The dynamic effects of age-related stereotype threat on explicit and implicit memory performance in older adults

While an awareness of age-related changes in memory may help older adults gain insight into their own cognitive abilities, it may also have a negative impact on memory performance through a mechanism of stereotype threat (ST). The consequence of ST is under-performance in abilities related to the stereotype. Here, we examined the degree to which explicit and implicit memory were affected by ST across a wide age-range. We found that explicit memory was affected by ST, but only in an Early-Aging group (mean age 67.83), and not in a Later-Aging group (mean age 84.59). Implicit memory was not affected in either the Early or Later Aging group. These results demonstrate that ST for age-related memory decline affects memory processes requiring controlled retrieval while sparing item encoding. Furthermore, this form of ST appears to dissipate as aging progresses. These results have implications for understanding psychological development across the span of aging.

Prefrontal inhibition of threat processing reduces working memory interference

Bottom-up processes can interrupt ongoing cognitive processing in order to adaptively respond to emotional stimuli of high potential significance, such as those that threaten wellbeing. However it is vital that this interference can be modulated in certain contexts to focus on current tasks. Deficits in the ability to maintain the appropriate balance between cognitive and emotional demands can severely impact on day-to-day activities. This fMRI study examined this interaction between threat processing and cognition; 18 adult participants performed a visuospatial working memory (WM) task with two load conditions, in the presence and absence of anxiety induction by threat of electric shock. Threat of shock interfered with performance in the low cognitive load condition; however interference was eradicated under high load, consistent with engagement of emotion regulation mechanisms. Under low load the amygdala showed significant activation to threat of shock that was modulated by high cognitive load. A directed top-down control contrast identified two regions associated with top-down control; ventrolateral PFC and dorsal ACC. Dynamic causal modeling provided further evidence that under high cognitive load, top-down inhibition is exerted on the amygdala and its outputs to prefrontal regions. Additionally, we hypothesized that individual differences in a separate, non-emotional top-down control task would predict the recruitment of dorsal ACC and ventrolateral PFC during top-down control of threat. Consistent with this, performance on a separate dichotic listening task predicted dorsal ACC and ventrolateral PFC activation during high WM load under threat of shock, though activation in these regions did not directly correlate with WM performance. Together, the findings suggest that under high cognitive load and threat, top-down control is exerted by dACC and vlPFC to inhibit threat processing, thus enabling WM performance without threat-related interference.

Real estate markets and the macroeconomy: a dynamic coherence framework

This paper analyzes the dynamic interactions between real estate markets, in the US and the UK and their macroeconomic environments. We apply a new approach based on a dynamic coherence function (DCF) to study these interactions bringing together different real estate markets (the securitized market, the commercial market and the residential market). The results suggest that there is a common trend that drives the different real estate markets in the UK and the US, particularly in the long run, since they have a similar shape of the DCF. We also find that, in the US, wealth and housing expenditure channels are very conductive during real estate crises. However, in the UK, only the wealth effect is significant as a transmission channel during real estate market downturns. In addition, real estate markets in the UK and the US react differently to institutional shocks. This brings some insights on the conduct of monetary policy in order to avoid disturbances in real estate markets.

Dynamic group communication for large-scale parallel data mining

Exascale systems are the next frontier in high-performance computing and are expected to deliver a performance of the order of 10^18 operations per second using massive multicore processors. Very large- and extreme-scale parallel systems pose critical algorithmic challenges, especially related to concurrency, locality and the need to avoid global communication patterns. This work investigates a novel protocol for dynamic group communication that can be used to remove the global communication requirement and to reduce the communication cost in parallel formulations of iterative data mining algorithms. The protocol is used to provide a communication-efficient parallel formulation of the k-means algorithm for cluster analysis. The approach is based on a collective communication operation for dynamic groups of processes and exploits non-uniform data distributions. Non-uniform data distributions can be either found in real-world distributed applications or induced by means of multidimensional binary search trees. The analysis of the proposed dynamic group communication protocol has shown that it does not introduce significant communication overhead. The parallel clustering algorithm has also been extended to accommodate an approximation error, which allows a further reduction of the communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing elements.