration of innovative capabilities (that acquire, mobilise and re-configure the key resources) throughout the lifecycle of a firm in four growth phases. This paper aims to contribute to the literature in a holistic manner by providing a theoretical discussion of USOs' development through adding evidence from a rapid growth emerging economy. To date, studies that have investigated the development of USOs in China recognised the heterogeneity of USOs in terms of capabilities still remain sparse. Addressing this research gap will be of great interest to entrepreneurs, policy makers and venture investors. © Copyright 2010 Inderscience Enterprises Ltd.