Rude assessment and I'm faking it: does witnessing incivility compel people to cheat?

The ease and convenience of personality testing for personnel selection purposes is somewhat marred by concerns that test-takers might fake their responses if they believe it is advantageous to do so. Whether or not a candidate fakes is determined by both the ability as well as the motivation to do so, and both are subject to individual difference as well as contextual factors. Here we report an experiment that demonstrates a powerful contextual link between rudeness and cheating. Participants who witnessed a rude encounter prior to a performance-linked cognitive task, subsequently overstated their performance (i.e. cheated) to a greater extent than participants who witnessed a neutral encounter but had the same opportunity to cheat. We suggest therefore that rudeness increases propensity to cheat. Consequently, to minimize the risk of test-takers providing fake responses, it is of practical importance to ensure a civil atmosphere before and during any testing situation that might afford an opportunity to cheat

Rude assessment and I'm faking it: does witnessing incivility compel people to cheat?

The ease and convenience of personality testing for personnel selection purposes is somewhat marred by concerns that test-takers might fake their responses if they believe it is advantageous to do so. Whether or not a candidate fakes is determined by both the ability as well as the motivation to do so, and both are subject to individual difference as well as contextual factors. Here we report an experiment that demonstrates a powerful contextual link between rudeness and cheating. Participants who witnessed a rude encounter prior to a performance-linked cognitive task, subsequently overstated their performance (i.e. cheated) to a greater extent than participants who witnessed a neutral encounter but had the same opportunity to cheat. We suggest therefore that rudeness increases propensity to cheat. Consequently, to minimize the risk of test-takers providing fake responses, it is of practical importance to ensure a civil atmosphere before and during any testing situation that might afford an opportunity to cheat

Temporal binding and internal clocks: No evidence for general pacemaker slowing.

The perception of time is distorted by many factors (e.g., arousal, temperature, age etc.), but is it possible that causality would affect our perception of time? We investigate timing changes in the temporal binding effect, which refers to a subjective shortening of the interval between actions and their outcomes. Four experiments investigated whether binding may be due to variations in the rate of an internal clock. Specifically, we asked whether binding reflects changes to a general timing system, or a dedicated clock unique to causal sequences. We developed a novel experimental paradigm (embedded interval estimation procedure) in which participants made temporal judgments of either causal or non causal intervals, or the duration of an event embedded within that interval. Stimuli and modality were combined factorially, with interval markers and embedded events being either visual or auditory. While we replicated the temporal binding effect, we found no evidence for commensurate changes to time perception of the embedded event, which suggests that temporal binding is effected by changes to a specific and dedicated, rather than a general clock system

The role of time perception in temporal binding: Impaired temporal resolution in causal sequences

Causality affects our perception of time; events that appear as causally related are perceived as closer together in time than unrelated events. This effect is known as temporal binding. One potential explanation of this effect is that causality slows an “internal clock” that is used in interval estimation. To explore this hypothesis, we first examined participants’ perceived duration of a range of intervals between a causal action and an effect, or between two unrelated events. If (apparent) causality slows the internal clock, then plotting perceived duration against actual duration should reveal a shallower slope in the causality condition (a relative compression of perceived time). This pattern was found. We then examined an interesting corollary: that a slower rate during causal sequences would result in reduced temporal acuity. This is what we found: Duration discrimination thresholds were higher for causal compared to non-causal sequences. These results are compatible with a clock-slowing account of temporal binding. Implications for sensory recalibration accounts of binding are discussed

Human vision reconstructs time to satisfy causal constraints

The goal of perception is to infer the most plausible source of sensory stimulation. Unisensory perception of temporal order, however, appears to require no inference, since the order of events can be uniquely determined from the order in which sensory signals arrive. Here we demonstrate a novel perceptual illusion that casts doubt on this intuition: in three studies (N=607) the experienced event timings are determined by causality in real-time. Adult observers viewed a simple three-item sequence ACB, which is typically remembered as ABC (Bechlivanidis & Lagnado, 2016), in line with principles of causality. When asked to indicate the time at which events B and C occurred, points of subjective simultaneity shifted so that the assumed cause B appeared earlier and the assumed effect C later, despite full attention and repeated viewings. This first demonstration of causality reversing perceived temporal order cannot be explained by post-perceptual distortion, lapsed attention, or saccades

Temporal Binding, Causation, and Agency: Developing a New Theoretical Framework

In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency.” However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause–effect sequence. In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, has been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people's causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding

The developmental profile of temporal binding: From childhood to adulthood.

Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6–7, 7–8, and 9–10 years) and adults on a novel interval estimation task. In Experiment 1, participants made judgements about the time interval between (a) their button press and a rocket launch, and (b) a non-causal predictive signal and rocket launch. In Experiment 2, an additional causal condition was included in which participants made judgements about the interval between an experimenter’s button press and the launch of a rocket. Temporal binding was demonstrated consistently and did not change in magnitude with age: estimates of delay were shorter in causal contexts for both adults and children. In addition, the magnitude of the binding effect was greater when participants themselves were the cause of an outcome compared with when they were mere spectators. This suggests that although causality underlies the binding effect, intentional action may modulate its magnitude. Again, this was true of both adults and children. Taken together, these results are the first to suggest that the binding effect is present and developmentally constant from childhood into adulthood

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation

Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices Q and R, respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, Q and R matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the Q and R matrices using ensemble-based data assimilation techniques. Most of the methodologies developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methodologies are based on two main statistical criteria: (i) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and (ii) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for Q and R. These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise