11,583 research outputs found

    Moderate threat causes longer lasting disruption to processing in anxious individuals

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    Anxiety is associated with increased attentional capture by threat. Previous studies have used simultaneous or briefly separated (<1 s) presentation of threat distractors and target stimuli. Here, we tested the hypothesis that high trait anxious participants would show a longer time window within which distractors cause disruption to subsequent task processing, and that this would particularly be observed for stimuli of moderate or ambiguous threat value. A novel temporally separated emotional distractor task was used. Face or house distractors were presented for 250 ms at short (∼1.6 s) or long (∼3 s) intervals prior to a letter string comprising Xs or Ns. Trait anxiety was associated with slowed identification of letter strings presented at long intervals after face distractors with part surprise/part fear expressions. In other words, these distractors had an impact on high anxious individuals' speed of target identification seconds after their offset. This was associated with increased activity in the fusiform gyrus and amygdala and reduced dorsal anterior cingulate recruitment. This pattern of activity may reflect impoverished recruitment of reactive control mechanisms to damp down stimulus-specific processing in subcortical and higher visual regions. These findings have implications for understanding how threat-related attentional biases in anxiety may lead to dysfunction in everyday settings where stimuli of moderate, potentially ambiguous, threat value such as those used here are fairly common, and where attentional disruption lasting several seconds may have a profound impact

    NAG: Network for Adversary Generation

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    Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the perturbations. However, for a given classifier, they generate one perturbation at a time, which is a single instance from the manifold of adversarial perturbations. Also, in order to build robust models, it is essential to explore the manifold of adversarial perturbations. In this paper, we propose for the first time, a generative approach to model the distribution of adversarial perturbations. The architecture of the proposed model is inspired from that of GANs and is trained using fooling and diversity objectives. Our trained generator network attempts to capture the distribution of adversarial perturbations for a given classifier and readily generates a wide variety of such perturbations. Our experimental evaluation demonstrates that perturbations crafted by our model (i) achieve state-of-the-art fooling rates, (ii) exhibit wide variety and (iii) deliver excellent cross model generalizability. Our work can be deemed as an important step in the process of inferring about the complex manifolds of adversarial perturbations.Comment: CVPR 201

    Monetary Policy Lag, Zero Lower Bound, and Inflation Targeting

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    Although the concept of monetary policy lag has historical roots deep in the monetary economics literature, relatively little attention has been paid to the idea. In this paper, we build on Svensson’s (1997) inflation targeting framework by explicitly taking into account the lagged effect of monetary policy and characterize the optimal monetary policy reaction function both in the absence and in the presence of the zero lower bound on the nominal interest rate. We numerically show the function to be more aggressive and more pre-emptive with the lagged effect than without it. We also characterize the long-run stabilization cost to the central bank by explicitly taking into account the lagged effect of monetary policy. It turns out that, in the presence of the zero lower bound constraint, the long-run stabilization cost is higher with the lagged effect than the case without it. This result suggests that the central bank and/or the government should set a relatively high inflation target when confronted with a relatively long monetary policy lag. This can be interpreted as another justification for targeting a positive inflation rate in the long-run.Inflation targets; Monetary policy framework; Monetary policy implementation
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