The Advantage of Timely Intervention.

Can people learn causal structure more effectively through intervention rather than observation? Four studies used a trial-based learning paradigm in which participants obtained probabilistic data about a causal chain through either observation or intervention and then selected the causal model most likely to have generated the data. Experiment 1 demonstrated that interveners made more correct model choices than did observers, and Experiments 2 and 3 ruled out explanations for this advantage in terms of informational differences between the 2 conditions. Experiment 4 tested the hypothesis that the advantage was driven by a temporal signal; interveners may exploit the cue that their interventions are the most likely causes of any subsequent changes. Results supported this temporal cue hypothesis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Word, thought, and deed: The role of object categories in children's inductive inferences and exploratory play.

Previous research (e.g., S. A. Gelman & E. M. Markman, 1986; A. Gopnik & D. M. Sobel, 2000) suggests that children can use category labels to make inductive inferences about nonobvious causal properties of objects. However, such inductive generalizations can fail to predict objects' causal properties when (a) the property being projected varies within the category, (b) the category is arbitrary (e.g., things smaller than a bread box), or (c) the property being projected is due to an exogenous intervention rather than intrinsic to the object kind. In 4 studies, the authors showed that preschoolers (M = 48 months; range = 42-57 months) were sensitive to these constraints on induction and selectively engaged in exploration when evidence about objects' causal properties conflicted with inductive generalizations from the objects' kind to their causal powers. This suggests that the exploratory actions children generate in free play could support causal learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Physical Maltreatment Victim to Antisocial Child: Evidence of an Environmentally Mediated Process.

The well-documented finding that child physical maltreatment predicts later antisocial behavior has at least 2 explanations: (a) Physical maltreatment causes antisocial behavior, and (b) genetic factors transmitted from parents to children influence the likelihood that parents will be abusive and that children will engage in antisocial behavior. The authors tested these hypotheses in the representative Environmental-Risk cohort of 1,116 twin pairs and their families, who were assessed when the twins were 5 and 7 years old. Mothers reported on children's experience of physical maltreatment, and mothers and teachers reported on children's antisocial behavior. The findings support the hypothesis that physical maltreatment plays a causal role in the development of children's antisocial behavior and that preventing maltreatment can prevent its violent sequelae. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A self-agency bias in preschoolers' causal inferences.

Preschoolers' causal learning from intentional actions—causal interventions—is subject to a self-agency bias. The authors propose that this bias is evidence-based, in other words, that it is responsive to causal uncertainty. In the current studies, two causes (one child controlled, one experimenter controlled) were associated with one or two effects, first independently, then simultaneously. When initial independent effects were probabilistic, and thus subsequent simultaneous actions were causally ambiguous, children showed a self-agency bias. Children showed no bias when initial effects were deterministic. Further controls established that children's self-agency bias is not a wholesale preference but rather is influenced by uncertainty in causal evidence. These results demonstrate that children's own experience of action influences their causal learning, and the findings suggest possible benefits in uncertain and ambiguous everyday learning contexts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Chronic and temporarily activated causal uncertainty beliefs and stereotype usage.

In 3 studies, we examined the hypothesis that the effects of stereotype usage on target judgments are moderated by causal uncertainty beliefs and related accuracy goal structures. In Study 1, we focused on the role of chronically accessible causal uncertainty beliefs as predictors of a target's level of guilt for an alleged academic misconduct offense. In Study 2, we examined the role of chronic causal uncertainty reduction goals and a manipulated accuracy goal; in Study 3, we investigated the role of primed causal uncertainty beliefs on guilt judgments. In all 3 studies, we found that activation of causal uncertainty beliefs and accuracy concerns was related to a reduced usage of stereotypes. Moreover, this reduction was not associated with participants' levels of perceived control, depression, state affect, need for cognition, or personal need for structure. Results are discussed in terms of their implications for the model of causal uncertainty and, more generally, in terms of the motivational processes underlying stereotype usage. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal relations and feature similarity in children's inductive reasoning.

Four experiments examined the development of property induction on the basis of causal relations. In the first 2 studies, 5-year-olds, 8-year-olds, and adults were presented with triads in which a target instance was equally similar to 2 inductive bases but shared a causal antecedent feature with 1 of them. All 3 age groups used causal relations as a basis for property induction, although the proportion of causal inferences increased with age. Subsequent experiments pitted causal relations against featural similarity in induction. It was found that adults and 8-year-olds, but not 5-year-olds, preferred shared causal relations over strong featural similarity as a basis for induction. The implications for models of inductive reasoning and development are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Analogical and category-based inference: A theoretical integration with Bayesian causal models.

A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Knowing who dunnit: Infants identify the causal agent in an unseen causal interaction.

Preverbal infants can represent the causal structure of events, including distinguishing the agentive and receptive roles and categorizing entities according to stable causal dispositions. This study investigated how infants combine these 2 kinds of causal inference. In Experiments 1 and 2, 9.5-month-olds used the position of a human hand or a novel puppet (causal agents), but not a toy train (an inert object), to predict the subsequent motion of a beanbag. Conversely, in Experiment 3, 10- and 7-month-olds used the motion of the beanbag to infer the position of a hand but not of a toy block. These data suggest that preverbal infants expect a causal agent as the source of motion of an inert object. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal uncertainty beliefs and diagnostic information seeking.

This study examined the extent to which chronic causal uncertainty beliefs influence diagnostic information seeking. Situational factors intended to increase the excitation level of causal uncertainty beliefs and the intensity of goal-directed behavior also were investigated. Participants expected to interview either a gender in-group or a gender out-group member, and half of them expected to be held accountable for their understanding of the interviewee. For out-group conditions, those accountable participants who possessed chronically accessible causal uncertainty beliefs revealed the greatest preference for diagnostic information. For in-group conditions, no differential pattern of information seeking as a function of chronic causal uncertainty beliefs or goal importance were found. Results are discussed in terms of a recent model of motivated social cognition proposed by G. Weary and J. A. Edwards (1996). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Accounting for occurrences: A new view of the use of contingency information in causal judgment.

When people make causal judgments from contingency information, a principal aim is to account for occurrences of the outcome. When 2 causes are under consideration, the capacity of either to account for occurrences is judged from how likely the cause is to be present when the outcome occurs and from the rate at which the outcome occurs when that cause alone is present, which gives an estimate of the strength of the cause. These propositions are formalized in a weighted averaging model, which successfully predicted several judgmental phenomena not predicted by other models of causal judgment. These include a tendency for judgment of one cause (A) to be reduced as the number of occurrences of when only the other one (B) increases and a tendency for A to receive higher judgments than B if A is better able to account for occurrences than B is even if B has a higher contingency with the outcome than A does. Overshadowing, a tendency for judgments of B to be depressed if A has a higher contingency, is weak or absent when B is better able to account for occurrences than A. Results of several experiments support these and related predictions derived from the accounting for occurrences hypothesis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Asymmetries in predictive and diagnostic reasoning.

In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

How people explain actions performed by groups and individuals.

The authors explore whether people explain intentional actions performed by groups differently from actions performed by individuals. A theoretical framework is offered that distinguishes between 2 modes of explanation: the agent's reasons (beliefs or desires in light of which the agent decided to act) and causal histories of reasons (CHRs; factors that preceded and brought about the agent's reasons). The authors develop the hypothesis that people use more CHR explanations when explaining group actions than when explaining individual actions. Study 1 demonstrates this asymmetry. Studies 2 and 3 explore 2 necessary conditions for the asymmetry: that the group be perceived as an aggregate of individual actors rather than as a jointly acting group and that explainers have general information available about the group. Discussion focuses on people's perception of groups as entities and agents. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Na?ve theories of causal force and compression of elapsed time judgments.

Recent research has shown that when people perceive a causal relation between 2 events, they “compress” the intervening elapsed time. The present work shows that a na?ve mechanical–physical conception of causality, in which causal forces are believed to dissipate over time, underlies the estimates of shorter elapsed time. Being primed with alternative, nondissipative causal mechanisms and having the cognitive capacity to consider such mechanisms moderates the compression effect. The studies rule out similarity, mnemonic association, and anchoring as alternative accounts for the effect. Taken together, the findings support the hypothesis that causal cognition plays a major role in judgments of elapsed time. The implications of the compression effect on the timing of future actions, persistence, and causal learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal inference and related statistical methods

Statistical approaches for evaluating causal effects and for discovering causal networks are discussed in this paper.A causal relation between two variables is different from an association or correlation between them.An association measurement between two variables and may be changed dramatically from positive to negative by omitting a third variable,which is called Yule-Simpson paradox.We shall discuss how to evaluate the causal effect of a treatment or exposure on an outcome to avoid the phenomena of Yule-Simpson paradox. Surrogates and intermediate variables are often used to reduce measurement costs or duration when measurement of endpoint variables is expensive,inconvenient,infeasible or unobservable in practice.There have been many criteria for surrogates.However,it is possible that for a surrogate satisfying these criteria,a treatment has a positive effect on the surrogate,which in turn has a positive effect on the outcome,but the treatment has a negative effect on the outcome,which is called the surrogate paradox.We shall discuss criteria for surrogates to avoid the phenomena of the surrogate paradox. Causal networks which describe the causal relationships among a large number of variables have been applied to many research fields.It is important to discover structures of causal networks from observed data.We propose a recursive approach for discovering a causal network in which a structural learning of a large network is decomposed recursively into learning of small networks.Further to discover causal relationships,we present an active learning approach in terms of external interventions on some variables.When we focus on the causes of an interest outcome, instead of discovering a whole network,we propose a local learning approach to discover these causes that affect the outcome.     

Do People Agree About the Causes of Behavior? A Social Relations Analysis of Behavior Ratings and Causal Attributions.

Two studies examined consistency and agreement in behavior ratings and causal attributions. In Study 1, participants (N = 280) engaged in a series of getting-acquainted conversations in one of 3 communication media (face-to-face, telephone, computer mediated); in Study 2, participants (N = 120) engaged in a competitive group task. In both studies, participants rated themselves and their interaction partners on a set of behaviors and then made attributions about the causes of those behaviors. The major findings were that (a) participants consistently favored some causal factors over others in explaining both their own and their partners' behavior, supporting the existence of generalized attributional styles; and (b) participants showed moderate self-partner and partner-partner agreement about behavior but virtually no agreement about the causes of behavior. Thus, in brief interactions people tend to see themselves and others through the lens of their stable patterns of perceiving and interpreting behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Temporal predictability facilitates causal learning.

[Correction Notice: An erratum for this article was reported in Vol 140(3) of Journal of Experimental Psychology: General (see record 2011-16270-001). Figure 2 (p. 759) contained an error. The corrected figure appears in the correction.] Temporal predictability refers to the regularity or consistency of the time interval separating events. When encountering repeated instances of causes and effects, we also experience multiple cause–effect temporal intervals. Where this interval is constant it becomes possible to predict when the effect will follow from the cause. In contrast, interval variability entails unpredictability. Three experiments investigated the extent to which temporal predictability contributes to the inductive processes of human causal learning. The authors demonstrated that (a) causal relations with fixed temporal intervals are consistently judged as stronger than those with variable temporal intervals, (b) that causal judgments decline as a function of temporal uncertainty, and (c) that this effect remains undiminished with increased learning time. The results therefore clearly indicate that temporal predictability facilitates causal discovery. The authors considered the implications of their findings for various theoretical perspectives, including associative learning theory, the attribution shift hypothesis, and causal structure models. (PsycINFO Database Record (c) 2011 APA, all rights reserved)