Time as a guide to cause.

How do people learn causal structure? In 2 studies, the authors investigated the interplay between temporal-order, intervention, and covariational cues. In Study 1, temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2, both temporal order and intervention contributed to accurate causal inference well beyond that achievable through covariational data alone. Together, the studies show that people use both temporal-order and interventional cues to infer causal structure and that these cues dominate the available statistical information. A hypothesis-driven account of learning is endorsed, whereby people use cues such as temporal order to generate initial models and then test these models against the incoming covariational data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Expectations and interpretations during causal learning.

In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. (PsycINFO Database Record (c) 2011 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)     

Attributions as inferences and explanations: Conjunction effects.

When asked to estimate the probability of an interpretation for an event, observers may either assess whether the event can give rise to the interpretation (an inference set) or assess whether the interpretation can give rise to the event (an explanation set). These two strategies may moderate the conjunction effects (Leddo, Abelson, & Gross, 1984)—attributors' tendency to assign lower probabilities to single-reason interpretations than to their conjunctions. Our two studies showed that explanation-set instructions (e.g., "assess the probability that the interpretation could be the reason for the event") produced stronger conjunction effects than inference-set instructions (e.g., "assess the probability that the interpretation could be inferred from the event"). This Set (inference or explanation)?×?Reason (multiple or single) interaction was not affected by whether the events involved voluntary or involuntary behavior or by whether they described events that happened or failed to happen. In a third study, we found that subjects in an inference set were more likely to report that they estimated probability of the interpretation (as opposed to probability of the behavior) than were subjects in an explanation set. The extent to which the explanation set may account for conjunction effects obtained in other studies was discussed. Possible implications and determinants of the inference-explanation distinction were also considered. (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Blickets and babies: The development of causal reasoning in toddlers and infants.

Previous research has suggested that preschoolers possess a cognitive system that allows them to construct an abstract, coherent representation of causal relations among events. Such a system lets children reason retrospectively when they observe ambiguous data in a rational manner (e.g., D. M. Sobel, J. B. Tenenbaum, & A. Gopnik, 2004). However, there is little evidence that demonstrates whether younger children possess similar inferential abilities. In Experiment 1, the authors extended previous findings with older children to examine 19- and 24-month-olds' causal inferences. Twenty-four-month-olds' inferences were similar to those of preschoolers, but younger children lacked the ability to make retrospective causal inferences, perhaps because of performance limitations. In Experiment 2, the authors designed an eye-tracking paradigm to test younger participants that eliminated various manual search demands. Eight-month-olds' anticipatory eye movements, in response to retrospective data, revealed inferences similar to those of 24-month-olds in Experiment 1 and preschoolers in previous research. These data are discussed in terms of associative reasoning and causal inference. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal inference and developmental psychology.

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference—tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of “ignorability.” The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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)     

Children's Reasoning About the Causal Significance of the Temporal Order of Events.

Four experiments examined children's ability to reason about the causal significance of the order in which 2 events occurred (the pressing of buttons on a mechanically operated box). In Study 1, 4-year-olds were unable to make the relevant inferences, whereas 5-year-olds were successful on one version of the task. In Study 2, 3-year-olds were successful on a simplified version of the task in which they were able to observe the events although not their consequences. Study 3 found that older children had difficulties with the original task even when provided with cues to attend to order information. However, 5-year-olds performed successfully in Study 4, in which the causally relevant event was made more salient. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Correction to Greville and Buehner (2010).

Reports an error in "Temporal predictability facilitates causal learning" by W. James Greville and Marc J. Buehner (Journal of Experimental Psychology: General, 2010[Nov], Vol 139[4], 756-771). Figure 2 (p. 759) contained an error. The corrected figure appears in the correction. (The following abstract of the original article appeared in record 2010-22538-005.) 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)     

A Function of Form: Terror Management and Structuring the Social World.

Drawing on lay epistemology theory (A. W. Kruglanski, 1980, 1989), the authors assessed a terror management analysis (J. Greenberg, S. Solomon, & T. Pyszczynski, 1997) of the psychological function of structuring social information. Seven studies tested variations of the hypothesis that simple, benign interpretations of social information function, in part, to manage death-related anxiety. In Studies 1-4, mortality salience (MS) exaggerated primacy effects and reliance on representative information, decreased preference for a behaviorally inconsistent target among those high in personal need for structure (PNS), and increased high-PNS participants' preference for interpersonal balance. In Studies 5-7, MS increased high-PNS participants' preference for interpretations that suggest a just world and a benevolent causal order of events in the social world. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Making causal judgments from the proportion of confirming instances: The pCI rule.

It is proposed that causal judgments about contingency information are derived from the proportion of confirmatory instances (pCI) that are evaluated as confirmatory for the causal candidate. In 6 experiments, pCI values were manipulated independently of objective contingencies assessed by the ΔP rule. Significant effects of the pCI manipulations were found in all cases, but causal judgments did not vary significantly with objective contingencies when pCI was held constant. The experiments used a variety of stimulus presentation procedures and different dependent measures. The power PC theory, a weighted version of the ΔP rule, the Rescorla-Wagner associative learning model (R. A. Rescorla & A. R. Wagner, 1972), and the ΔD rule, which is the frequency-based version of the pCI rule, were unable to account for the significant effects of the pCI manipulations. These results are consistent with a general explanatory approach to causal judgment involving the evaluation of evidence and updating of beliefs with regard to causal hypotheses. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dissociating Processes Supporting Causal Perception and Causal Inference in the Brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

How causal knowledge affects classification: A generative theory of categorization.

Several theories have been proposed regarding how causal relations among features of objects affect how those objects are classified. The assumptions of these theories were tested in 3 experiments that manipulated the causal knowledge associated with novel categories. There were 3 results. The 1st was a multiple cause effect in which a feature's importance increases with its number of causes. The 2nd was a coherence effect in which good category members are those whose features jointly corroborate the category's causal knowledge. These 2 effects can be accounted for by assuming that good category members are those likely to be generated by a category's causal laws. The 3rd result was a primary cause effect, in which primary causes are more important to category membership. This effect can also be explained by a generative account with an additional assumption: that categories often are perceived to have hidden generative causes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Event structure in perception and conception.

Events can be understood in terms of their temporal structure. The authors first draw on several bodies of research to construct an analysis of how people use event structure in perception, understanding, planning, and action. Philosophy provides a grounding for the basic units of events and actions. Perceptual psychology provides an analogy to object perception: Like objects, events belong to categories, and, like objects, events have parts. These relationships generate 2 hierarchical organizations for events: taxonomies and partonomies. Event partonomies have been studied by looking at how people segment activity as it happens. Structured representations of events can relate partonomy to goal relationships and causal structure; such representations have been shown to drive narrative comprehension, memory, and planning. Computational models provide insight into how mental representations might be organized and transformed. These different approaches to event structure converge on an explanation of how multiple sources of information interact in event perception and conception. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Aging and the Detection of Contingency in Causal Learning.

Young and older participants' ability to detect negative, random, and positive response-outcome contingencies was evaluated using both contingency estimation and response rate adaptation tasks. Age differences in contingency estimation were consistently greater for negative than positive contingencies, and these differences, though still present, were smaller when response rate adaptation was used as the measure of contingency learning. Detecting causal contingency apparently becomes more difficult with age, especially when an oven numerical estimate of contingency must be provided and when the relationship between a causal event and an outcome is negative. A model that incorporates features of both associative and rule-based approaches to contingency learning (e.g., P. C. Price & J. F. Yates, 1995; D. R. Shanks, 1995) provides the best explanation for this pattern of findings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multicausal inference: Evaluation of evidence in causally complex situations.

Investigated in 2 experiments the types of information that people use in making inferences about causality in uncertain situations in which there are many potentially causal factors. A model previously proposed for unicausal inference by M. W. Schustack and the present 2nd author (see record 1982-02716-001) was found to be appropriate to the multicausal case. In Exp I, 60 abstract causal inference problems were presented to 47 college students. Ss were asked to evaluate the likelihood that the particular set of events described in hypothetical situations would lead to the outcome described. In Exp II, 60 abstract multicausal inference problems were presented to college students, 34 in the abstract condition and 40 in the concrete condition. Findings show that both multicausal and unicausal inference rely primarily on 4 types of evidence concerning the sufficiency and necessity of possible causal events. In multicausal inference, people also consider the representativeness, or resemblance, of the events in a situation to causal models suggested by previous situations. When evaluating multicausal problems presented in either abstract or concrete terms, most people average the unicausal likelihoods of all the events in a situation and adjust for the situation's representativeness. However, when evaluating concrete problems, some people base their multicausal estimates only on the unicausal likelihood for the most likely causal event and the situation's representativeness. (36 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal status and coherence in causal-based categorization.

Research has documented two effects of interfeature causal knowledge on classification. A causal status effect occurs when features that are causes are more important to category membership than their effects. A coherence effect occurs when combinations of features that are consistent with causal laws provide additional evidence of category membership. In this study, we found that stronger causal relations led to a weaker causal status effect and a stronger coherence effect (Experiment 1), that weaker alternative causes led to stronger causal status and coherence effects (Experiment 2), and that “essentialized” categories led to a stronger causal status effect (Experiment 3), albeit only for probabilistic causal links (Experiment 4). In addition, the causal status effect was mediated by features' subjective category validity, the probability they occur in category members. These findings were consistent with a generative model of categorization but inconsistent with an alternative model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)