Functional versus dysfunctional coping with physical pain: An experimental comparison of acceptance vs. avoidance coping.

This study compared acceptance vs. avoidance coping with acute physical pain, in a pain-induction experiment and examined both between and within-group differences, multi-methodically and multi-dimensionally using behavioral, physiological and self-report measures. The sample consisted of 88 University students (76.1% females; Mage = 21.33 years). Participants were randomly assigned to four instructed groups and participated twice in the Cold Pressor Task: (a) Acceptance followed by avoidance; (b) Avoidance followed by acceptance; (c) No instructions (control) followed by acceptance, and (d) No instructions (control) followed by avoidance. All analyses were conducted using repeated-measures ANOVAs. Randomized techniques analyses showed that participants receiving no instructions followed by acceptance reported significantly greater changes in physiological and behavioral measures across time. Low adherence to acceptance instructions was found, especially during the first phase. Exploratory analyses on actual techniques used (as opposed to taught technique) showed that participants using avoidance followed by acceptance exhibited significantly greater changes in physiological and behavioral measures across time. No significant differences were found for the self-report of negative affect outcome. Overall, our findings provide support to ACT theory, as participants might have to use firstly ineffective coping to understand what works best to cope with pain. This is the first study examining acceptance vs. avoidance coping both between and within individuals in physical pain, multi-methodically and multi-dimensionally.

Estimating the COVID-19 Prevalence in Spain With Indirect Reporting via Open Surveys.

During the initial phases of the COVID-19 pandemic, accurate tracking has proven unfeasible. Initial estimation methods pointed toward case numbers that were much higher than officially reported. In the CoronaSurveys project, we have been addressing this issue using open online surveys with indirect reporting. We compare our estimates with the results of a serology study for Spain, obtaining high correlations (R squared 0.89). In our view, these results strongly support the idea of using open surveys with indirect reporting as a method to broadly sense the progress of a pandemic.

Comparing apples and oranges or different types of citrus fruits? Using wearable versus stationary devices to analyze psychophysiological data.

Wearable devices capable of capturing psychophysiological signals are popular. However, such devices have, yet, to be established in experimental and clinical research. This study, therefore, compared psychophysiological data (skin conductance level (SCL), heart rate (HR), and heart rate variability (HRV)) captured with a wearable device (Microsoft band 2) to those of a stationary device (Biopac MP150), in an experimental pain induction paradigm. Additionally, the present study aimed to compare two analytical techniques of HRV psychophysiological data: traditional (i.e., peaks are detected and manually checked) versus automated analysis using Python programs. Forty-three university students (86% female; Mage = 21.37 years) participated in the cold pressor pain induction task. Results showed that the majority of the correlations between the two devices for the mean HR were significant and strong (rs > .80) both during baseline and experimental phases. For the time-domain measure of mean RR (function of autonomic influences) of HRV, the correlations between the two devices at baseline were almost perfect (rs = .99), whereas at the experimental phase were significantly strong (rs > .74). However, no significant correlations were found for mean SCL (p> .05). Additionally, automated analysis led to similar features for HRV stationary data as the traditional analysis. Implications for data collection include the establishment of a methodology to compare stationary to mobile devices and a new, more cost efficient way of collecting psychophysiological data. Implications for data analysis include analyzing the data faster, with less effort and allowing for large amounts of data to be recorded.

Algorithmic mechanisms for reliable crowdsourcing computation under collusion.

We consider a computing system where a master processor assigns a task for execution to worker processors that may collude. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a game among workers. That is, we assume that workers are rational in a game-theoretic sense. We identify analytically the parameter conditions for a unique Nash Equilibrium where the master obtains the correct result. We also evaluate experimentally mixed equilibria aiming to attain better reliability-profit trade-offs. For a wide range of parameter values that may be used in practice, our simulations show that, in fact, both master and workers are better off using a pure equilibrium where no worker cheats, even under collusion, and even for colluding behaviors that involve deviating from the game.