Measures of contextuality in cyclic systems and the negative probabilities measure CNT3.

Several principled measures of contextuality have been proposed for general systems of random variables (i.e. inconsistently connected systems). One such measure is based on quasi-couplings using negative probabilities (here denoted by [Formula: see text], Dzhafarov & Kujala, 2016 Quantum interaction). Dzhafarov & Kujala (Dzhafarov & Kujala 2019 Phil. Trans. R. Soc. A 377, 20190149. (doi:10.1098/rsta.2019.0149)) introduced a measure of contextuality, [Formula: see text], that naturally generalizes to a measure of non-contextuality. Dzhafarov & Kujala (Dzhafarov & Kujala 2019 Phil. Trans. R. Soc. A 377, 20190149. (doi:10.1098/rsta.2019.0149)) additionally conjectured that in the class of cyclic systems these two measures are proportional. Here we prove that conjecture is correct. Recently, Cervantes (Cervantes 2023 J. Math. Psychol. 112, 102726. (doi:10.1016/j.jmp.2022.102726)) showed the proportionality of [Formula: see text] and the Contextual Fraction measure introduced by Abramsky & Brandenburger (Abramsky & Brandenburger 2011 New J. Phys. 13, 113036. (doi:10.1088/1367-2630/13/11/113036)). The present proof completes the description of the interrelations of all contextuality measures proposed within or translated into the Contextuality-by-Default framework so far as they pertain to cyclic systems. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

What does a population-level mediation reveal about individual people?

Mediation analysis investigates the covariation of variables in a population of interest. In contrast, the resolution level of psychological theory, at its core, aims to reach all the way to the behaviors, mental processes, and relationships of individual persons. It would be a logical error to presume that the population-level pattern of behavior revealed by a mediation analysis directly describes all, or even many, individual members of the population. Instead, to reconcile collective covariation with theoretical claims about individual behavior, one needs to look beyond abstract aggregate trends. Taking data quality as a given and a mediation model's estimated parameters as accurate population-level depictions, what can one say about the number of people properly described by the linkages in that mediation analysis? How many individuals are exceptions to that pattern or pathway? How can we bridge the gap between psychological theory and analytic method? We provide a simple framework for understanding how many people actually align with the pattern of relationships revealed by a population-level mediation. Additionally, for those individuals who are exceptions to that pattern, we tabulate how many people mismatch which features of the mediation pattern. Consistent with the person-oriented research paradigm, understanding the distribution of alignment and mismatches goes beyond the realm of traditional variable-level mediation analysis. Yet, such a tabulation is key to designing potential interventions. It provides the basis for predicting how many people stand to either benefit from, or be disadvantaged by, which type of intervention.

Outcomes of patients with COVID-19 in the intensive care unit in Mexico: A multicenter observational study.

BACKGROUND: As of June 15, 2020, a cumulative total of 7,823,289 confirmed cases of COVID-19 have been reported across 216 countries and territories worldwide. However, there is little information on the clinical characteristics and outcomes of critically ill patients with severe COVID-19 who were admitted to intensive care units (ICUs) in Latin America. The present study evaluated the clinical characteristics and outcomes of critically ill patients with severe COVID-19 who were admitted to ICUs in Mexico. METHODS: This was a multicenter observational study that included 164 critically ill patients with laboratory-confirmed COVID-19 who were admitted to 10 ICUs in Mexico, from April 1 to April 30, 2020. Demographic data, comorbid conditions, clinical presentation, treatment, and outcomes were collected and analyzed. The date of final follow-up was June 4, 2020. RESULTS: A total of 164 patients with severe COVID-19 were included in this study. The mean age of patients was 57.3 years (SD 13.7), 114 (69.5%) were men, and 6.0% were healthcare workers. Comorbid conditions were common in patients with critical COVID-19: 38.4% of patients had hypertension and 32.3% had diabetes. Compared to survivors, nonsurvivors were older and more likely to have diabetes, hypertension or other conditions. Patients presented to the hospital a median of 7 days (IQR 4.5-9) after symptom onset. The most common presenting symptoms were shortness of breath, fever, dry cough, and myalgias. One hundred percent of patients received invasive mechanical ventilation for a median time of 11 days (IQR 6-14). A total of 139 of 164 patients (89.4%) received vasopressors, and 24 patients (14.6%) received renal replacement therapy during hospitalization. Eighty-five (51.8%) patients died at or before 30 days, with a median survival of 25 days. Age (OR, 1.05; 95% CI, 1.02-1.08; p<0.001) and C-reactive protein levels upon ICU admission (1.008; 95% CI, 1.003-1.012; p<0.001) were associated with a higher risk of in-hospital death. ICU length of stay was associated with reduced in-hospital mortality risk (OR, 0.89; 95% CI, 0.84-0.94; p<0.001). CONCLUSIONS: This observational study of critically ill patients with laboratory-confirmed COVID-19 who were admitted to the ICU in Mexico demonstrated that age and C-reactive protein level upon ICU admission were associated with in-hospital mortality, and the overall hospital mortality rate was high. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04336345.

Contextuality Analysis of Impossible Figures.

This paper has two purposes. One is to demonstrate contextuality analysis of systems of epistemic random variables. The other is to evaluate the performance of a new, hierarchical version of the measure of (non)contextuality introduced in earlier publications. As objects of analysis we use impossible figures of the kind created by the Penroses and Escher. We make no assumptions as to how an impossible figure is perceived, taking it instead as a fixed physical object allowing one of several deterministic descriptions. Systems of epistemic random variables are obtained by probabilistically mixing these deterministic systems. This probabilistic mixture reflects our uncertainty or lack of knowledge rather than random variability in the frequentist sense.

True contextuality beats direct influences in human decision making.

In quantum physics there are well-known situations when measurements of the same property in different contexts (under different conditions) have the same probability distribution but cannot be represented by one and the same random variable. Such systems of random variables are called contextual. More generally, true contextuality is observed when different contexts force measurements of the same property (in psychology, responses to the same question) to be more dissimilar random variables than warranted by the difference of their distributions. The difference in distributions is itself a form of context-dependence but of another nature: it is attributable to direct causal influences exerted by contexts upon the random variables. The Contextuality-by-Default theory allows one to separate true contextuality from direct influences in the overall context-dependence. The Contextuality-by-Default analysis of numerous previous attempts to demonstrate contextuality in human judgments shows that all context-dependence in them can be accounted for by direct influences, with no true contextuality present. However, contextual systems in human behavior can be found. In this paper we present a series of crowd-sourcing experiments that exhibit true contextuality in simple decision making. The design of these experiments is an elaboration of one introduced in the Snow Queen experiment (Decision 5, 193-204, 2018), in which contextuality was for the first time demonstrated unequivocally. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Contextuality in canonical systems of random variables.

Random variables representing measurements, broadly understood to include any responses to any inputs, form a system in which each of them is uniquely identified by its content (that which it measures) and its context (the conditions under which it is recorded). Two random variables are jointly distributed if and only if they share a context. In a canonical representation of a system, all random variables are binary, and every content-sharing pair of random variables has a unique maximal coupling (the joint distribution imposed on them so that they coincide with maximal possible probability). The system is contextual if these maximal couplings are incompatible with the joint distributions of the context-sharing random variables. We propose to represent any system of measurements in a canonical form and to consider the system contextual if and only if its canonical representation is contextual. As an illustration, we establish a criterion for contextuality of the canonical system consisting of all dichotomizations of a single pair of content-sharing categorical random variables.This article is part of the themed issue 'Second quantum revolution: foundational questions'.

On contextuality in behavioural data.

Dzhafarovet al.(Dzhafarovet al.2016Phil. Trans. R. Soc. A374, 20150099. (doi:10.1098/rsta.2015.0099)) reviewed several behavioural datasets imitating the formal design of the quantum-mechanical contextuality experiments. The conclusion was that none of these datasets exhibited contextuality if understood in the generalized sense proposed by Dzhafarovet al.(2015Found. Phys.7, 762-782. (doi:10.1007/s10701-015-9882-9)), while the traditional definition of contextuality does not apply to these data because they violate the condition of consistent connectedness (also known as marginal selectivity, no-signalling condition, no-disturbance principle, etc.). In this paper, we clarify the relationship between (in)consistent connectedness and (non)contextuality, as well as between the traditional and extended definitions of (non)contextuality, using as an example the Clauser-Horn-Shimony-Holt inequalities originally designed for detecting contextuality in entangled particles.