Going beyond the evidence: abstract laws and preschoolers' responses to anomalous data

Thought Experiments as an Error Detection and Correction Tool

The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal errors and trigger belief revision in the service of error correction. Across two experiments, 1149 participants engaged in reasoning about force and motion (a domain with well-documented misconceptions) in a pre-training-training-post-training design. The two experiments manipulated the type of mental model manipulated in the thought experiments (i.e., whether participants reasoned about forces acting on their own bodies vs. on external objects), as well as the level of relational and argumentative reasoning about the outcomes of the thought experiments. The results showed that: (i) thought experiments can serve as a tool to elicit inconsistencies in one's representations; (ii) the level of relational and argumentative reasoning determines the level of belief revision in the service of error correction; and (iii) the type of mental model manipulated in a thought experiment determines its outcome and its potential to initiate belief revision. Thought experiments can serve as a valuable teaching and learning tool, and they can help us better understand the nature of error detection and correction systems.

Active inductive inference in children and adults: A constructivist perspective

A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children's and adults' active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8.97±1.11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a 'computational constructivist' perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children's learning is driven by less fine-tuned construction mechanisms than adults', resulting in a greater diversity of ideas but less reliable discovery of simple explanations.

Novel objects with causal event schemas elicit selective responses in tool- and hand-selective lateral occipitotemporal cortex

Tool-selective lateral occipitotemporal cortex (LOTC) responds preferentially to images of tools (hammers, brushes) relative to non-tool objects (clocks, shoes). What drives these responses? Unlike other objects, tools exert effects on their surroundings. We tested whether LOTC responses are influenced by event schemas that denote different temporal relations. Participants learned about novel objects embedded in different event sequences. Causer objects moved prior to the appearance of an environmental event (e.g. stars), while Reactor objects moved after an event. Visual features and motor association were controlled. During functional magnetic resonance imaging, participants viewed still images of the objects. We localized tool-selective LOTC and non-tool-selective parahippocampal cortex (PHC) by contrasting neural responses to images of familiar tools and non-tools. We found that LOTC responded more to Causers than Reactors, while PHC did not. We also measured responses to images of hands, which elicit overlapping responses with tools. Across inferior temporal cortex, voxels' tool and hand selectivity positively predicted a preferential response to Causers. We conclude that an event schema typical of tools is sufficient to drive LOTC and that category-preferential responses across the temporal lobe may reflect relational event structures typical of those domains.

How Awe Shaped Us: An Evolutionary Perspective

Research shows the experience of awe is associated with a variety of benefits ranging from increased well-being and prosocial behavior to enhanced cognition. The adaptive purpose of awe, however, is elusive. In this article, we aim to show that the current framework used to conceptualize awe points towards higher-order cognition as the key adaptive function. This goes against past evolutionary positions that posit social benefits or unidimensional behavioral adaptations. In the second half of the article, we highlight a distinct cognitive advantage of awe. The literature connecting awe and cognition is surveyed and used to develop a view that situates awe as a critical component in the cognitive success of the human species.

Putting the pieces together: Cognitive correlates of self-derivation of new knowledge in elementary school classrooms

A central goal of development and formal education is to build a knowledge base. Accumulating knowledge relies, in part, on self-derivation of new semantic knowledge via integration of separate yet related learning episodes. Previous tests of self-derivation evidence both age-related and significant individual variability in self-derivation performance in the laboratory and the classroom due in part to individual differences in verbal comprehension (children and adults) and working memory (adults only). In the only extant investigation of cognitive correlates of children's successful self-derivation in the classroom, 3rd graders' verbal comprehension predicted self-derivation, whereas working memory did not. In the current research, we expanded the battery of cognitive correlates investigated, the age range of participants (8-11 years), and the sample size (N = 330) to examine candidate sources of variability in self-derivation. More specifically, in a diverse sample, we measured children's auditory and spatial working memory, inhibitory control, metacognitive awareness, verbal comprehension, and metacognitive judgments at test for self-derivation. Metacognition was of particular interest in the current research because little is currently known about how children's understanding of their cognition, at the trait or item-specific level, may affect their derivation of new knowledge. Only verbal comprehension and metacognitive knowledge predicted children's self-derivation performance; children's metacognitive judgments at the time of testing for self-derivation were not related to their performance. These findings suggest that having both semantic knowledge and knowledge of one's self as a learner, as well as knowing how to use one's knowledge, support further knowledge base development.

Toddlers learn and flexibly apply multiple possibilities

The ability to consider multiple possibilities forms the basis for a wide variety of human-unique cognitive capacities. When does this skill develop? Previous studies have narrowly focused on children's ability to prepare for incompatible future outcomes. Here, we investigate this capacity in a causal learning context. Adults (N = 109) and 18- to 30-month olds (N = 104) observed evidence that was consistent with two hypotheses, each occupying a different level of abstraction (individual vs. relational causation). Results suggest that adults and toddlers identified multiple candidate causes for an effect, held these possibilities in mind, and flexibly applied the appropriate hypothesis to inform subsequent inferences. These findings challenge previous suggestions that the ability to consider multiple alternatives does not emerge until much later in development.

What happened? Do preschool children and capuchin monkeys spontaneously use visual traces to locate a reward?

The ability to infer unseen causes from evidence is argued to emerge early in development and to be uniquely human. We explored whether preschoolers and capuchin monkeys could locate a reward based on the physical traces left following a hidden event. Preschoolers and capuchin monkeys were presented with two cups covered with foil. Behind a barrier, an experimenter (E) punctured the foil coverings one at a time, revealing the cups with one cover broken after the first event and both covers broken after the second. One event involved hiding a reward, the other event was performed with a stick (order counterbalanced). Preschoolers and, with additional experience, monkeys could connect the traces to the objects used in the puncturing events to find the reward. Reversing the order of events perturbed the performance of 3-year olds and capuchins, while 4-year-old children performed above chance when the order of events was reversed from the first trial. Capuchins performed significantly better on the ripped foil task than they did on an arbitrary test in which the covers were not ripped but rather replaced with a differently patterned cover. We conclude that by 4 years of age children spontaneously reason backwards from evidence to deduce its cause.

The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components.

Young children's metacognitive awareness of confounded evidence

Young children selectively explore confounded evidence-when causality is ambiguous due to multiple candidate causes. This suggests that they have an implicit understanding that confounded evidence is uninformative. This study examined explicit understanding, or metacognitive awareness, of the informativeness of different qualities of evidence during early childhood. In two within-participants conditions, children (N = 60 5- and 6-year-olds) were presented with confounded and unconfounded evidence and were asked to evaluate and explain their knowledge of a causal relation. Children more frequently requested further information in the confounded condition than in the unconfounded condition. Nearly half of them referred to multiple candidate causes when explaining confounded evidence. Our data demonstrate that young children can reason explicitly about the informativeness of different kinds of evidence.

Developing an Understanding of Science

Young children are adept at several types of scientific reasoning, yet older children and adults have difficulty mastering formal scientific ideas and practices. Why do “little scientists” often become scientifically illiterate adults? We address this question by examining the role of intuition in learning science, both as a body of knowledge and as a method of inquiry. Intuition supports children's understanding of everyday phenomena but conflicts with their ability to learn physical and biological concepts that defy firsthand observation, such as molecules, forces, genes, and germs. Likewise, intuition supports children's causal learning but provides little guidance on how to navigate higher-order constraints on scientific induction, such as the control of variables or the coordination of theory and data. We characterize the foundations of children's intuitive understanding of the natural world, as well as the conceptual scaffolds needed to bridge these intuitions with formal science.

Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism

Constructing an intuitive theory from data confronts learners with a "chicken-and-egg" problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken-and-egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We present 4- and 5-year-olds with two different simplified magnet-learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence. Following a critical intervention, they learn the correct theory. In the second study, children infer the correct number of categories given no information about the possible causal laws. Children's hypotheses in these tasks are explained as rational inferences within a Bayesian computational framework.

What is social essentialism and how does it develop?

Social essentialism consists of the commonly held belief that certain ways of categorizing people (e.g., gender and race) reflect meaningful, fundamental distinctions found in nature-that some kind of category "essence" (e.g., something in their blood or their DNA) explains why groups of people (such as boys and girls) are different from one another. Yet as common as they are, essentialist beliefs can give rise to adverse consequences, including stereotyping and social prejudice. In this chapter, we examine the development of social essentialism. To begin, we briefly address the evidence that these beliefs are the result of developmental processes that unfold beginning in early childhood (and not something innate that children are born with). Then, we consider the nature of those processes; specifically, how basic processes underlying conceptual development give rise to different components of essentialist beliefs. We then address how different essentialist beliefs might be integrated into a coherent essentialist view of a category, and finally into a coherent essentialist view of a domain.

Incidental binding between predictive relations

Knowledge of predictive relations is a core aspect of learning. Beyond individual relations, we also represent intuitive theories of the world, which include interrelated sets of relations. We asked whether individual predictive relations learned incidentally in the same context become associatively bound and whether they spontaneously influence later learning. Participants performed a cover task while watching three sequences of events. Each sequence contained the same set of events, but differed in how the events related to each other. The first two sequences each had two strong predictive relations (R1 & R2, and R3 & R4). The third contained either a consistent pairing of relations (R1 & R2) or an inconsistent pairing (R1 & R3). We found that participants' learning of the individual relations in the third sequence was affected by pairing consistency, suggesting the mind associates relations to each other as part of the intrinsic way it learns about the world. This was despite participants' minimal ability to verbally describe most of the relations they had learned. Thus, participants spontaneously developed the expectation that pairs of relations should cohere, and this affected their ability to learn new evidence. Such associative binding of relational information may help us build intuitive theories.

Inferring Unseen Causes: Developmental and Evolutionary Origins

Human adults can infer unseen causes because they represent the events around them in terms of their underlying causal mechanisms. It has been argued that young preschoolers can also make causal inferences from an early age, but whether or not non-human apes can go beyond associative learning when exploiting causality is controversial. However, much of the developmental research to date has focused on fully-perceivable causal relations or highlighted the existence of a causal relationship verbally and these were found to scaffold young children's abilities. We examined inferences about unseen causes in children and chimpanzees in the absence of linguistic cues. Children (N = 129, aged 3-6 years) and zoo-living chimpanzees (N = 11, aged 7-41 years) were presented with an event in which a reward was dropped through an opaque forked-tube into one of two cups. An auditory cue signaled which of the cups contained the reward. In the causal condition, the cue followed the dropping event, making it plausible that the sound was caused by the reward falling into the cup; and in the arbitrary condition, the cue preceded the dropping event, making the relation arbitrary. By 4-years of age, children performed better in the causal condition than the arbitrary one, suggesting that they engaged in reasoning. A follow-up experiment ruled out a simpler associative learning explanation. Chimpanzees and 3-year-olds performed at chance in both conditions. These groups' performance did not improve in a simplified version of the task involving shaken boxes; however, the use of causal language helped 3-year-olds. The failure of chimpanzees could reflect limitations in reasoning about unseen causes or a more general difficulty with auditory discrimination learning.

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes-optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes-optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6- to 7-year-old children when tasked with localizing a 'hidden' target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6- to 7-year-olds learn task-relevant statistics at a rate on par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young children's behavior to a greater extent than that of adults, leading their behavior to look sub-optimal. Our findings have important implications for how we should interpret failures in young children's ability to carry out sophisticated computations. These 'failures' need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.

Context shapes early diversity in abstract thought

Early abstract reasoning has typically been characterized by a "relational shift," in which children initially focus on object features but increasingly come to interpret similarity in terms of structured relations. An alternative possibility is that this shift reflects a learned bias, rather than a typical waypoint along a universal developmental trajectory. If so, consistent differences in the focus on objects or relations in a child's learning environment could create distinct patterns of relational reasoning, influencing the type of hypotheses that are privileged and applied. Specifically, children in the United States may be subject to culture-specific influences that bias their reasoning toward objects, to the detriment of relations. In experiment 1, we examine relational reasoning in a population with less object-centric experience-3-y-olds in China-and find no evidence of the failures observed in the United States at the same age. A second experiment with younger and older toddlers in China (18 to 30 mo and 30 to 36 mo) establishes distinct developmental trajectories of relational reasoning across the two cultures, showing a linear trajectory in China, in contrast to the U-shaped trajectory that has been previously reported in the United States. In a third experiment, Chinese 3-y-olds exhibit a bias toward relational solutions in an ambiguous context, while those in the United States prefer object-based solutions. Together, these findings establish population-level differences in relational bias that predict the developmental trajectory of relational reasoning, challenging the generality of an initial object focus and suggesting a critical role for experience.

From Exploration to Instruction: Children Learn From Exploration and Tailor Their Demonstrations to Observers' Goals and Competence

This study investigated whether children learn from exploration and act as effective informants by providing informative demonstrations tailored to observers' goals and competence. Children (4.0-6.9 years, N = 98) explored a causally ambiguous toy to discover its causal structure and then demonstrated the toy to a naive observer. Children provided more costly and informative evidence when the observer wanted to learn about the toy than observe its effects (Experiment 1) and when the observer was ordinary than exceptionally intelligent (Experiment 2). Relative to the evidence they generated during exploration, children produced fewer, less costly actions when the observer wanted or needed less evidence. Children understand the difference between acting-to-learn and acting-to-inform; after learning from exploration, they consider others' goals and competence to provide "uninstructed instruction".

Causal Learning Across Culture and Socioeconomic Status

Extensive research has explored the ability of young children to learn about the causal structure of the world from patterns of evidence. These studies, however, have been conducted with middle-class samples from North America and Europe. In the present study, low-income Peruvian 4- and 5-year-olds and adults, low-income U.S. 4- and 5-year-olds in Head Start programs, and middle-class children from the United States participated in a causal learning task (N = 435). Consistent with previous studies, children learned both specific causal relations and more abstract causal principles across culture and socioeconomic status (SES). The Peruvian children and adults generally performed like middle-class U.S. children and adults, but the low-SES U.S. children showed some differences.

Science Is Awe-Some: The Emotional Antecedents of Science Learning

Scientists from Einstein to Sagan have linked emotions like awe with the motivation for scientific inquiry, but no research has tested this possibility. Theoretical and empirical work from affective science, however, suggests that awe might be unique in motivating explanation and exploration of the physical world. We synthesize theories of awe with theories of the cognitive mechanisms related to learning, and offer a generative theoretical framework that can be used to test the effect of this emotion on early science learning.

Going beyond the lesson: Self-generating new factual knowledge in the classroom

For children to build a knowledge base, they must integrate and extend knowledge acquired across separate episodes of new learning. Children's performance was assessed in a task requiring them to self-generate new factual knowledge from the integration of novel facts presented through separate lessons in the classroom. Whether self-generation performance predicted academic outcomes in reading comprehension and mathematics was also examined. The 278 participating children were in kindergarten through Grade 3 (mean age=7.7years, range=5.5-10.3). Children self-generated new factual knowledge through integration in the classroom; age-related increases were observed. Self-generation performance predicted both reading comprehension and mathematics academic outcomes, even when controlling for caregiver education.

The early emergence and puzzling decline of relational reasoning: Effects of knowledge and search on inferring abstract concepts

We explore the developmental trajectory and underlying mechanisms of abstract relational reasoning. We describe a surprising developmental pattern: Younger learners are better than older ones at inferring abstract causal relations. Walker and Gopnik (2014) demonstrated that toddlers are able to infer that an effect was caused by a relation between two objects (whether they are the same or different), rather than by individual kinds of objects. While these findings are consistent with evidence that infants recognize same-different relations, they contrast with a large literature suggesting that older children tend to have difficulty inferring these relations. Why might this be? In Experiment 1a, we demonstrate that while younger children (18-30-month-olds) have no difficulty learning these relational concepts, older children (36-48-month-olds) fail to draw this abstract inference. Experiment 1b replicates the finding with 18-30-month-olds using a more demanding intervention task. Experiment 2 tests whether this difference in performance might be because older children have developed the general hypothesis that individual kinds of objects are causal - the high initial probability of this alternative hypothesis might override the data that favors the relational hypothesis. Providing additional information falsifying the alternative hypothesis improves older children's performance. Finally, Experiment 3 demonstrates that prompting for explanations during learning also improves performance, even without any additional information. These findings are discussed in light of recent computational and algorithmic theories of learning.

How Children and Adults Represent God's Mind

For centuries, humans have contemplated the minds of gods. Research on religious cognition is spread across sub-disciplines, making it difficult to gain a complete understanding of how people reason about gods' minds. We integrate approaches from cognitive, developmental, and social psychology and neuroscience to illuminate the origins of religious cognition. First, we show that although adults explicitly discriminate supernatural minds from human minds, their implicit responses reveal far less discrimination. Next, we demonstrate that children's religious cognition often matches adults' implicit responses, revealing anthropomorphic notions of God's mind. Together, data from children and adults suggest the intuitive nature of perceiving God's mind as human-like. We then propose three complementary explanations for why anthropomorphism persists in adulthood, suggesting that anthropomorphism may be (a) an instance of the anchoring and adjustment heuristic; (b) a reflection of early testimony; and/or (c) an evolutionary byproduct.

Verbal framing of statistical evidence drives children's preference inferences

Although research has shown that statistical information can support children's inferences about specific psychological causes of others' behavior, previous work leaves open the question of how children interpret statistical information in more ambiguous situations. The current studies investigated the effect of specific verbal framing information on children's ability to infer mental states from statistical regularities in behavior. We found that preschool children inferred others' preferences from their statistically non-random choices only when they were provided with verbal information placing the person's behavior in a specifically preference-related context, not when the behavior was presented in a non-mentalistic action context or an intentional choice context. Furthermore, verbal framing information showed some evidence of supporting children's mental state inferences even from more ambiguous statistical data. These results highlight the role that specific, relevant framing information can play in supporting children's ability to derive novel insights from statistical information.

Children’s Inductive Reasoning: Developmental and Educational Perspectives

Omnipresent in human thought, inductive reasoning consists in (a) detecting regularities, (b) abstracting relations, and (c) deriving general rules. In the first part of this article, I attempt to identify the basic mechanisms underpinning inductive reasoning and the reasons why it is so central to the workings of intelligence. I then go on to describe several factors that researchers in developmental psychology believe may contribute to the development of inductive reasoning. Each factor’s influence is illustrated by its potential contribution to the resolution of Raven’s Progressives Matrices. In the third and final part, I examine the issue from an educational perspective, showing how developmental hypotheses can inform different types of interventions designed to foster inductive reasoning in children.

Constructing a new theory from old ideas and new evidence

A central tenet of constructivist models of conceptual development is that children's initial conceptual level constrains how they make sense of new evidence and thus whether exposure to evidence will prompt conceptual change. Yet little experimental evidence directly examines this claim for the case of sustained, fundamental conceptual achievements. The present study combined scaling and experimental microgenetic methods to examine the processes underlying conceptual change in the context of an important conceptual achievement of early childhood-the development of a representational theory of mind. Results from 47 children (M age=3.7 years) indicate that only children who were conceptually close to understanding false belief at the beginning of the study, and who were experimentally exposed to evidence of people acting on false beliefs, reliably developed representational theories of minds. Combined scaling and microgenetic data revealed how prior conceptual level interacts with experience, thereby providing critical experimental evidence for how conceptual change results from the interplay between conceptions and evidence.

The relation between essentialist beliefs and evolutionary reasoning

Historians of science have pointed to essentialist beliefs about species as major impediments to the discovery of natural selection. The present study investigated whether such beliefs are impediments to learning this concept as well. Participants (43 children aged 4-9 and 34 adults) were asked to judge the variability of various behavioral and anatomical properties across different members of the same species. Adults who accepted within-species variation-both actual and potential-were significantly more likely to demonstrate a selection-based understanding of evolution than adults who denied within-species variation. The latter demonstrated an alternative, incorrect understanding of evolution and produced response patterns that were both quantitatively and qualitatively similar to those produced by preschool-aged children. Overall, it is argued that psychological essentialism, although a useful bias for drawing species-wide inductions, leads individuals to devalue within-species variation and, consequently, to fail to understand natural selection.

Scientific thinking in young children: theoretical advances, empirical research, and policy implications

New theoretical ideas and empirical research show that very young children's learning and thinking are strikingly similar to much learning and thinking in science. Preschoolers test hypotheses against data and make causal inferences; they learn from statistics and informal experimentation, and from watching and listening to others. The mathematical framework of probabilistic models and Bayesian inference can describe this learning in precise ways. These discoveries have implications for early childhood education and policy. In particular, they suggest both that early childhood experience is extremely important and that the trend toward more structured and academic early childhood programs is misguided.

Giving the giggles: prediction, intervention, and young children's representation of psychological events

Adults recognize that if event A predicts event B, intervening on A might generate B. Research suggests that young children have difficulty making this inference unless the events are initiated by goal-directed actions [1]. The current study tested the domain-generality and development of this phenomenon. Replicating previous work, when the events involved a physical outcome, toddlers (mean: 24 months) failed to generalize the outcome of spontaneously occurring predictive events to their own interventions; toddlers did generalize from prediction to intervention when the events involved a psychological outcome. We discuss these findings as they bear on the development of causal concepts.

Children balance theories and evidence in exploration, explanation, and learning

We look at the effect of evidence and prior beliefs on exploration, explanation and learning. In Experiment 1, we tested children both with and without differential prior beliefs about balance relationships (Center Theorists, mean: 82 months; Mass Theorists, mean: 89 months; No Theory children, mean: 62 months). Center and Mass Theory children who observed identical evidence explored the block differently depending on their beliefs. When the block was balanced at its geometric center (belief-violating to a Mass Theorist, but belief-consistent to a Center Theorist), Mass Theory children explored the block more, and Center Theory children showed the standard novelty preference; when the block was balanced at the center of mass, the pattern of results reversed. The No Theory children showed a novelty preference regardless of evidence. In Experiments 2 and 3, we follow-up on these findings, showing that both Mass and Center Theorists selectively and differentially appeal to auxiliary variables (e.g., a magnet) to explain evidence only when their beliefs are violated. We also show that children use the data to revise their predictions in the absence of the explanatory auxiliary variable but not in its presence. Taken together, these results suggest that children's learning is at once conservative and flexible; children integrate evidence, prior beliefs, and competing causal hypotheses in their exploration, explanation, and learning.

Rational randomness: the role of sampling in an algorithmic account of preschooler's causal learning

Probabilistic models of cognitive development indicate the ideal solutions to computational problems that children face as they try to make sense of their environment. Under this approach, children's beliefs change as the result of a single process: observing new data and drawing the appropriate conclusions from those data via Bayesian inference. However, such models typically leave open the question of what cognitive mechanisms might allow the finite minds of human children to perform the complex computations required by Bayesian inference. In this chapter, we highlight one potential mechanism: sampling from probability distributions. We introduce the idea of approximating Bayesian inference via Monte Carlo methods, outline the key ideas behind such methods, and review the evidence that human children have the cognitive prerequisites for using these methods. As a result, we identify a second factor that should be taken into account in explaining human cognitive development--the nature of the mechanisms that are used in belief revision.

Probabilistic models as theories of children's minds

My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world – coherent, structured hypotheses with consistent relationships to probabilistic patterns of evidence. We also propose that Bayesian inference is one mechanism by which children learn these models from data. These proposals are straightforward psychological hypotheses and far from “Bayesian Fundamentalism.”

A unified account of abstract structure and conceptual change: Probabilistic models and early learning mechanisms

We need not propose, as Carey does, a radical discontinuity between core cognition, which is responsible for abstract structure, and language and “Quinian bootstrapping,” which are responsible for learning and conceptual change. From a probabilistic models view, conceptual structure and learning reflect the same principles, and they are both in place from the beginning.