Mechanisms of anxiety related attentional biases in children with Autism Spectrum Disorder

Children with autism spectrum disorder (ASD) have high levels of anxiety. It is unclear whether they exhibit threat-related attentional biases commensurate with anxiety disorders as manifest in non-ASD populations, such as facilitated attention toward, and difficulties disengaging engaging from, threatening stimuli. Ninety children, 45 cognitively able with ASD and 45 age, perceptual-IQ, and gender matched typically developing children, aged 7–12 years, were administered a visual dot probe task using threatening facial pictures. Parent-reported anxiety symptoms were also collected. Children with ASD showed similarly high levels of anxiety compared with normative data from an anxiety disordered sample. Children with ASD had higher levels of parent-reported anxiety but did not show differences in disengaging from, or facilitated attention toward, threatening facial stimuli compared with typically developing children. In contrast to previously published studies of anxious children, in this study there were no differences in attentional biases in children with ASD meeting clinical cutoff for anxiety and those who did not. There were no correlations between attentional biases and anxiety symptoms and no gender differences. These findings indicate the cognitive mechanisms underlying anxiety in cognitively able children with ASD could differ from those commonly found in anxious children which may have implications for both understanding the aetiology of anxiety in ASD and for anxiety interventions

Is optimistic bias influenced by control or delay?

Optimistic bias is a commonly observed but poorly explained phenomenon. Our aim was to determine whether optimistic bias varied according to the nature of the event. Two event characteristics were explored: control and delay. A sample of 100 participants aged 18–30 years was randomly selected from the local residential telephone directory. Respondents were interviewed over the telephone. The highly structured interview schedule assessed respondents' perceptions of their own risk, and the risk of an average person of their age and sex for experiencing four negative life events: developing skin cancer, being involved in a serious car accident as the driver, being involved in a serious car accident as a passenger and having to wear a hearing aid. It also assessed respondents' perceptions of control and delay for each event. Data analysis using a repeated-measures MANOVA showed that optimistic bias occurred for all four events. Optimistic bias was significantly greater for the two events high in control (skin cancer and accident as the driver) than for those low in control (accident as a passenger and hearing aid). Delay was not related to the magnitude of optimistic bias. These findings have implications for health promotion campaigns and self-protective behaviors.

Optimistic bias for negative and positive events.

Presents results of an investigation into the occurrence of optimistic bias in relation to both positive and negative events, using absolute judgements to assess perceived risk for each of six events. Participants were asked why they offered different ratings for themselves and for others. The results showed that optimistic bias is a pervasive phenomenon that occurs for both positive and negative events. It occurred for all six events when comparisons were made with an unspecified person of the same age and sex, and occurred for three of six events when comparisons were made with the same-sex best friend. Participants could provide information about their own behaviour that they felt justified their positive outlook; however, they implicitly assumed that the comparison target did not engage in the same behaviour. Concludes that risk-reduction education needs to be made personally relevant to the target audience.

Maintaining life satisfaction: the role of positive cognitive bias

Recent research into population standards of life satisfaction has revealed a remarkable level of uniformity, with the mean values for Western populations clustering at around three-quarters of the measurement scale maximum. While this seems to suggest the presence of a homeostatic mechanism for life satisfaction, the character of such a hypothetical device is uncertain. This paper proposes that well-being homeostasis is controlled by positive cognitive biases pertaining to the self. Most particular in this regard are the positive biases in relation to self-esteem, control and optimism. Past controversies in relation to this proposition are reviewed and resolved in favour of the proposed mechanism. The empirical data to support this hypothesis are discussed in the context of perceived well-being as an adaptive human attribute.

Publication bias in the economic freedom and economic growth literature

The impact of institutions on economic performance has attracted significant attention from researchers, as well as from policy reformers. A rapidly growing area in this literature is the impact of economic freedom on economic growth. The aim of this paper was to explore publication bias in this literature by means of traditional funnel plots, meta‐significance testing, as well as by bootstrapping these meta‐significance tests. When all the available estimates are combined and averaged, there seems to be evidence of a genuine and positive economic freedom – economic growth effect. However, it is also shown that the economic freedom – economic growth literature is tainted strongly with publication bias. The existence of publication bias makes it difficult to identify the magnitude of the genuine effect of economic freedom on economic growth. The paper explores the differences between aggregate and disaggregate measures of economic freedom and shows that selection effects are stronger when aggregate measures are used.

Publication bias in union-productivity research?

This paper develops and applies several meta-analytic techniques to investigate the presence of publication bias in industrial relations research, specifically in the union-productivity effects literature. Publication bias arises when statistically insignificant results are suppressed or when results satisfying prior expectations are given preference. Like most fields, research in industrial relations is vulnerable to publication bias. Unlike other fields such as economics, there is no evidence of publication bias in the union-productivity literature, as a whole. However, there are pockets of publication selection, as well as negative autoregression, confirming the controversial nature of this area of research. Meta-regression analysis reveals evidence of publication bias (or selection) among US studies.

The need for low bias algorithms in classification learning from large data sets

This paper reviews the appropriateness for application to large data sets of standard machine learning algorithms, which were mainly developed in the context of small data sets. Sampling and parallelisation have proved useful means for reducing computation time when learning from large data sets. However, such methods assume that algorithms that were designed for use with what are now considered small data sets are also fundamentally suitable for large data sets. It is plausible that optimal learning from large data sets requires a different type of algorithm to optimal learning from small data sets. This paper investigates one respect in which data set size may affect the requirements of a learning algorithm — the bias plus variance decomposition of classification error. Experiments show that learning from large data sets may be more effective when using an algorithm that places greater emphasis on bias management, rather than variance management.

Publication bias in industrial relations research

Publication bias arises when statistically non-significant results are suppressed or when only results satisfying prior expectations are published. Like most fields, research in industrial relations is vulnerable to publication bias. In this paper qualitative and quantitative techniques are used in order to detect publication bias in the union-productivity effects literature. We find no evidence of publication bias in this literature, although there does appear to be autoregression in the published results.

Convex hulls as an hypothesis language bias

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model' concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CHI, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. The convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. This representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.

Reducing performance bias for unbalanced text mining

In text categorization applications, class imbalance, which refers to an uneven data distribution where one class is represented by far more less instances than the others, is a commonly encountered problem. In such a situation, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced, learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This paper aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one-class classifiers achieve more balanced performance than the standard approaches.

Taxation threat, motivational postures, and responsive regulation

The central proposition of motivational posturing theory is that regulatees place social distance between themselves and authority, communicating the nature of that distance through a narrative that protects the self from negative appraisal by the authority. One of the key components of posturing is the coping sensibility that individuals adopt to manage the threat of authority. At a baseline level, authorities make demands on citizens and as such threaten individual freedom. At the highest level, authorities threaten through punishment for non-compliance. Data collected from 3,253 randomly selected Australian taxpayers and a special group of 2,292 taxpayers in conflict with the tax authority are used to show that in both groups, three coping sensibilities contribute to posturing ("thinking morally,""feeling oppressed," and "taking control"), and that all three sensibilities are significantly heightened in the group experiencing conflict with the authority. The article argues that the most effective regulatory outcome is achieved when the regulatory process can dampen the "taking control" and "feeling oppressed" sensibilities, and strengthen the "thinking morally" sensibility. Responsive regulation is an approach that encourages tax authorities to read motivational postures, understand the sensibilities that shape them, and tailor a regulatory intervention accordingly.

System of systems approach to threat detection and integration of heterogeneous independently operable systems

This paper presents a system of systems approach to threat detection through integration of heterogeneous independently operable systems. The approach is presented on a realistic situation where a human-controlled base robot, swarm robot(s), and sensors work together to obtain a decision about a possible threat in the environment. The base robot is remotely operated by a human using a haptic control system. The swarm robot(s) are autonomous and can accept directives from the base robot. Finally, sensors directly communicate with (report to) the base robot. In this scenario, heterogeneous systems and human interact in a system of systems architecture. With the inclusion of human expert and sensor verification of swarm robots, the system can successfully perform the threat detection and reduce the false alarms. Finally, a system of systems simulation framework including a base robot, a swarm robot, and two sensors is presented in addition to an experimental evaluation of the proposed SoS architecture

Primary sex ratio bias in an endangered cooperatively breeding bird, the Black-eared Miner, and its implications for conservation

The aim of our study was to investigate primary and adult sex ratios in the cooperatively breeding black-eared miner, Manorina melanotis. We used genetic methods to determine the sex of all birds. Observations were made to quantify differences in helping behaviour between the sexes. As in other miners, Manorina spp., non-breeding males provided most of the help in raising young. Male and female nestlings did not differ significantly in weight, suggesting that both sexes are equally costly to produce. Like other miners, the adult sex ratio in black-eared miners is male-biased (64.4%). However, unlike its congeners, the black-eared miner’s primary sex ratio was strongly biased toward females (62.5%). This suggests that females suffer higher juvenile mortality than males. Our study illustrates how understanding sex ratios is both of theoretical interest and relevant to biological conservation.

The 'China Threat' in American self-imagination: the discursive construction of other as power politics

Argues that the "China threat" argument in mainstream international relations literature in the United States is derived, primarily, from a discursive construction of otherness. Construction which is predicated on a particular narcissistic understanding of the U.S. self and on a positivist-based realism, concerned with absolute certainty and security; Concern which is central to the dominant U.S. self-imagery.