Promoting climate actions: A cognitive-constraints approach.

The present paper reports an experiment with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take political and personal climate actions. Many Americans still do not view climate change as a threat requiring urgent action. Moreover, among American conservatives, higher science literacy is paradoxically associated with higher anthropogenic climate-change skepticism.

How do humans want causes to combine their effects? The role of analytically-defined causal invariance for generalizable causal knowledge.

The present paper reports two experiments (N = 232, 254) addressing the question: How do reasoners reconcile the desire to have useable (i.e., invariant) causal knowledge - knowledge that holds true when applied in new circumstances/contexts - with the reality that causes often interact with other causes present in the context? The experiments test two views of how reasoners learn and generalize potentially complex causal knowledge.

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre-schoolers.

The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal-invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes.

Causal invariance as a tacit aspiration: Analytic knowledge of invariance functions.

For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner's current set of candidates may fail to include a cause that generalizes well to an application context.

Perceived self-competence, psychosocial adjustment, and quality of life in pediatric patients with pacemakers.

Objectives: To compare participants' self-competence levels to normative data and examine self-competence as a potential protective factor against poorer health-related quality of life (HRQOL) and psychosocial adjustment in children with pacemakers.

Methods: Twenty-seven children between the ages of 8 and 18 years and their caregivers were recruited from a pediatric pacemaker clinic. Participants completed self-report and parent-proxy measures of children's health-related quality of life (HRQOL), self-competence, and psychosocial functioning, which included externalizing and internalizing symptoms, adaptive skills, and behavioral symptoms.

Health related quality of life and social support in pediatric patients with pacemakers.

Prior research evaluating health-related quality of life (HRQOL) among pediatric patients with internal cardiac devices has primarily focused on children with cardiac defibrillators, with scant attention devoted to pacemaker recipients. Social support has been conceptualized as a protective factor that partially accounts for differences in HRQOL. This study compares the HRQOL of children with pacemakers with that of healthy children, and examines associations between HRQOL and social support.

Inferential dependencies in causal inference: a comparison of belief-distribution and associative approaches.

Causal evidence is often ambiguous, and ambiguous evidence often gives rise to inferential dependencies, where learning whether one cue causes an effect leads the reasoner to make inferences about whether other cues cause the effect. There are 2 main approaches to explaining inferential dependencies. One approach, adopted by Bayesian and propositional models, distributes belief across multiple explanations, thereby representing ambiguity explicitly.

Causal learning and inference as a rational process: the new synthesis.

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable.

The influence of virtual sample size on confidence and causal-strength judgments.

The authors investigated whether confidence in causal judgments varies with virtual sample size--the frequency of cases in which the outcome is (a) absent before the introduction of a generative cause or (b) present before the introduction of a preventive cause. Participants were asked to evaluate the influence of various candidate causes on an outcome as well as to rate their confidence in those judgments. They were presented with information on the relative frequencies of the outcome given the presence and absence of various candidate causes.

Bayesian generic priors for causal learning.

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P.

The impact of adult behaviors and vocalizations on infant distress during immunizations.

Objective: The Child-Adult Medical Procedure Interaction Scale-Infant Version (CAMPIS-IV) was used to examine the influence of adult and infant behaviors on infant distress following injections.

Methods: In this naturalistic observation study, videotaped interactions of 49 infants, parents, and nurses were coded using the CAMPIS-IV. A series of three lag sequential analyses were used to examine the immediate and delayed effects of each of the CAMPIS-IV criterion behaviors, as well as the effects of the onset of each behavior, on infant distress.

When is a cause the "same"? Coherent generalization across contexts.

Two competing psychological approaches to causal learning make different predictions regarding what aspect of perceived causality is generalized across contexts. Two experiments tested these predictions. In one experiment, the task required a judgment regarding the existence of a simple causal relation; in the other, the task required a judgment regarding the existence of an interaction between a candidate cause and unobserved background causes.

Pediatric procedural pain.

This article reviews the various settings in which infants, children, and adolescents experience pain during acute medical procedures and issues related to referral of children to pain management teams. In addition, self-report, reports by others, physiological monitoring, and direct observation methods of assessment of pain and related constructs are discussed and recommendations are provided. Pharmacological, other medical approaches, and empirically supported cognitive behavioral interventions are reviewed.

Parent and child psychological factors in pediatric syncope and other somatic symptoms.

The authors examined associations among parental and child adjustment, child syncope, somatic, and school problems. Participants were children (N = 56) ages 7-18 years with syncope. Measures included syncope severity, parental distress, and children's internalizing symptoms.

Assessing interactive causal influence.

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory.

From covariation to causation: a test of the assumption of causal power.

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.