Bias in judgement: Comparing individuals and groups

The relative susceptibility of individuals and groups to systematic judgmental biases is considered. An overview of the relevant empirical literature reveals no clear or general pattern. However, a theoretical analysis employing J. H. Davis's (1973) social decision scheme (SDS) model reveals that the relative magnitude of individual and group bias depends upon several factors, including group size, initial individual judgment, the magnitude of bias among individuals, the type of bias, and most of all, the group-judgment process. It is concluded that there can be no simple answer to the question, "Which are more biased, individuals or groups?," but the SDS model offers a framework for specifying some of the conditions under which individuals are both more and less biased than groups

Cyber Buddy is Better than No Buddy: A Test of the Köhler Motivation Effect in Exergames.

Objective: Although exergames are popular, few people take advantage of the potential of group dynamics to motivate play (and achieve associated health bene?ts). One motivation gain phenomenon has shown promise for motivating greater effort in partnered exergames: The Koehler effect (working at a task with a more capable partner where one’s performance is indispensable to the group). This article examines whether a Koehler effect can be demonstrated in an exergame by exercising with a moderately superior humanoid, software-generated partner. Materials and Methods: Male and female (n = 120; mean age, 19.41 years) college students completed a series of plank exercises using ‘‘CyBuddy Exercise,’’ a program developed speci?cally for this study. In a lab in an academic building, participants completed the exercises individually and, after a rest, were randomly assigned to complete the same exercises again, but with a ‘‘live’’ human partner (HP) presented virtually, a nearlyhuman-like, humanoid partner (NHP), a hardly human-like, software-generated partner (HHP), or a no-partner control condition (IC), with equal numbers in each group (i.e., n = 30). Exercise persistence, perceived exertion, self-ef?cacy beliefs, enjoyment, and intentions to exercise were recorded and analyzed. Results: A 4X2 analysis of variance on the (Block 2 – Block 1) difference scores showed that a signi?cant Koehler motivation gain was observed in all partner conditions (compared with IC), but persistence was signi?cantly greater with HPs than with either NHP or HHP humanoid partners (P < 0.05). By the conclusion of the study, there were no signi?cant differences among the partnered conditions in perceived exertion, self-ef?cacy, enjoyment, or future intentions to exercise. Conclusions: These results suggest that a software-generated partner can elicit the Koehler motivation gain in exergames, but not as strongly as a partner who is thought to be human

Sleep deprivation impairs and caffeine enhances my performance, but not always our performance: how acting in a group can change the effects of impairments and enhancements

What effects do factors that impair or enhance performance in individuals have when these individuals act in groups? We provide a framework, called the GIE ("Effects of Grouping on Impairments and Enhancements”) framework, for investigating this question. As prominent examples for individual-level impairments and enhancements, we discuss sleep deprivation and caffeine. Based on previous research, we derive hypotheses on how they influence performance in groups, specifically process gains and losses in motivation, individual capability, and coordination. We conclude that the effect an impairment or enhancement has on individual-level performance is not necessarily mirrored in group performance: grouping can help or hurt. We provide recommendations on how to estimate empirically the effects individual-level performance impairments and enhancements have in groups. By comparing sleep deprivation to stress and caffeine to pharmacological cognitive enhancement, we illustrate that we cannot readily generalize from group results on one impairment or enhancement to another, even if they have similar effects on individual-level performance

High frequency oscillatory ventilation and prone positioning in a porcine model of lavage-induced acute lung injury

BACKGROUND: This animal study was conducted to assess the combined effects of high frequency oscillatory ventilation (HFOV) and prone positioning on pulmonary gas exchange and hemodynamics. METHODS: Saline lung lavage was performed in 14 healthy pigs (54 ± 3.1 kg, mean ± SD) until the arterial oxygen partial pressure (PaO(2)) decreased to 55 ± 7 mmHg. The animals were ventilated in the pressure controlled mode (PCV) with a positive endexpiratory pressure (PEEP) of 5 cmH(2)O and a tidal volume (V(T)) of 6 ml/kg body weight. After a stabilisation period of 60 minutes, the animals were randomly assigned to 2 groups. Group 1: HFOV in supine position; group 2: HFOV in prone position. After evaluation of prone positioning in group 2, the mean airway pressure (P(mean)) was increased by 3 cmH(2)O from 16 to 34 cmH(2)O every 20 minutes in both groups accompanied by measurements of respiratory and hemodynamic variables. Finally all animals were ventilated supine with PCV, PEEP = 5 cm H(2)O, V(T )= 6 ml/kg. RESULTS: Combination of HFOV with prone positioning improves oxygenation and results in normalisation of cardiac output and considerable reduction of pulmonary shunt fraction at a significant (p < 0.05) lower P(mean )than HFOV and supine positioning. CONCLUSION: If ventilator induced lung injury is ameliorated by a lower P(mean), a combined treatment approach using HFOV and prone positioning might result in further lung protection

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation

<p>Abstract</p> <p>Background</p> <p>External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.</p> <p>Results</p> <p>We therefore develop the concept of graph-decorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways in a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the features (e.g. genes) and are thus able to define a graph-delayed correlation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph-decorrelation algorithm (GraDe). To analyze alterations in the gene response in <it>IL-6 </it>stimulated primary mouse hepatocytes, we performed a time-course microarray experiment and applied GraDe. In contrast to standard techniques, the extracted time-resolved gene expression profiles showed that <it>IL-6 </it>activates genes involved in cell cycle progression and cell division. Genes linked to metabolic and apoptotic processes are down-regulated indicating that <it>IL-6 </it>mediated priming renders hepatocytes more responsive towards cell proliferation and reduces expenditures for the energy metabolism.</p> <p>Conclusions</p> <p>GraDe provides a novel framework for the decomposition of large-scale 'omics' data. We were able to show that including prior knowledge into the separation task leads to a much more structured and detailed separation of the time-dependent responses upon <it>IL-6 </it>stimulation compared to standard methods. A Matlab implementation of the GraDe algorithm is freely available at <url>http://cmb.helmholtz-muenchen.de/grade</url>.</p

Dynamic Switch of Negative Feedback Regulation in Drosophila Akt–TOR Signaling

Akt represents a nodal point between the Insulin receptor and TOR signaling, and its activation by phosphorylation controls cell proliferation, cell size, and metabolism. The activity of Akt must be carefully balanced, as increased Akt signaling is frequently associated with cancer and as insufficient Akt signaling is linked to metabolic disease and diabetes mellitus. Using a genome-wide RNAi screen in Drosophila cells in culture, and in vivo analyses in the third instar wing imaginal disc, we studied the regulatory circuitries that define dAkt activation. We provide evidence that negative feedback regulation of dAkt occurs during normal Drosophila development in vivo. Whereas in cell culture dAkt is regulated by S6 Kinase (S6K)–dependent negative feedback, this feedback inhibition only plays a minor role in vivo. In contrast, dAkt activation under wild-type conditions is defined by feedback inhibition that depends on TOR Complex 1 (TORC1), but is S6K–independent. This feedback inhibition is switched from TORC1 to S6K only in the context of enhanced TORC1 activity, as triggered by mutations in tsc2. These results illustrate how the Akt–TOR pathway dynamically adapts the routing of negative feedback in response to the activity load of its signaling circuit in vivo

52 Genetic Loci Influencing Myocardial Mass.

BACKGROUND: Myocardial mass is a key determinant of cardiac muscle function and hypertrophy. Myocardial depolarization leading to cardiac muscle contraction is reflected by the amplitude and duration of the QRS complex on the electrocardiogram (ECG). Abnormal QRS amplitude or duration reflect changes in myocardial mass and conduction, and are associated with increased risk of heart failure and death. OBJECTIVES: This meta-analysis sought to gain insights into the genetic determinants of myocardial mass. METHODS: We carried out a genome-wide association meta-analysis of 4 QRS traits in up to 73,518 individuals of European ancestry, followed by extensive biological and functional assessment. RESULTS: We identified 52 genomic loci, of which 32 are novel, that are reliably associated with 1 or more QRS phenotypes at p < 1 × 10(-8). These loci are enriched in regions of open chromatin, histone modifications, and transcription factor binding, suggesting that they represent regions of the genome that are actively transcribed in the human heart. Pathway analyses provided evidence that these loci play a role in cardiac hypertrophy. We further highlighted 67 candidate genes at the identified loci that are preferentially expressed in cardiac tissue and associated with cardiac abnormalities in Drosophila melanogaster and Mus musculus. We validated the regulatory function of a novel variant in the SCN5A/SCN10A locus in vitro and in vivo. CONCLUSIONS: Taken together, our findings provide new insights into genes and biological pathways controlling myocardial mass and may help identify novel therapeutic targets