Working memory load elicits attentional bias to threat

Anxious individuals tend to show attentional bias to threats and dangers; this is usually in-terpreted as a specific bias in threat-processing. However, they also tend to show general working memory and cognitive control impairments. We hypothesised that the lack of work-ing memory resources might contribute to attentional bias, by limiting anxious individuals’ ability to regulate their responses to emotional stimuli. If this is true, then loading working memory should elicit attentional bias to threat, even in non-anxious participants. We tested this hypothesis in two experiments, with participants unselected for anxiety. In Experiment 1, a phonological working memory load (remembering a string of digits) elicited an attentional bias to fear-conditioned Japanese words. In Experiment 2, a visuo-spatial working memory load (remembering a series of locations in a matrix of squares) elicited an attentional bias to emotional schematic faces. Results suggest that working memory and cognitive control may moderate the attentional bias to threat commonly observed in anxiety

Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap

This paper investigates the use of bootstrap-based bias correction of semi-parametric estimators of the long memory parameter in fractionally integrated processes. The re-sampling method involves the application of the sieve bootstrap to data pre-filtered by a preliminary semi-parametric estimate of the long memory parameter. Theoretical justification for using the bootstrap techniques to bias adjust log-periodogram and semi-parametric local Whittle estimators of the memory parameter is provided. Simulation evidence comparing the performance of the bootstrap bias correction with analytical bias correction techniques is also presented. The bootstrap method is shown to produce notable bias reductions, in particular when applied to an estimator for which analytical adjustments have already been used. The empirical coverage of confidence intervals based on the bias-adjusted estimators is very close to the nominal, for a reasonably large sample size, more so than for the comparable analytically adjusted estimators. The precision of inferences (as measured by interval length) is also greater when the bootstrap is used to bias correct rather than analytical adjustments.Comment: 38 page

Tests of Bias in Log-Periodogram Regression

This paper proposes simple Hausman-type tests to check for bias in the log-periodogram regression of a time series believed to be long memory. The statistics are asymptotically standard normal on the null hypothesis that no bias is present, and the tests are consistent.Long memory, log periodogram regression, Hausman test.

Higher-Order Kernel Semiparametric M-Estimation of Long Memory

Econometric interest in the possibility of long memory has developed as a flexible alternative to, or compromise between, the usual short memory or unit root prescriptions, for example in the context of modelling cointegrating or other relationships and in describing the dependence structure of nonlinear functions of financial returns. Semiparametric methods of estimating the memory parameter can avoid bias incurred by misspecification of the short memory component. We introduce a broad class of such semiparametric estimates that also covers pooling across frequencies. A leading "Box-Club" sub-class, indexed by a single tuning parameter, interpolates between the popular local log periodogram and local Whittle estimates, leading to a smooth interpolation of asymptotic variances. The bias of these two estimates also differs to higher order, and we also show how bias, and asymptotic mean square error, can be reduced, across the class of estimates studied, by means of a suitable version of higher-order kernels. We thence calculate an optimal bandwidth (the number of low frequency periodogram ordinates employed) which minimizes this mean squared error. Finite sample performance is studied in a small Monte Carlo experiment, and an empirical application to intra-day foreign exchange returns is included.Long memory, semiparametric methods, higher-order kernel, M-estimation, bias, mean-squared error.

Attachment-related expectations and mother-referent memory bias in middle childhood

Understanding the cognitive processing of attachment-relevant information has become a major focus of attachment research. Previous research demonstrated links between attachment and memory for attachment-related information, but results were contradictory and did not control for mood-effects. The current study aimed to provide a conceptual framework to capture inconsistencies. A straightforward memory bias hypothesis was derived and tested. Fifty children (aged 10-12) completed questionnaires assessing confidence in maternal support and depressive symptoms, and a memory task in which they recalled positive and negative words that referred to previous interactions with mother. Less confidence in maternal support and more depressive symptoms were linked to a more negative mother-related memory bias. The effect of confidence in maternal support remained marginally significant when controlling for depressive symptoms, explaining the initial effect of depressive symptoms. These findings support attachment theory's hypothesis that attachment-relevant information is processed in an attachment expectation-congruent way

Variable-gap bias structure for magnetic bubble memory package

Size and thickness of field adjusting plate can be varied. Memory chip specification is relaxed, since chips in structure need not be matched in bias margin

Polarization memory in the nonpolar magnetic ground state of multiferroic CuFeO2

We investigate polarization memory effects in single-crystal CuFeO2, which has a magnetically-induced ferroelectric phase at low temperatures and applied B fields between 7.5 and 13 T. Following electrical poling of the ferroelectric phase, we find that the nonpolar collinear antiferromagnetic ground state at B = 0 T retains a strong memory of the polarization magnitude and direction, such that upon re-entering the ferroelectric phase a net polarization of comparable magnitude to the initial polarization is recovered in the absence of external bias. This memory effect is very robust: in pulsed-magnetic-field measurements, several pulses into the ferroelectric phase with reverse bias are required to switch the polarization direction, with significant switching only seen after the system is driven out of the ferroelectric phase and ground state either magnetically (by application of B > 13 T) or thermally. The memory effect is also largely insensitive to the magnetoelastic domain composition, since no change in the memory effect is observed for a sample driven into a single-domain state by application of stress in the [1-10] direction. On the basis of Monte Carlo simulations of the ground state spin configurations, we propose that the memory effect is due to the existence of helical domain walls within the nonpolar collinear antiferromagnetic ground state, which would retain the helicity of the polar phase for certain magnetothermal histories.Comment: 9 pages, 7 figure

Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists

Language Models (LMs) are important components in several Natural Language Processing systems. Recurrent Neural Network LMs composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results. However, these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading and a bias towards more recent information. In this paper we demonstrate an effective mechanism for retrieving information in a memory augmented LSTM LM based on attending to information in memory in proportion to the number of timesteps the LSTM gating mechanism persisted the information