Pretense,Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters

Young children spend a large portion of their time pretending about non‐real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary.     

No frills: Simple regularities in language can go a long way in the development of word knowledge

Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of these models raise the intriguing possibility that exposure to word use in language also shapes the word knowledge that children amass during development. However, this possibility is strongly challenged by the fact that models use language input and learning mechanisms that may be unavailable to children. Across three studies, we found that unrealistically complex input and learning mechanisms are unnecessary. Instead, simple regularities of word use in children's language input that they have the capacity to learn can foster knowledge about word meanings. Thus, exposure to language may play a simple but powerful role in children's growing word knowledge. A video abstract of this article can be viewed at https://youtu.be/dT83dmMffnM .

Research Highlights

• Natural Language Processing (NLP) models can learn that words are similar in meaning from higher-order statistical regularities of word use.
• Unlike NLP models, infants and children may primarily learn only simple co-occurrences between words.
• We show that infants' and children's language input is rich in simple co-occurrence that can support learning similarities in meaning between words.
• We find that simple co-occurrences can explain infants' and children's knowledge that words are similar in meaning.

     

Causal learning is collaborative: Examining explanation and exploration in social contexts

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children’s explanation-seeking behaviors in the form of “why” questions. Second, we examine parents’ elaboration of meaning about causal relations. Finally, we consider parents’ interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children’s causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.     

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.     

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).     

Just do it? Investigating the gap between prediction and action in toddlers’ causal inferences

Adults’ causal representations integrate information about predictive relations and the possibility of effective intervention; if one event reliably predicts another, adults can represent the possibility that acting to bring about the first event might generate the second. Here we show that although toddlers (mean age: 24 months) readily learn predictive relationships between physically connected events, they do not spontaneously initiate one event to try to generate the second (although older children, mean age: 47 months, do; Experiments 1 and 2). Toddlers succeed only when the events are initiated by a dispositional agent (Experiment 3), when the events involve direct contact between objects (Experiment 4), or when the events are described using causal language (Experiment 5). This suggests that causal language may help children extend their initial causal representations beyond agent-initiated and direct contact events.     

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.     

Can You Do It? How Preschoolers Judge Whether Others Have Learned

Two experiments investigated how preschoolers judge whether learning has occurred. Experiment 1 showed that 3- and 4-year-olds used an individual's ability to demonstrate knowledge to judge whether he/she had learned something, regardless of that individual's claim about whether he/she had learned. Experiment 2 considered whether children responded based on just the character's demonstrative ability or whether children integrate various pieces of mental state knowledge to make a judgment about learning. Using a similar procedure, preschoolers were first told that the character claimed to be ignorant and then that they learned or did not learn a piece of information. In these cases, judgments of learning changed when the characters' claims and demonstrative abilities conflicted. These results suggest that children's understanding of learning involves the integration of various pieces of mental state knowledge. This process starts in the preschool years, but these data also suggest that crucial developments are taking place after age 4.     

The contribution of logical reasoning to the learning of mathematics in primary school

It has often been claimed that children's mathematical understanding is based on their ability to reason logically, but there is no good evidence for this causal link. We tested the causal hypothesis about logic and mathematical development in two related studies. In a longitudinal study, we showed that (a) 6‐year‐old children's logical abilities and their working memory predict mathematical achievement 16 months later; and (b) logical scores continued to predict mathematical levels after controls for working memory, whereas working memory scores failed to predict the same measure after controls for differences in logical ability. In our second study, we trained a group of children in logical reasoning and found that they made more progress in mathematics than a control group who were not given this training. These studies establish a causal link between logical reasoning and mathematical learning. Much of children's mathematical knowledge is based on their understanding of its underlying logic.     

Capacities underlying word learning

Children are strikingly good at learning the meanings of words. Current controversy focuses on the relative importance of different capacities in this learning process including principles of association, low-level attentional mechanisms, special word learning constraints, syntactic cues and theory of mind. We argue that children succeed at word learning because they possess certain conceptual biases about the external world, the ability to infer the referential intentions of others and an appreciation of syntactic cues to word meaning. Support for this view comes from studies exploring the phenomena of fast mapping, the whole object bias, the acquisition of names for entities belonging to different ontological kinds and the effect of lexical contrast. Word learning is not the result of a general associative learning process, nor does it involve specialized constraints. The ability to learn the meanings of words depends on a number of capacities, some of which are specific to language and unique to humans, others of which are potentially shared with other species.     

Alice in Systems Wonderland: A Children's Systems Learning Guidebook Accompanying Alice's Adventures in Wonderland

The author proposes the development of systems learning guidebooks to accompany famous children's classic books. Children's classic books can make excellent bases for children's learning guidebooks on systems thinking and global ecology, because they are fun to read and well known worldwide. If such learning guidebooks are properly designed with humor and entertaining aspects, they could stimulate children to learn more about systems thinking. Lewis Carroll's Alice's Adventures in Wonderland is chosen as a pilot case for developing such a children's guidebook. The systems learning guidebook that accompanies Alice's Wonderland shall be entitled Alice in Systems Wonderland and will help its readers look at the Alice's Wonderland story with a systems perspective.     

A Computational Theory of Learning Causal Relationships

I present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples are consistent with a general theory of causality. This article describes a learning process that uses a general theory of causality as background knowledge. The learning process, which I call theory-driven learning (TDL), hypothesizes causal relationships consistent both with observed data and the general theory of causality. TDL accounts for data on both the rate at which human learners acquire causal relationships, and the types of causal relationships they acquire. Experiments with TDL demonstrate the advantage of TDL for acquiring causal relationships over similarity-based approaches to learning: Fewer examples are required to learn an accurate relationship.     

Mechanisms of theory formation in young children

Research suggests that by the age of five, children have extensive causal knowledge, in the form of intuitive theories. The crucial question for developmental cognitive science is how young children are able to learn causal structure from evidence. Recently, researchers in computer science and statistics have developed representations (causal Bayes nets) and learning algorithms to infer causal structure from evidence. Here we explore evidence suggesting that infants and children have the prerequisites for making causal inferences consistent with causal Bayes net learning algorithms. Specifically, we look at infants and children's ability to learn from evidence in the form of conditional probabilities, interventions and combinations of the two.     

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.     

Young Children's Reports of When Events Occurred: Do Event Type and Assessment Method Matter?

Studying young children's reporting about when various events occurred informs about the development of episodic memory and metacognition. In two experiments, 55 3‐ to 5‐year‐old children participated in two activity sessions, a week apart. During the activity sessions, they learned novel animal facts and body movements, and they coloured animal pictures and posed for body movement photos. Immediately after the second activity session, children were interviewed about when they experienced the various events. Overall, children were as accurate about learning events as physical events, but they were more accurate when asked temporal distance (e.g. ‘Which did you learn a longer time ago, “X” or “Y”?’) than temporal location questions (e.g. ‘Which did you learn before today, “X” or “Y”?’). The results suggest that young children's apparent difficulty recognizing new learning is not due to a rapid ‘remember‐to‐know shift’. Rather, the way we ask young children about when they experienced various events determines their accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

Highchair philosophers: the impact of seating context‐dependent exploration on children's naming biases

We examine developmental interactions between context, exploration, and word learning. Infants show an understanding of how nonsolid substances are categorized that does not reliably transfer to learning how these categories are named in laboratory tasks. We argue that what infants learn about naming nonsolid substances is contextually bound – most nonsolids that toddlers are familiar with are foods and thus, typically experienced when sitting in a highchair. We asked whether 16‐month‐old children's naming of nonsolids would improve if they were tested in that typical context. Children tested in the highchair demonstrated better understanding of how nonsolids are named. Furthermore, context‐based differences in exploration drove differences in the properties attended to in real‐time. We discuss what implications this context‐dependency has for understanding the development of an ontological distinction between solids and nonsolids. Together, these results demonstrate a developmental cascade between context, exploration, and word learning.     

Guided by Intention: Preschoolers' Imitation Reflects Inferences of Causation

Object use is a ubiquitous characteristic of the human species, and learning how objects function is a fundamental part of development. In this article the authors examine the role that intentionality plays in children's understanding of causal relationships during observational learning of object use. Children observed demonstrations in which causally irrelevant and causally relevant actions were performed to achieve a desired goal. The intentionality of these actions was manipulated using verbal markers. Irrelevant actions were performed either intentionally (“There!”) or accidentally (“Whoops! I didn't mean to do that!”). Three-, 4-, and 5-year-olds, but not 2-year-olds, were less likely to imitate causally irrelevant actions performed accidentally than when they were performed intentionally. This suggests that older children used intentionality to guide causal inference and perceived intentional actions as causally effective and accidental actions as causally ineffective. Findings are discussed from an evolutionary perspective in relation to the cultural transmission of tool-use knowledge.