Choosing optimal causal backgrounds for causal discovery

In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context. For generative causes this would be a context with a low base rate of effects generated by other causes and for preventive causes a context with a high base rate. In the following experiments, we used probabilistic and/or deterministic target causes and contexts. In each experiment, participants observed several contexts in which the effect occurred with different probabilities. After this training, the participants were presented with different target causes whose causal status was unknown. In order to discover the influence of each cause, participants were allowed, on each trial, to choose the context in which the cause would be tested. As expected by inferential theories, the participants preferred to test generative causes in low base rate contexts and preventative causes in high base rate contexts. The participants, however, persisted in choosing the less informative contexts on a substantial minority of trials long after they had discovered the power of the cause. We discuss the matching law from operant conditioning as an alternative explanation of the findings.     

Second-order conditioning of human causal learning

This article provides the first demonstration of a reliable second-order conditioning (SOC) effect in human causal learning tasks. It demonstrates the human ability to infer relationships between a cause and an effect that were never paired together during training. Experiments 1a and 1b showed a clear and reliable SOC effect, while Experiments 2a and 2b demonstrated that first-order extinction did not affect SOC. These results were similar to those found in animal and human conditioning and suggested that a similar associative mechanism could explain these effects. However, they can also be used to look into the underlying causal mental model people build and store while they are learning this task. From a cognitive view, overall results suggest that an independent rather than a chain causal mental model is stored after second-order learning in human causal tasks.     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

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.     

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.     

Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.     

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.     

Generality of the summation effect in human causal learning

Considerable research has examined the contrasting predictions of the elemental and configural association theories proposed by Rescorla and Wagner (1972) and Pearce (1987), respectively. One simple method to distinguish between these approaches is the summation test, in which the associative strength attributed to a novel compound of two separately trained cues is examined. Under common assumptions, the configural view predicts that the strength of the compound will approximate to the average strength of its components, whereas the elemental approach predicts that the strength of the compound will be greater than the strength of either component. Different studies have produced mixed outcomes. In studies of human causal learning, Collins and Shanks (2006) suggested that the observation of summation is encouraged by training, in which different stimuli are associated with different submaximal outcomes, and by testing, in which the alternative outcomes can be scaled. The reported experiments further pursued this reasoning. In Experiment 1, summation was more substantial when the participants were trained with outcomes identified as submaximal than when trained with simple categorical (presence/absence) outcomes. Experiments 2 and 3 demonstrated that summation can also be obtained with categorical outcomes during training, if the participants are encouraged by instruction or the character of training to rate the separately trained components with submaximal ratings. The results are interpreted in terms of apparent performance constraints in evaluations of the contrasting theoretical predictions concerning summation.     

Encouraging children to think counterfactually enhances blocking in a causal learning task

According to a higher order reasoning account, inferential reasoning processes underpin the widely observed cue competition effect of blocking in causal learning. The inference required for blocking has been described as modus tollens (if p then q, not q therefore not p). Young children are known to have difficulties with this type of inference, but research with adults suggests that this inference is easier if participants think counterfactually. In this study, 100 children (51 five-year-olds and 49 six- to seven-year-olds) were assigned to two types of pretraining groups. The counterfactual group observed demonstrations of cues paired with outcomes and answered questions about what the outcome would have been if the causal status of cues had been different, whereas the factual group answered factual questions about the same demonstrations. Children then completed a causal learning task. Counterfactual pretraining enhanced levels of blocking as well as modus tollens reasoning but only for the younger children. These findings provide new evidence for an important role for inferential reasoning in causal learning.     

Priming interdependence affects processing of context information in causal inference—But not how you might think

Cultural mindset is related to performance on a variety of cognitive tasks. In particular, studies of both chronic and situationally-primed mindsets show that individuals with a relatively interdependent mindset (i.e., an emphasis on relationships and connections among individuals) are more sensitive to background contextual information than individuals with a more independent mindset. Two experiments tested whether priming cultural mindset would affect sensitivity to background causes in a contingency learning and causal inference task. Participants were primed (either independent or interdependent), and then saw complete contingency information on each of 12 trials for two cover stories in Experiment 1 (hiking causing skin rashes, severed brakes causing wrecked cars) and two additional cover stories in Experiment 2 (school deadlines causing stress, fertilizers causing plant growth). We expected that relative to independent-primed participants, those interdependent-primed would give more weight to the explicitly-presented data indicative of hidden alternative background causes, but they did not do so. In Experiment 1, interdependents gave less weight to the data indicative of hidden background causes for the car accident cover story and showed a decreased sensitivity to the contingencies for that story. In Experiment 2, interdependents placed less weight on the observable data for cover stories that supported more extra-experimental causes, while independents' sensitivity did not vary with these extra-experimental causes. Thus, interdependents were more sensitive to background causes not explicitly presented in the experiment, but this sensitivity hurt rather than improved their acquisition of the explicitly-presented contingency information.     

A causal model for causal priority

Recent attempts to fix the direction of causal priority without reference to the direction of temporal priority have begun with an analysis of the causal relation itself. I offer a method, based on causal modelling theory, designed to determine the direction of causal priority while remaining as agnostic as possible about the nature of the causal relation.     

The role of preschoolers' social understanding in evaluating the informativeness of causal interventions

Preschoolers use information from interventions, namely intentional actions, to make causal inferences. We asked whether children consider some interventions to be more informative than others based on two components of an actor’s knowledge state: whether an actor possesses causal knowledge, and whether an actor is allowed to use their knowledge in a given situation. Three- and four-year-olds saw a novel toy that activated in the presence of certain objects. Two actors, one knowledgeable about the toy and one ignorant, each tried to activate the toy with an object. In Experiment 1, either the actors chose objects or the child chose for them. In Experiment 2, the actors chose objects blindfolded. Objects were always placed on the toy simultaneously, and thus were equally associated with the effect. Preschoolers’ causal inferences favored the knowledgeable actor’s object only when he was allowed to choose it (Experiment 1). Thus, children consider both personal and situational constraints on knowledge when evaluating the informativeness of causal interventions.     

Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention

When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.     

Knowing who knows: Metacognitive and causal learning abilities guide infants’ selective social learning

Given the widespread interest in the development of children's selective social learning, there is mounting evidence suggesting that infants prefer to learn from competent informants (Poulin‐Dubois & Brosseau‐Liard, Current Directions in Psychological Science, 2016, 25). However, little research has been dedicated to understanding how this selectivity develops. The present study investigated whether causal learning and precursor metacognitive abilities govern discriminant learning in a classic word‐learning paradigm. Infants were exposed to a speaker who accurately (reliable condition) or inaccurately (unreliable condition) labeled familiar objects and were subsequently tested on their ability to learn a novel word from the informant. The predictive power of causal learning skills and precursor metacognition (as measured through decision confidence) on infants' word learning was examined across both reliable and unreliable conditions. Results suggest that infants are more inclined to accept an unreliable speaker's testimony on a word learning task when they also lack confidence in their own knowledge on a task measuring their metacognitive ability. Additionally, when uncertain, infants draw on causal learning abilities to better learn the association between a label and a novel toy. This study is the first to shed light on the role of causal learning and precursor metacognitive judgments in infants' abilities to engage in selective trust.     

Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence

Children are ubiquitous imitators, but how do they decide which actions to imitate? One possibility is that children rationally combine multiple sources of information about which actions are necessary to cause a particular outcome. For instance, children might learn from contingencies between action sequences and outcomes across repeated demonstrations, and they might also use information about the actor’s knowledge state and pedagogical intentions. We define a Bayesian model that predicts children will decide whether to imitate part or all of an action sequence based on both the pattern of statistical evidence and the demonstrator’s pedagogical stance. To test this prediction, we conducted an experiment in which preschool children watched an experimenter repeatedly perform sequences of varying actions followed by an outcome. Children’s imitation of sequences that produced the outcome increased, in some cases resulting in production of shorter sequences of actions that the children had never seen performed in isolation. A second experiment established that children interpret the same statistical evidence differently when it comes from a knowledgeable teacher versus a naïve demonstrator. In particular, in the pedagogical case children are more likely to “overimitate” by reproducing the entire demonstrated sequence. This behavior is consistent with our model’s predictions, and suggests that children attend to both statistical and pedagogical evidence in deciding which actions to imitate, rather than obligately imitating successful action sequences.     

Toward a causal realist account of causal understanding

Within a limited domain, humans can perceive causal relations directly. The term causal realism is used to denote this psychological hypothesis. The domain of causal realism is in actions upon objects and haptic perception of the effects of those actions: When we act upon an object we cannot be mistaken about the fact that we are acting upon it and perceive the causal relation directly through mechanoreceptors. Experiences of actions upon objects give rise to causal knowledge that can be used in the interpretation of perceptual input. Phenomenal causality, the occurrence of causal impressions in visual perception, is a product of the application of acquired causal knowledge in the automatic perceptual interpretation of appropriate stimuli. Causal realism could constitute the foundation on which all causal perception, judgment, inference, attribution, and knowledge develop.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Inferring action structure and causal relationships in continuous sequences of human action

In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.     

Summarizing information by means of causal sentences through causal graphs

The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source these causal sentences, and finally produce a text summary gathering all the information connected by means of this causal graph. This procedure has three main steps. The first one is focused in the extraction, filtering and selection of those causal sentences that could have relevant information for the system. The second one is focused on the composition of a suitable causal graph, removing redundant information and solving ambiguity problems. The third step is a procedure able to read the causal graph to compose a suitable answer to a proposed causal question by summarizing the information contained in it.     

Spontaneous causal inferences

Three studies examine the hypothesis that people spontaneously (i.e., unintentionally and without awareness of doing so) infer causes (the Spontaneous Causal Inference, or SCI, hypothesis). Using a cued-recall paradigm, Study 1 examines whether SCIs occur and Study 2 allows for a comparison between implicitly inferred and explicitly mentioned causes. Study 3 examines whether SCIs can be fully explained in terms of spreading activation to general, abstract schemes. It is suggested that STIs (e.g., Winter & Uleman, 1984), and spontaneous predicting inferences (e.g., [McKoon and Ratcliff, 1986a] and [McKoon and Ratcliff, 1986b]), may be better understood in their relation to SCIs.