Young children's metacognitive awareness of confounded evidence

Designing museum exhibits to support the development of scientific thinking in informal learning environments: A university-museum-community partnership

Our objective is to scaffold the natural behaviors that support scientific thinking and STEM learning in children through museum exhibit design and development. Here, we describe a collaborative research-to-practice initiative called "Designing Museum Exhibits to Support the Development Scientific Thinking in Informal Learning Environments: A University-Museum-Community Partnership," in which we document natural behavior in the context of children's informal learning environments and detail our plans to translate our findings into exhibit development. This initiative is part of a long-standing university (UT Austin, Center for Applied Cognitive Science), museum (Thinkery-Austin Children's Museum), and community (Austin's Early Learner Community) partnership called Thinkery Connect. Our first aim here is to review best practices in STEM exhibit design that fosters scientific thinking. We will then describe the design of a study on exhibit signage to promote scientific thinking development. We will also discuss our plans to develop and evaluate exhibit signage in context. Our long-term objective is to deepen engagement in activities that build scientific thinking for visitors at children's museums like Thinkery, at home, and in the community.

Calculated Comparisons: Manufacturing Societal Causal Judgments by Implying Different Counterfactual Outcomes

How do people come to opposite causal judgments about societal problems, such as whether a public health policy reduced COVID-19 cases? The current research tests an understudied cognitive mechanism in which people may agree about what actually happened (e.g., that a public health policy was implemented and COVID-19 cases declined), but can be made to disagree about the counterfactual, or what would have happened otherwise (e.g., whether COVID-19 cases would have declined naturally without intervention) via comparison cases. Across two preregistered studies (total N = 480), participants reasoned about the implementation of a public policy that was followed by an immediate decline in novel virus cases. Study 1 shows that people's judgments about the causal impact of the policy could be pushed in opposite directions by emphasizing comparison cases that imply different counterfactual outcomes. Study 2 finds that people recognize they can use such information to influence others. Specifically, in service of persuading others to support or reject a public health policy, people systematically showed comparison cases implying the counterfactual outcome that aligned with their position. These findings were robust across samples of U.S. college students and politically and socioeconomically diverse U.S. adults. Together, these studies suggest that implied counterfactuals are a powerful tool that individuals can use to manufacture others' causal judgments and warrant further investigation as a mechanism contributing to belief polarization.

Young children's conceptualization of empirical disagreement

Chinese and American children aged 5-11 years (total N = 144) heard two child informants make conflicting empirical claims about each of 4 scenarios. For example, one informant claimed that a ball would float when dropped in water whereas the other informant claimed that it would sink. Children were asked to judge whether each informant could be right, and to justify their overall judgment. In both samples, there was a change with age. Older children often said that each informant could be right whereas younger children, especially in China, were more likely to say that only one informant could be right. Nevertheless, in the wake of decisive empirical evidence (e.g., the ball was shown to sink when dropped in water), almost all children, irrespective of age, drew appropriate conclusions about which of the two informants had been right. Thus, with increasing age, children differ in their prospective - but not in their retrospective - appraisal of empirical disagreement. Absent decisive evidence, older children are more likely than younger children to suspend judgment by acknowledging that either of two conflicting claims could be right. We argue that children's tendency to suspend judgment is linked to their developing awareness of empirical uncertainty, as expressed both in the justifications they give when judging the disagreement and in their own beliefs about the scenarios. Implications for children's understanding of disagreement are discussed.

The complex associations between scientific reasoning and advanced theory of mind

This 6-wave longitudinal study (2014-2018) of 161 German 5- to 10-year-olds from a midsized city and rural area in southern Germany (89 females, 72 males; predominantly White; mostly middle class) found that scientific-reasoning abilities first develop at 6 years. Abilities were highly stable, with the kindergarten score predicting 25% of end-of-elementary-school variance. Individual but not developmental differences were related to language abilities (0.39), mindreading skills (0.33), and parental education (0.36). In early elementary school, mindreading skills predicted scientific reasoning (0.15), but not vice versa; in late elementary school, bidirectional associations emerged (0.11-0.33). Our findings suggest that mindreading is a precursor for the development of scientific reasoning and that older children use scientific reasoning to revise their advanced theories of mind.

Contributions of causal reasoning to early scientific literacy

Although early causal reasoning has been studied extensively, inconsistency in the tasks used to assess it has clouded our understanding of its structure, development, and relevance to broader developmental outcomes. The current research attempted to bring clarity to these questions by exploring patterns of performance across several commonly used measures of causal reasoning, and their relation to scientific literacy, in a sample of 3- to 5-year-old children from diverse backgrounds (N = 153). A longitudinal confirmatory factor analysis revealed that some measures of causal reasoning (counterfactual reasoning, causal learning, and causal inference), but not all of them (tracking cause-effect associations and resolving confounded evidence), assess a unidimensional factor and that this resulting factor was relatively stable across time. A cross-lagged panel model analysis revealed associations between causal reasoning and scientific literacy across each age tested. Causal reasoning and scientific literacy related to each other concurrently, and each predicted the other in subsequent years. These relations could not be accounted for by children's broader cognitive skills. Implications for early STEM (science, technology, engineering, and math) engagement and success are discussed.

Developmental Trajectories in Diagnostic Reasoning: Understanding Data Are Confounded Develops Independently of Choosing Informative Interventions to Resolve Confounded Data

Two facets of diagnostic reasoning related to scientific thinking are recognizing the difference between confounded and unconfounded evidence and selecting appropriate interventions that could provide learners the evidence necessary to make an appropriate causal conclusion (i.e., the control-of-variables strategy). The present study investigates both these abilities in 3- to 6-year-old children (N = 57). We found both competence and developmental progress in the capacity to recognize that evidence is confounded. Similarly, children performed above chance in some tasks testing for the selection of a controlled test of a hypothesis. However, these capacities were unrelated, suggesting that preschoolers' nascent understanding of the control-of-variables strategy may not be driven by a metacognitive understanding that confounded evidence does not support a unique causal conclusion, and requires further investigation.

A Model of Scientific Data Reasoning

Data reasoning is an essential component of scientific reasoning, as a component of evidence evaluation. In this paper, we outline a model of scientific data reasoning that describes how data sensemaking underlies data reasoning. Data sensemaking, a relatively automatic process rooted in perceptual mechanisms that summarize large quantities of information in the environment, begins early in development, and is refined with experience, knowledge, and improved strategy use. Summarizing data highlights set properties such as central tendency and variability, and these properties are used to draw inferences from data. However, both data sensemaking and data reasoning are subject to cognitive biases or heuristics that can lead to flawed conclusions. The tools of scientific reasoning, including external representations, scientific hypothesis testing, and drawing probabilistic conclusions, can help reduce the likelihood of such flaws and help improve data reasoning. Although data sensemaking and data reasoning are not supplanted by scientific data reasoning, scientific reasoning skills can be leveraged to improve learning about science and reasoning with data.

Rethinking the "gap": Self-directed learning in cognitive development and scientific reasoning

To improve upon their current knowledge, learners must be able to generate informative data and accurately evaluate this evidence. However, there is substantial disagreement regarding self-directed learners' competence in these behaviors. Researchers in cognitive development have suggested that learners are "intuitive scientists," generating informative actions and rationally coordinating their current observations and prior beliefs from an early age. Conversely, researchers in scientific reasoning report that learners struggle with experimentation and often fail to reach appropriate conclusions from evidence, even as adults. According to the prevailing narrative, these inconsistent findings must be "bridged" to explain the gap between learners' successes and failures. Here, we advocate for an alternative approach. First, we review the research on scientific reasoning and find that there may be less evidence for learners' failures than is typically assumed. Second, we offer a novel interpretation that aims to account for both literatures: we suggest that self-directed learners may be best understood as competent causal reasoners. That is, many seemingly uninformative or irrational behaviors are consistent with the goals of causal learning. This account not only resolves the apparent contradictions in the existing research, but also offers a way forward towards a more accurate and integrated understanding of self-directed learning. This article is categorized under: Psychology > Development and Aging Psychology > Learning Psychology > Reasoning and Decision Making.