因果模型在类比推理中的作用

通过操纵因果模型的特征维度及推理方向, 探讨因果模型在类比推理中的作用。实验一探讨了当结果特征未知时进行类比推理的情况, 发现在一果多因时, 被试采用因果模型进行类比推理, 而在一因多果时, 被试同时采用因果模型和计算模型进行类比推理。实验二探讨当原因特征未知时进行类比推理的情况, 发现在一果多因和一因多果时, 被试均采用因果模型进行类比推理。结果表明：(1)当结果特征未知时, 人们会建构因果模型进行类比推理。且当因果模型和计算模型处于冲突情境时, 人们会采用因果模型进行类比推理; 但当因果模型和计算模型处于非冲突情境时, 人们会同时采用因果模型和计算模型。(2)当原因特征未知时, 即按照因果模型推理的难度增加时, 人们仍会建构因果模型进行类比推理。     

A theory of causal learning in children: causal maps and Bayes nets

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.     

The role of causal models in analogical inference

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3 experiments, the authors explored the possibility that people may use causal models to assess the strength of analogical inferences. Experiments 1-2 showed that reducing analogical overlap by eliminating a shared causal relation (a preventive cause present in the source) from the target increased inductive strength even though it decreased similarity of the analogs. These findings were extended in Experiment 3 to cross-domain analogical inferences based on correspondences between higher order causal relations. Analogical inference appears to be mediated by building and then running a causal model. The implications of the present findings for theories of both analogy and causal inference are discussed.     

Levels of causal understanding in chimpanzees and children

We compare three levels of causal understanding in chimpanzees and children: (1) causal reasoning, (2) labelling the components (actor, object, and instrument) of a causal sequence, and (3) choosing the correct alternative for an incomplete representation of a causal sequence. We present two tests of causal reasoning, the first requiring chimpanzees to read and use as evidence the emotional state of a conspecific. Despite registering the emotion, they failed to use it as evidence. The second test, comparing children and chimpanzees, required them to infer the location of food eaten by a trainer. Children and, to a lesser extent, chimpanzees succeeded. When given information showing the inference to be unsound - physically impossible - 4-year-old children abandoned the inference but younger children and chimpanzees did not. Children and chimpanzees are both capable of labelling causal sequences and completing incomplete representations of them. The chimpanzee Sarah labelled the components of a causal sequence, and completed incomplete representations of actions involving multiple transformations. We conclude the article with a general discussion of the concept of cause, suggesting that the concept evolved far earlier in the psychological domain than in the physical.     

From Blickets to Synapses: Inferring Temporal Causal Networks by Observation

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike‐time dependent plasticity, long‐term depression, and heterosynaptic competition rules to implement Rescorla–Wagner‐like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well‐known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening‐off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.     

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.     

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.     

A problem for Wegner and colleagues’ model of the sense of agency

The sense of agency, that is the sense that one is the agent of one’s bodily actions, is one component of our self-consciousness. Recently, Wegner and colleagues have developed a model of the causal history of this sense. Their model takes it that the sense of agency is elicited for an action when one infers that one or other of one’s mental states caused that action. In their terms, the sense of agency is elicited by the inference to apparent mental state causation. Here, I argue that this model is inconsistent with data from developmental psychology that suggests children can identify the agent behind an action without being capable of understanding the relationship between their intentions and actions. Furthermore, I argue that this model is inconsistent with the preserved sense of agency in autism. In general, the problem is that there are cases where subjects can experience themselves as the agent behind their actions despite lacking the resources to make the inference to apparent mental state causation.     

Which one made it go? The emergence of diagnostic reasoning in preschoolers

We evaluate the hypothesis that children's diagnostic causal reasoning becomes more sophisticated as their understanding of uncertainty advances. When the causal status of candidate causes was known, 3- and 4-year-olds were capable of diagnostic inference (Experiment 1) and could revise their beliefs when told their initial diagnosis was incorrect (Experiment 2). In Experiments 3 and 4, only 4-year-olds made successful inferences when the causal status of candidate causes was uncertain. The results suggest that by age 3, children appreciate that an effect can have multiple candidate causes, but it is not until age 4 that they begin to reason correctly when the causal status of candidate causes is unknown.     

Children's selective trust decisions: rational competence and limiting performance factors

Recent research has amply documented that even preschoolers learn selectively from others, preferring, for example, reliable over unreliable and competent over incompetent models. It remains unclear, however, what the cognitive foundations of such selective learning are, in particular, whether it builds on rational inferences or on less sophisticated processes. The current study, therefore, was designed to test directly the possibility that children are in principle capable of selective learning based on rational inference, yet revert to simpler strategies such as global impression formation under certain circumstances. Preschoolers (N = 75) were shown pairs of models that either differed in their degree of competence within one domain (strong vs. weak or knowledgeable vs. ignorant) or were both highly competent, but in different domains (e.g., strong vs. knowledgeable model). In the test trials, children chose between the models for strength‐ or knowledge‐related tasks. The results suggest that, in fact, children are capable of rational inference‐based selective trust: when both models were highly competent, children preferred the model with the competence most predictive and relevant for a given task. However, when choosing between two models that differed in competence on one dimension, children reverted to halo‐style wide generalizations and preferred the competent models for both relevant and irrelevant tasks. These findings suggest that the rational strategies for selective learning, that children master in principle, can get masked by various performance factors.     

Drawing Causal Inferences Using Propensity Scores: A Practical Guide for Community Psychologists

Confounding present in observational data impede community psychologists’ ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods—weighting, matching, and subclassification—is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.     

Why Causation Need Not Follow from Statistical Association: Boundary Conditions for the Evaluation of Generative and Preventive Causal Powers

In experimental design, a tacit principle is that to test whether a candidate cause c (i.e., a manipulation) prevents an effect e, e must occur at least some of the time without the introduction of c. This principle is the preventive analogue of the explicit principle of avoiding a ceiling effect in tests of whether c produces e. Psychological models of causal inference that adopt either the covariation approach or the power approach, among their other problems, fail to explain these principles. The present article reports an experiment that demonstrates the operation of these principles in untutored reasoning. The results support an explanation of these principles according to the power PC theory, a theory that integrates the previous approaches to overcome the problems that cripple each.     

Inferring interventional predictions from observational learning data

Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.     

Hypothesis Formulation,Model Interpretation,and Model Equivalence: Implications of a Mereological Causal Interpretation of Structural Equation Models

One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts determines the state of the whole. This interpretation shares several properties with efficient causal interpretations but also differs in terms of other important properties. The availability of alternative causal interpretations of the same statistical models has implications for hypothesis specification, research design, causal inference, data analysis, and the interpretation of research results.     

Are self-explanations always beneficial?

We present evidence suggesting that the effect of self-explanations on learning is not always beneficial and, in fact, in some contexts has a detrimental effect. Over eight sessions, fourth-graders engaged in investigation of a database with the goal of identifying causal effects. In a separate task, children in one condition also generated self-explanations regarding the mechanisms underlying the causal effects they believed to be present. In a comparison condition, they did not. On a transfer task, children in the no-explanations condition showed superior causal inference performance. The findings are discussed as reflecting the development of “data-reading” skill with which an emphasis on explanations may interfere.     

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.     

First Principles Organize Attention to and Learning About Relevant Data: Number and the Animate-Inanimate Distinction as Examples

Early cognitive development benefits from nonlinguistic representations of skeletal sets of domain-specific principles and complementary domain-relevant data abstraction processes. The principles outline the domain, identify relevant inputs, and structure coherently what is learned. Knowledge acquisition within the domain is a joint function of such domain-specific principles and domain-general learning mechanisms. Two examples of early learning illustrate this. Skeletal preverbal counting principles help children sort different linguistic strings into those that function as the conventional count-word as opposed to labels for objects in the child's linguistic community. Skeletal causal principles, working with complementary perceptual processes that abstract information about biological and nonbiological conditions and patterns of movement, lead to the rapid acquisition of knowledge about the animate-inanimate distinction. By 3 years of age children con soy whether photographs of unfamiliar nonmammalian animals, mammals, statues, and wheeled objects portray objects capable or incapable of self-generated motion. They also generate answers to questions about the insides of animate items more readily than ones about the insides of inanimate items. Although these children already ore articulate about matters relevant to a theory of action, their limited knowledge of growth illustrates that early skeletal principles do not rule out the need to acquire new principles, in this case ones that underlie a biological account of animacy (Carey, 1985).