Optimism bias and its relation to scenario valence, gender, sociality, and insecure attachment.

Optimism bias refers to the tendency to display unjustified high/low expectations of future positive/negative events. This study asked 202 participants to estimate the likelihood of 96 different events. We investigated optimism biases for both oneself and the general population, and how these biases are influenced by gender, valence of the event, sociality of the event, as well as attachment anxiety and attachment avoidance. We found that sociality interacted with gender, with the difference in optimism bias for social vs. alone events being larger among women than among men. Attachment anxiety mainly reduced the optimism bias among men deliberating over future alone situations, while attachment avoidance primarily reduced optimism bias among female respondents deliberating over future social interactions. These results may have implications for the well-being and motivation of differently attached men and women and ultimately inspire psychotherapy interventions

Enhanced sensitivity to optimistic cues is manifested in brain structure: A voxel-based morphometry study

Recent research shows that congruent outcomes are more rapidly (and incongruent less rapidly) detected when individuals receive optimistic rather than pessimistic cues, an effect that was termed optimism robustness. In the current voxel-based morphometry study, we examined whether optimism robustness has a counterpart in brain structure. The participants’ task was to detect two different letters (symbolizing monetary gain or loss) in a visual search matrix. Prior to each onset of the search matrix, two different verbal cues informed our participants about a high probability to gain (optimistic expectancy) or lose (pessimistic expectancy) money. The target presented was either congruent or incongruent with these induced expectancies. Optimism robustness revealed in the participants’ reaction times correlated positively with gray matter volume (GMV) in brain regions involved in selective attention (medial visual association area, intraparietal sulcus), emphasizing the strong intertwinement of optimistic expectancies and attention deployment. In addition, GMV in the primary visual cortex diminished with increasing optimism robustness, in line with the interpretation of optimism robustness arising from a global, context-oriented perception. Future studies should address the malleability of these structural correlates of optimism robustness. Our results may assist in the identification of treatment targets in depression

Predictive modeling of optimism bias using gray matter cortical thickness.

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual's (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R2 = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex-areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being

Structural Change in (Economic) Time Series

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed.Comment: 12 pages, 6 figure

Height Fluctuations and Intermittency of V2O5V_2 O_5 Films by Atomic Force Microscopy

The spatial scaling law and intermittency of the $V_2 O_5$ surface roughness by atomic force microscopy has been investigated. The intermittency of the height fluctuations has been checked by two different methods, first, by measuring scaling exponent of q-th moment of height-difference fluctuations i.e. $C_q =$ and the second, by defining generating function $Z(q,N)$ and generalized multi-fractal dimension $D_q$. These methods predict that there is no intermittency in the height fluctuations. The observed roughness and dynamical exponents can be explained by the numerical simulation on the basis of forced Kuramoto-Sivashinsky equation.Comment: 6 pages (two columns), 11 eps. figures, late

Structural breaks in panel data: large number of panels and short length time series

The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T, the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N→∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises

A Grhl2-dependent gene network controls trophoblast branching morphogenesis

Healthy placental development is essential for reproductive success; failure of the feto-maternal interface results in pre-eclampsia and intrauterine growth retardation. We found that grainyhead-like 2 (GRHL2), a CP2-type transcription factor, is highly expressed in chorionic trophoblast cells, including basal chorionic trophoblast (BCT) cells located at the chorioallantoic interface in murine placentas. Placentas from Grhl2-deficient mouse embryos displayed defects in BCT cell polarity and basement membrane integrity at the chorioallantoic interface, as well as a severe disruption of labyrinth branching morphogenesis. Selective Grhl2 inactivation only in epiblast-derived cells rescued all placental defects but phenocopied intraembryonic defects observed in global Grhl2 deficiency, implying the importance of Grhl2 activity in trophectoderm-derived cells. ChIP-seq identified 5282 GRHL2 binding sites in placental tissue. By integrating these data with placental gene expression profiles, we identified direct and indirect Grhl2 targets and found a marked enrichment of GRHL2 binding adjacent to genes downregulated in Grhl2(-/-) placentas, which encoded known regulators of placental development and epithelial morphogenesis. These genes included that encoding the serine protease inhibitor Kunitz type 1 (Spint1), which regulates BCT cell integrity and labyrinth formation. In human placenta, we found that human orthologs of murine GRHL2 and its targets displayed co-regulation and were expressed in trophoblast cells in a similar domain as in mouse placenta. Our data indicate that a conserved Grhl2-coordinated gene network controls trophoblast branching morphogenesis, thereby facilitating development of the site of feto-maternal exchange. This might have implications for syndromes related to placental dysfunction