Children's Ideas About What Can Really Happen: The Impact of Age and Religious Background

Five- to 11-year-old U.S. children, from either a religious or secular background, judged whether story events could really happen. There were four different types of stories: magical stories violating ordinary causal regularities; religious stories also violating ordinary causal regularities but via a divine agent; unusual stories not violating ordinary causal regularities but with an improbable event; and realistic stories not violating ordinary causal regularities and with no improbable event. Overall, children were less likely to judge that religious and magical stories could really happen than unusual and realistic stories although religious children were more likely than secular children to judge that religious stories could really happen. Irrespective of background, children frequently invoked causal regularities in justifying their judgments. Thus, in justifying their conclusion that a story could really happen, children often invoked a causal regularity, whereas in justifying their conclusion that a story could not really happen, they often pointed to the violation of causal regularity. Overall, the findings show that children appraise the likelihood of story events actually happening in light of their beliefs about causal regularities. A religious upbringing does not impact the frequency with which children invoke causal regularities in judging what can happen, even if it does impact the type of causal factors that children endorse.

[A new classification of measured temporalities: based on the time axis in nature]

In causal inference, the concept of temporality (or directionality) has not been fully clarified. Starting from causal thinking, this paper divides the time axis in nature into three time domains and two time points by the occurrence timings of both a real cause and a real effect. This has anchored that causal inference can only be realized in the third domain. The measured temporalities can be divided into five types: cross-first-to-third-domain longitudinal (or experimental temporalities), cross-second-to-third-domain longitudinal, within-domain longitudinal, within-domain reversely longitudinal, and within-domain transversal (or observational temporalities). This new classification encompasses all measurement strategies, either for first or multiple measurements, or timely and delayed measurements. Except that the actual measurement for the cause occurs either before its occurrence (only in experiment) or within the second domain, all other measurements are similar to the act of historical reconstruction or "archaeology" , where the importance of measured temporalities may be inferior to the accuracy of the measurements. From the point of view that research design should integrate bias design, this new classification for measured temporalities based on the time axis in Nature, which has a clear meaning and helps to judge the possible biases in the observation methods, provides a basis for correct causal inferences.

[May cross-sectional studies provide causal inferences?]

Due to the flaws inherited in synchronicity, statistical association and survivor bias on variables under measurement, a common 'consensus' has been reached on "cross-sectiional studies (CSS) can lead to failure on causal inference". In this paper, under both causal thinking and diagram, the real and measured cross-sections are clearly defined that these two concepts only exist theoretically. In real CSS research, the temporal orders of measured variables are all non-synchronic, equivalent to the assumption that measurement variables are independent to each other, or there is no differentiated classification bias. Similar to cumulative case-control or historical cohort studies, both exposure and outcome that exist or occur before their measurements in cross-sectional studies, are actions of historical reconstruction or doing 'Archaeology'. One of the common preconditions for causal inference in such studies is that: there must be a causal relation between the measured variables and their historical counterparts. The measured variables are all agents of their corresponding real counterparts, and the temporal orders are not that important in causal inference. It is necessary to better understand the analytic role of the CSS.

[The application of causal thinking in several issues in estimation of effects]

Epidemiology is a branch of science that mainly involves in the etiology studies of non-randomness phenomenon among homogenous populations. In this paper, we use causal-thinking, supported by its tool-Directed Acyclic Graphs, to illustrate how the estimation of effects is affected by the issues as relations between effect and association, time sequences between variables and their measured counterparts, natural picture of dynamic population, formation of susceptible population, selection of study population, impact of covariates and types of cases etc., on the estimation of effects. This type of thinking may help us to re-capture the epidemiological theories, methods and related applications. Thus, causal-thinking should be strengthened.

Superstition and belief as inevitable by-products of an adaptive learning strategy

The existence of superstition and religious beliefs in most, if not all, human societies is puzzling for behavioral ecology. These phenomena bring about various fitness costs ranging from burial objects to celibacy, and these costs are not outweighed by any obvious benefits. In an attempt to resolve this problem, we present a verbal model describing how humans and other organisms learn from the observation of coincidence (associative learning). As in statistical analysis, learning organisms need rules to distinguish between real patterns and randomness. These rules, which we argue are equivalent to setting the level of α for rejection of the null hypothesis in statistics, are governed by risk management as well as by comparison to previous experiences. Risk management means that the cost of a possible type I error (superstition) has to be traded off against the cost of a possible type II error (ignorance). This trade-off implies that the occurrence of superstitious beliefs is an inevitable consequence of an organism's ability to learn from observation of coincidence. Comparison with previous experiences (as in Bayesian statistics) improves the chances of making the right decision. While this Bayesian approach is found in most learning organisms, humans have evolved a unique ability to judge from experiences whether a candidate subject has the power to mechanistically cause the observed effect. Such "strong" causal thinking evolved because it allowed humans to understand and manipulate their environment. Strong causal thinking, however, involves the generation of hypotheses about underlying mechanisms (i.e., beliefs). Assuming that natural selection has favored individuals that learn quicker and more successfully than others owing to (1) active search to detect patterns and (2) the desire to explain these patterns mechanistically, we suggest that superstition has evolved as a by-product of the first, and that belief has evolved as a by-product of the second.