“You can't kid a kidder”: association between production and detection of deception in an interactive deception task

Both the ability to deceive others, and the ability to detect deception, has long been proposed to confer an evolutionary advantage. Deception detection has been studied extensively, and the finding that typical individuals fare little better than chance in detecting deception is one of the more robust in the behavioral sciences. Surprisingly, little research has examined individual differences in lie production ability. As a consequence, as far as we are aware, no previous study has investigated whether there exists an association between the ability to lie successfully and the ability to detect lies. Furthermore, only a minority of studies have examined deception as it naturally occurs; in a social, interactive setting. The present study, therefore, explored the relationship between these two facets of deceptive behavior by employing a novel competitive interactive deception task (DeceIT). For the first time, signal detection theory (SDT) was used to measure performance in both the detection and production of deception. A significant relationship was found between the deception-related abilities; those who could accurately detect a lie were able to produce statements that others found difficult to classify as deceptive or truthful. Furthermore, neither ability was related to measures of intelligence or emotional ability. We, therefore, suggest the existence of an underlying deception-general ability that varies across individuals

Profile of health-related quality of life outcomes after liver transplantation: univariate effects and multivariate models

AbstractAim. To test the effects of pre- and post-transplant clinical covariates on post-transplant health-related quality of life (HRQOL) score profiles in liver transplant recipients. Material and methods. HRQOL was measured before and after transplantation using the SF-36® Health Survey. Clinical data [diagnosis, model of end-stage liver disease (MELD) score, post-transplant rejection and infection episodes], pre-transplant functional performance (FP), and demographics were collected. Multivariate models for the eight SF-36 scales and two summary components were developed using multiple regression. Discriminant analysis was used to test whether the score profiles differentiated among recipients with and without hepatitis C virus (HCV) infection. Results. 104 adults reported pre- and post-transplant HRQOL. Time post-transplant averaged 9±8 months (range 1–39). Scores on all SF-36 measures improved from pre- to post-transplant (p<0.001), and 7 of 10 models were significant (p<0.05). After controlling for pre-transplant HRQOL and time post-transplant, HCV infection had a negative effect on the role physical, bodily pain, and role emotional scales. History of a rejection episode had a negative effect on the bodily pain and vitality scales. MELD scores ≥18 had a positive effect on the role physical scale. Pre-transplant FP and post-transplant infection episodes did not affect post-transplant HRQOL. HCV infection had a significant effect on the SF-36 score profile (canonical correlation=0.50; p<0.001). Conclusions. Pre-transplant HCV infection, MELD score, and post-transplant rejection episodes have significant independent effects on HRQOL after liver transplantation. Their specific effects vary among the individual SF-36 scales, and HRQOL score profiles differ among HCV+ and HCV– recipients

A cluster randomised controlled trial to investigate the effectiveness and cost effectiveness of the 'Girls Active' intervention: a study protocol

Background: Despite the health benefits of physical activity, data from the UK suggest that a large proportion of adolescents do not meet the recommended levels of moderate-to-vigorous physical activity (MVPA). This is particularly evident in girls, who are less active than boys across all ages and may display a faster rate of decline in physical activity throughout adolescence. The &lsquo;Girls Active&rsquo; intervention has been designed by the Youth Sport Trust to target the lower participation rates observed in adolescent girls. &lsquo;Girls Active&rsquo; uses peer leadership and marketing to empower girls to influence decision making in their school, develop as role models and promote physical activity to other girls. Schools are provided with training and resources to review their physical activity, sport and PE provision, culture and practices to ensure they are relevant and attractive to adolescent girls.&nbsp; Methods/Design: This study is a two-arm cluster randomised controlled trial (RCT) aiming to recruit 20 secondary schools. Clusters will be randomised at the school level (stratified by school size and proportion of Black and Minority Ethnic (BME) pupils) to receive either the &lsquo;Girls Active&rsquo; intervention or carry on with usual practice (1:1). The 20 secondary schools will be recruited from state secondary schools within the Midlands area. We aim to recruit 80 girls aged 11&ndash;14 years in each school. Data will be collected at three time points; baseline and seven and 14months after baseline. Our primary aim is to investigate whether &lsquo;Girls Active&rsquo; leads to higher objectively measured (GENEActiv) moderate-to-vigorous physical activity in adolescent girls at 14months after baseline assessment compared to the control group. Secondary outcomes include other objectively measured physical activity variables, adiposity, physical activity-related psychological factors and the cost-effectiveness of the &lsquo;Girls Active&rsquo; intervention. A thorough process evaluation will be conducted during the course of the intervention delivery.&nbsp; Discussion: The findings of this study will provide valuable information on whether this type of school-based approach to increasing physical activity in adolescent girls is both effective and cost-effective in the UK

Good Liars Are Neither ‘Dark’ Nor Self-Deceptive

Deception is a central component of the personality 'Dark Triad' (Machiavellianism, Psychopathy and Narcissism). However, whether individuals exhibiting high scores on Dark Triad measures have a heightened deceptive ability has received little experimental attention. The present study tested whether the ability to lie effectively, and to detect lies told by others, was related to Dark Triad, Lie Acceptability, or Self-Deceptive measures of personality using an interactive group-based deception task. At a group level, lie detection accuracy was correlated with the ability to deceive others—replicating previous work. No evidence was found to suggest that Dark Triad traits confer any advantage either to deceive others, or to detect deception in others. Participants who considered lying to be more acceptable were more skilled at lying, while self-deceptive individuals were generally less credible and less confident when lying. Results are interpreted within a framework in which repeated practice results in enhanced deceptive ability

Transcranial current stimulation of the temporoparietal junction improves lie detection

The ability to detect deception is of vital importance in human society, playing a crucial role in communication, cooperation, and trade between societies, businesses, and individuals. However, numerous studies have shown, remarkably consistently, that we are only slightly above chance when it comes to detecting deception [1]. Here we investigate whether inconsistency between one's own opinion and the stated opinion of another impairs judgment of the veracity of that statement, in the same way that one's own mental, affective, and action states, when inconsistent, can interfere with representation of those states in another [2]. Within the context of lie detection, individuals may be less accurate when judging the veracity of another's opinion when it is inconsistent with their own opinion. Here we present a video-mediated lie-detection task to confirm this prediction: individuals correctly identified truths or lies less often when the other's expressed opinion was inconsistent with their own (experiment 1). Transcranial direct current stimulation (tDCS) of the temporoparietal junction (TPJ) has previously been shown to improve the ability to selectively represent the self or another [3-5]. We therefore predicted that TPJ stimulation would enable lie detectors to inhibit their own views, enhance those of the other, and improve their ability to determine whether another was presenting their true opinion. Experiment 2 confirmed this second prediction: anodal tDCS of the TPJ improved lie detection specifically when one's own and others' views were conflicting

Templates for Convex Cone Problems with Applications to Sparse Signal Recovery

This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as follows: first, determine a conic formulation of the problem; second, determine its dual; third, apply smoothing; and fourth, solve using an optimal first-order method. A merit of this approach is its flexibility: for example, all compressed sensing problems can be solved via this approach. These include models with objective functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or a combination thereof. In addition, the paper also introduces a number of technical contributions such as a novel continuation scheme, a novel approach for controlling the step size, and some new results showing that the smooth and unsmoothed problems are sometimes formally equivalent. Combined with our framework, these lead to novel, stable and computationally efficient algorithms. For instance, our general implementation is competitive with state-of-the-art methods for solving intensively studied problems such as the LASSO. Further, numerical experiments show that one can solve the Dantzig selector problem, for which no efficient large-scale solvers exist, in a few hundred iterations. Finally, the paper is accompanied with a software release. This software is not a single, monolithic solver; rather, it is a suite of programs and routines designed to serve as building blocks for constructing complete algorithms.Comment: The TFOCS software is available at http://tfocs.stanford.edu This version has updated reference

Understanding Insider Threat: A Framework for Characterising Attacks

The threat that insiders pose to businesses, institutions and governmental organisations continues to be of serious concern. Recent industry surveys and academic literature provide unequivocal evidence to support the significance of this threat and its prevalence. Despite this, however, there is still no unifying framework to fully characterise insider attacks and to facilitate an understanding of the problem, its many components and how they all fit together. In this paper, we focus on this challenge and put forward a grounded framework for understanding and reflecting on the threat that insiders pose. Specifically, we propose a novel conceptualisation that is heavily grounded in insider-threat case studies, existing literature and relevant psychological theory. The framework identifies several key elements within the problem space, concentrating not only on noteworthy events and indicators- technical and behavioural- of potential attacks, but also on attackers (e.g., the motivation behind malicious threats and the human factors related to unintentional ones), and on the range of attacks being witnessed. The real value of our framework is in its emphasis on bringing together and defining clearly the various aspects of insider threat, all based on real-world cases and pertinent literature. This can therefore act as a platform for general understanding of the threat, and also for reflection, modelling past attacks and looking for useful patterns

Choosing how to choose : Institutional pressures affecting the adoption of personnel selection procedures

The gap between science and practice in personnel selection is an ongoing concern of human resource management. This paper takes Oliver´s framework of organizations´ strategic responses to institutional pressures as a basis for outlining the diverse economic and social demands that facilitate or inhibit the application of scientifically recommended selection procedures. Faced with a complex network of multiple requirements, practitioners make more diverse choices in response to any of these pressures than has previously been acknowledged in the scientific literature. Implications for the science-practitioner gap are discussed

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem