Sufficiency and Necessity Assumptions in Causal Structure Induction

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain‐general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.     

Explaining prompts children to privilege inductively rich properties

Four experiments with preschool-aged children test the hypothesis that engaging in explanation promotes inductive reasoning on the basis of shared causal properties as opposed to salient (but superficial) perceptual properties. In Experiments 1a and 1b, 3- to 5-year-old children prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. Experiment 2 replicated this effect of explanation in a case of label extension (i.e., categorization). Experiment 3 demonstrated that explanation improves memory for clusters of causally relevant (non-perceptual) features, but impairs memory for superficial (perceptual) features, providing evidence that effects of explanation are selective in scope and apply to memory as well as inference. In sum, our data support the proposal that engaging in explanation influences children’s reasoning by privileging inductively rich, causal properties.     

Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4-year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference.     

Enabling conditions and children’s understanding of pretense

Two experiments examined whether preschoolers’ difficulties on tasks that required relating pretending and knowledge (e.g., Lillard, A. S. (1993a). Young children’s conceptualization of pretense: Action or mental representational state? Child Development, 64, 372-386) were due to children’s inability to appreciate the causal mechanism behind enabling conditions. In Experiment 1, 4-year-olds were told about a character who knew about one kind of animal and did not know about another. The character acted in a manner consistent with both animals. Children were asked whether the character was pretending to be the animal of which he was ignorant. The character’s knowledge was either represented in a generic manner (as a picture) or in a manner that suggested a particular enabling condition relation that children found accessible (as a battery, which most 4-year-olds recognize is critical for making toys work). Children were more successful at relating knowledge and pretending in the battery condition. This improvement in performance extended to another task in which children had to identify the enabling condition relation between knowledge and identification, in which there were reduced demands on the inhibitory mechanisms necessary for success. Experiment 2 found that the results in Experiment 1 were not due to demands of the procedure used in Experiment 1. These results are discussed in the context of recent theories of theory of mind that focus on the importance of causal relations among mental states.     

Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study

The application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent with one another. In Experiment 1, children aged between 4 and 8 years made causal structure judgments on a three‐component causal system followed by counterfactual intervention judgments. In Experiment 2, children’s causal structure judgments were followed by intervention judgments phrased as future hypotheticals. In Experiment 3, we explicitly told children what the correct causal structure was and asked them to make intervention judgments. The results of the three experiments suggest that the representations that support causal structure judgments do not easily support simple judgments about interventions in children. We discuss our findings in light of strong interventionist claims that the two types of judgments should be closely linked.     

Compound cue processing within the fast and frugal heuristics approach in nonlinearly separable environments

Three experiments investigated whether participants used Take The Best (TTB) Configural, a fast and frugal heuristic that processes configurations of cues when making inferences concerning which of two alternatives has a higher criterion value. Participants were presented with a compound cue that was nonlinearly separable from its elements. The compound was highly valid in Experiments 1 and 2, but invalid in Experiment 3. Participants’ causal mental models were manipulated via instructions: participants were either told that cues acted through the same causal mechanism (configural causal model), through different causal mechanisms (elemental causal model), or the causal mechanisms were not specified (neutral causal model). A high percentage of participants used TTB-Configural when they had a configural causal model and a highly valid compound existed, suggesting that causal knowledge can be incorporated in otherwise very basic cognitive mechanisms to allow fine-grained adaptation to complex task structures.     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.     

Children Use Temporal Cues to Learn Causal Directionality

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real‐world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.     

Rationales in children’s causal learning from others’ actions

Shown commensurate actions and information by an adult, preschoolers’ causal learning was influenced by the pedagogical context in which these actions occurred. Four-year-olds who were provided with a reason for an experimenter’s action relevant to learning causal structure showed more accurate causal learning than children exposed to the same action and data accompanied by an inappropriate rationale or accompanied by no explanatory information. These results suggest that children’s accurate causal learning is influenced by contextual factors that specify the instructional value of others’ actions.     

Causal learning from probabilistic events in 24‐month‐olds: an action measure

How do young children learn about causal structure in an uncertain and variable world? We tested whether they can use observed probabilistic information to solve causal learning problems. In two experiments, 24‐month‐olds observed an adult produce a probabilistic pattern of causal evidence. The toddlers then were given an opportunity to design their own intervention. In Experiment 1, toddlers saw one object bring about an effect with a higher probability than a second object. In Experiment 2, the frequency of the effect was held constant, though its probability differed. After observing the probabilistic evidence, toddlers in both experiments chose to act on the object that was more likely to produce the effect. The results demonstrate that toddlers can learn about cause and effect without trial‐and‐error or linguistic instruction on the task, simply by observing the probabilistic patterns of evidence resulting from the imperfect actions of other social agents. Such observational causal learning from probabilistic displays supports human children's rapid cultural learning.     

Effects of Causal Structure on Decisions About Where to Intervene on Causal Systems

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations (immediate causes, root causes) in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were presented with a long‐term goal versus a short‐term goal. These results show that the structural properties of a causal system can guide the design of interventions.     

Norms Affect Prospective Causal Judgments

People more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—that is, judgments about the cause of some future outcome. Four experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.     

Attention-capturing properties of high frequency luminance flicker: Implications for brake light conspicuity

In two experiments, the authors studied the effect of high frequency luminance flicker on reaction time in a simulated driving task. In Experiment 1, 24 research participants performing a primary simulated driving task were required to apply the brake pedal as quickly as possible following the activation of a red light somewhere in their field of vision. The red lights were displayed in one of two ways, (a) with 20 Hz luminance flicker and (b) continuous (no flicker), and presented at three eccentricities, 10°, 45° and 80°. Results showed that the light with 20 Hz flicker was more attention-capturing than the continuous light, but that the effect of 20 Hz flicker on reaction time was not influenced by eccentricity of presentation. The second experiment was identical to the first except that the primary simulated driving task was considerably more attention demanding. Under this higher workload, the light with 20 Hz flicker again proved to be more attention-capturing than the continuous light, and to a greater extent than in Experiment 1. In Experiment 2, there also appeared to be tendency for the light with 20 Hz flicker to be more effective at greater eccentricities. Implications for brake light conspicuity and rear-end collision avoidance are discussed.     

The power of causal beliefs and conflicting evidence on causal judgments and decision making

In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. Participants had to decide which of two alternatives would attain a higher outcome on the basis of four cues. After completing the decision task, they were asked to estimate to what extent each cue was a reliable cause of the outcome. Participants were provided with instructions that causally related two of the cues to the outcome, whereas they received neutral information about the other two cues. Two of the four cues—a causal and a neutral cue—had high validity and were both generative. The remaining two cues had low validity, and were generative in Experiment 1, but almost not related to the outcome in Experiment 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training was provided. Results revealed that the impact of causal beliefs and empirical evidence depends on both the experienced pre-training and cue validity. When all cues were generative and participants received pre-training with causal cues, they mostly relied on their causal beliefs, whereas they relied on empirical evidence when they received pre-training with neutral cues. In contrast, when some of the cues were almost not related to the outcome, participants’ responses were primarily influenced by validity and—to a lesser extent—by causal beliefs. In either case, however, the influence of causal beliefs was higher in causal judgments than in decision making. While current theoretical approaches in causal learning focus either on the effect of causal beliefs or empirical evidence, the present research shows that both factors are required to explain the flexibility involved in human inferences.     

The development of causal categorization

Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.     

Valuing intervention and observation

Understanding causal relations is fundamental to effective action but causal data can be confounded. We examined the value that participants placed on data derived from a hypothetical intervention or observation. Our materials involved a possible cause (“bottled water”), a possible confound (“food”), and a context (“a restaurant”). We supposed that participants seek to draw as specific a causal inference as possible from presented data and value information sources more highly that allow them to do so. On this basis, we predicted that in circumstances where an intervention removed the confounding causal factor but observation did not, participants would prefer data derived from an intervention when the possible cause was present (the bottled water was drunk) but show the reverse preference when the possible cause was absent (the bottled water was not drunk). Experiment 1 confirmed this prediction. Using a between-subjects design, Experiment 2 tested for a difference in confidence in causal judgements given identical data, including data on the confound, as a function of method of data collection (intervention or observation). There was no significant difference in confidence ratings between the two methods but confidence ratings were sensitive to the probability of an effect (illness) given the cause. Using a within-subjects design, Experiment 3 revealed systematic individual differences in preference for the two methods. Participants were divided between those who considered intervention more confounded and those who considered observation more confounded. Our experiments point to the subtleties of participants' evaluation of data from studies of human beings.     

Causal perception of action-and-reaction sequences in 8- to 10-month-olds

Four experiments with 202 8- to 10-month-old infants studied their sensitivity to causation-at-a-distance in schematic events seen as goal-directed action and reaction by adults and whether this depends on attributes associated with animate agents. In Experiment 1, a red square moved toward a blue square without making contact; in “reaction” events blue moved away while red was approaching, whereas in “delay” events blue started after red stopped. Infants were habituated to one event and then tested on its reversal. Spatiotemporal features reversed for both events, but causal roles changed only in reversed reactions. Infants dishabituated more to reversed reaction events than to reversed delay events. Squares moved rigidly or in a nonrigid animal-like fashion. Infants discriminated these, but motion pattern did not affect responses to reversal. Infants also discriminated reactions from launching and dishabituated to reversed reactions lacking self-initiated motion. These results suggest that sensitivity to causation-at-a-distance depends on the event structure but not pattern or onset typical of animal motion.     

Effort denial in self-deception

We propose a mixed belief model of self-deception. According to the theory, people distribute belief over two possible causal paths to an action, one where the action is freely chosen and one where it is due to factors outside of conscious control. Self-deceivers take advantage of uncertainty about the influence of each path on their behavior, and shift weight between them in a self-serving way. This allows them to change their behavior to provide positive evidence and deny doing so, enabling diagnostic inference to a desired trait. In Experiment 1, women changed their pain tolerance to provide positive evidence about the future quality of their skin, but judgments of effort claimed the opposite. This “effort denial” suggests that participants’ mental representation of their behavior was dissociated from their actual behavior, facilitating self-deception. Experiment 2 replicated the pattern in a hidden picture task where search performance was purportedly linked to self-control.     

Time and order effects on causal learning

Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2 found that a 48-h retention interval eliminates the trial order effect, so that participants gave a global judgment about the relationship between the stimulus and the two outcomes equivalent to the one given by participants that received the two phases intermixed. This result was replicated in Experiment 3 in a situation in which the probability of the outcome in the presence of the cue was changed from .5 for both outcomes (Experiments 1, 2, 4, and 5) to .75 and .25 for outcomes 1 and 2, respectively. Experiment 4 found that retention intervals ranging from 45 min to 48 h eliminated the trial order effect similarly. Experiment 5 found that a 10-min retention interval replicated the effect of time upon sequential training found in precedent experiments, regardless of whether participants remained within the laboratory during the retention interval or spent it outside. The combined results of this experimental series suggest that retention intervals reduce retroactive interference in causal learning by allowing participants to use all the information presented across phases, rather than differentially increasing or decreasing retrieval of information about each of them.