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

Several principled measures of contextuality have been proposed for general systems of random variables (i.e. inconsistently connected systems).

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.

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.

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.

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).

On contextuality in behavioural data.

Dzhafarovet al.(Dzhafarovet al.2016Phil.