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.     

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.     

Theory-based Bayesian models of inductive learning and reasoning

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.     

Data-Driven Belief Revision in Children and Adults

The ability to use numerical evidence to revise beliefs about the physical world is an essential component of scientific reasoning that begins to develop in middle childhood. In 2 studies, we explored how data variability and consistency with participants’ initial beliefs about causal factors associated with pendulums affected their ability to revise those beliefs. Children (9–11 years old) and college-aged adults ran experiments in which they generated, recorded, and interpreted data so as to identify factors that might affect the period of a pendulum. In Study 1, several children and most adults used observed evidence to revise their initial understanding, but participants were more likely to change incorrect noncausal beliefs to causal beliefs than the reverse. In Study 2, we oriented participants toward either an “engineering” goal (to get an effect) or a “science” goal (to discover the causal structure of the domain) and presented them with variable data about potentially causal factors. Science goals produced more belief revision than engineering goals. Numerical data, when presented in context, with appropriate structure, can help children and adults reexamine their beliefs and initiate and support the process of conceptual change and robust scientific thinking.     

Effects of explaining on children's preference for simpler hypotheses

Research suggests that the process of explaining influences causal reasoning by prompting learners to favor hypotheses that offer “good” explanations. One feature of a good explanation is its simplicity. Here, we investigate whether prompting children to generate explanations for observed effects increases the extent to which they favor causal hypotheses that offer simpler explanations, and whether this changes over the course of development. Children aged 4, 5, and 6 years observed several outcomes that could be explained by appeal to a common cause (the simple hypothesis) or two independent causes (the complex hypothesis). We varied whether children were prompted to explain each observation or, in a control condition, to report it. Children were then asked to make additional inferences for which the competing hypotheses generated different predictions. The results revealed developmental differences in the extent to which children favored simpler hypotheses as a basis for further inference in this task: 4-year-olds did not favor the simpler hypothesis in either condition; 5-year-olds favored the simpler hypothesis only when prompted to explain; and 6-year-olds favored the simpler hypothesis whether or not they explained.     

What causal conditional reasoning tells us about people's understanding of causality

Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial of the Antecedent, and Affirmation of the Consequent—causal conditional reasoning data are only partially able to support these principles. We present three experiments that use concrete and abstract causal scenarios and combine inference tasks with a new type of task in which people reformulate a given causal situation. The results provide evidence for the proposed representational principles. Implications for theories of the naïve understanding of causality are discussed.     

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.     

Nonmonotonic Reasoning and Causation

It is suggested that taking into account considerations that traditionally fall within the scope of computer science in general, and artificial intelligence in particular, sheds new light on the subject of causation. It is argued that adopting causal notions con be viewed as filling a computational need: They allow reasoning with incomplete information, facilitate economical representations, and afford relatively efficient methods for reasoning about those representations. Specifically, it is proposed that causal reasoning is intimately bound to nonmonotonic reasoning. An account of causation is offered that relies upon this connection, and compares this proposal to previous accounts within philosophy and artificial intelligence.     

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.     

Children’s causal reasoning: Counterfactual thinking occurs for ‘negative’ outcomes only

Harris, German and Mills (Children’s use of counterfactual thinking in causal reasoning. Cognition, 61 (1996), 223–259), following Mackie, argue that children make explicit use of counterfactual thinking in arriving at causal judgments. They showed that children as young as 3, in explaining simple mishap events, made reference to courses of action that a protagonist had rejected, when that course of action would have prevented the observed outcome. It is hypothesized here that such counterfactual thinking might have been invoked by the ‘negative’ mishaps rather than as part of the causal reasoning process. Although the generation of counterfactuals in explanation was replicated using mishap outcomes such as those used by Harris et al., counterfactual thinking was not evident in children’s explanations of ‘positive’ outcomes. These results undermine the view that a counterfactual thinking process, as indexed by reference to possible actions rejected by a protagonist, is necessary for causal reasoning. Alternative characterizations of the relationship between causals and counterfactuals are discussed.     

Using domain-general principles to explain children's causal reasoning abilities

A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed 'causal properties' and is capable of making several types of inferences that 4-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.     

Belief revision in children: the role of prior knowledge and strategies for generating evidence

Evolving beliefs and reasoning strategies were observed in 22 fifth- and sixth-grade children who worked over 8 weeks for a total of about 5 h on a causal reasoning problem. Children planned, performed, and interpreted experiments to learn about the relations between design features and speed of race cars in a computerized microworld. The group made progress, but by the end of the sessions did not fully understand those features that disconfirmed their initial beliefs. In their activity with the microworld, children often failed to make informative comparisons or valid judgments about the outcomes. Exploratory strategies improved as children exercised them over time, but invalid heuristics that preserved children's favored theories about cars were evident throughout. Those children using more valid strategies achieved more complete, stable comprehension of the microworld's structure. In turn, children used their beliefs to make meaning of the complex patterns of evidence they observed. The most successful children evaluated both the evidence and their changing theories, and were sensitive to the fact that they should be mutually constraining.     

Causal knowledge and imitation/emulation switching in chimpanzees (Pan troglodytes) and children (Homo sapiens)

This study explored whether the tendency of chimpanzees and children to use emulation or imitation to solve a tool-using task was a response to the availability of causal information. Young wild-born chimpanzees from an African sanctuary and 3- to 4-year-old children observed a human demonstrator use a tool to retrieve a reward from a puzzle-box. The demonstration involved both causally relevant and irrelevant actions, and the box was presented in each of two conditions: opaque and clear. In the opaque condition, causal information about the effect of the tool inside the box was not available, and hence it was impossible to differentiate between the relevant and irrelevant parts of the demonstration. However, in the clear condition causal information was available, and subjects could potentially determine which actions were necessary. When chimpanzees were presented with the opaque box, they reproduced both the relevant and irrelevant actions, thus imitating the overall structure of the task. When the box was presented in the clear condition they instead ignored the irrelevant actions in favour of a more efficient, emulative technique. These results suggest that emulation is the favoured strategy of chimpanzees when sufficient causal information is available. However, if such information is not available, chimpanzees are prone to employ a more comprehensive copy of an observed action. In contrast to the chimpanzees, children employed imitation to solve the task in both conditions, at the expense of efficiency. We suggest that the difference in performance of chimpanzees and children may be due to a greater susceptibility of children to cultural conventions, perhaps combined with a differential focus on the results, actions and goals of the demonstrator.     

Learning from others' mistakes? limits on understanding a trap-tube task by young chimpanzees (Pan troglodytes) and children (Homo sapiens)

A trap-tube task was used to determine whether chimpanzees (Pan troglodytes) and children (Homo sapiens) who observed a model's errors and successes could master the task in fewer trials than those who saw only successes. Two- to 7-year-old chimpanzees and 3- to 4-year-old children did not benefit from observing errors and found the task difficult. Two of the 6 chimpanzees developed a successful anticipatory strategy but showed no evidence of representing the core causal relations involved in trapping. Three- to 4-year-old children showed a similar limitation and tended to copy the actions of the demonstrator, irrespective of their causal relevance. Five- to 6-year-old children were able to master the task but did not appear to be influenced by social learning or benefit from observing errors.     

小学儿童在不同刺激材料中的左右空间方位传递性推理能力及策略分析

采用定性和定量相结合的研究方法,从图形刺激材料和文字刺激材料入手,探查了7岁、9岁、11岁儿童的一维空间方位传递性推理能力的发展水平及其认知策略。主要研究结果表明:(1)小学儿童的一维空间方位传递性推理能力从7岁到9岁,从9岁到11岁都有显著性提高。7岁儿童处于能力的初步形成期,9岁儿童是能力的提高和发展期,11岁儿童已基本具有了一维空间方位单模型条件下的传递性推理能力;(2)小学儿童在图形刺激材料条件下的推理成绩显著优于文字刺激材料条件下的推理成绩;(3)前提数量的增加没有对小学儿童的推理造成显著性影响;(4)随着儿童年龄的增长,模型建构策略的使用者越来越多,但在图形刺激材料和文字刺激材料下,儿童解决问题的策略有所不同。在图形刺激材料下,大部分7岁和9岁的儿童采用知觉策略,11岁儿童中只有少部分人使用这一策略。在文字刺激材料下,模型建构策略是儿童推理的主要策略。少部分7岁儿童使用模型建构策略,大部分9岁儿童和绝大部分11岁儿童使用模型建构策略来解决问题。     

Perception Versus Knowledge of Cause and Effect in Children: When Seeing Is Believing

Recent research has shown that even infants have perceptual sensitivity to the causal structure of the world, and it is often claimed that causal knowledge supports many of preschoolers' impressive cognitive achievements. That older children nevertheless can encounter difficulties in causal-reasoning tasks is typically attributed to lack of domain knowledge. A different explanation, however, is that causal structure may appear at more than one level, in perception, as well as in underlying knowledge. Children may make some reasoning errors because they have difficulty coordinating these levels. This article reviews relevant evidence on physical causality: Even infants in their first year perceive the causal structure of simple collision events. This perceptual skill could support rapid causal learning without prior knowledge and thus helps us understand children's precocity preschoolers already reason with the assumption that causes and effects are linked by underlying physical mechanisms. However, what may promote early development may later become a hindrance: When perception and mechanism point to different causes, children may not realize that mechanism is superordinate. Although the components of competent causal reasoning are available early in development, much experience may be required before children learn how to integrate them.     

儿童类比推理的影响因素及干预促进

类比推理这一重要认知能力能够帮助儿童在未经历过的复杂情况下进行推断和学习。近年来研究者主要从行为研究、计算机模型和眼动技术的角度探究了儿童抑制控制和工作记忆在类比推理中的交互作用模式及类比推理策略对类比推理的影响。在此基础上,研究者围绕语言标签和物理表征两方面对儿童类比推理进行了干预研究。儿童类比推理的未来研究应着眼于改进研究方法、关注类比推理各加工阶段影响因素及加强儿童类比推理策略的干预应用研究。     

因果变化模式与因果联结强度在幼儿因果推理中的作用

对93名幼儿进行了五种因果变化模式的因果推理题目的测试。结果表明:(1)在不同的因果变化模式下,被试进行因果推理的成绩存在差异,且在对于这五类题目的掌握上具有一定的顺序。(2)被试在同样因果变化模式题目的表现之间具有较高的相似性,而在因果联结强度相同的题目之间则具有显著的差异。(3)被试对于各题目回答的正确率并不随原因与结果联结次数的增多而提高。(4)即使是在观察到的刺激完全一致的情况下,被试的回答仍会因因果变化模式的差异及主试对于题目解释的不同而存在差别。