Children adapt their questions to achieve efficient search

Ruggeri, Azzurra; Lombrozo, Tania

doi:10.1016/j.cognition.2015.07.004

Cited by 114 publications

(145 citation statements)

References 47 publications

(63 reference statements)

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“…This desire also reflects children's drive to understand the world around them (Brewer, Chinn, & Samarapungavan, ; Gopnik, ). For example, children seek out information from others and the efficiency and efficacy of their questions increases with age (Legare, Mills, Souza, Plummer, & Yasskin, ; Mills, Legare, Grant, & Landrum, ; Ruggeri & Lombrozo, ; Ruggeri, Lombrozo, Griffiths, & Xu, ). The motivation to ask for explanations from others might also reflect children's drive for social interaction and desire to share knowledge.…”

Section: Explanatory Conversation and Causal Thinkingmentioning

confidence: 99%

Exploration, Explanation, and Parent–Child Interaction in Museums

Callanan

Legare²,

Sobel³

et al. 2020

Monographs Society Res Child

106

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Young children develop causal knowledge through everyday family conversations and activities. Children's museums are an informative setting for studying the social context of causal learning because family members engage together in everyday scientific thinking as they play in museums. In this multisite collaborative project, we investigate children's developing causal thinking in the context of family interaction at museum exhibits. We focus on explaining and exploring as two fundamental collaborative processes in parent–child interaction, investigating how families explain and explore in open‐ended collaboration at gear exhibits in three children's museums in Providence, RI, San Jose, CA, and Austin, TX. Our main research questions examined (a) how open‐ended family exploration and explanation relate to one another to form a dynamic for children's learning; (b) how that dynamic differs for families using different interaction styles, and relates to contextual factors such as families' science background, and (c) how that dynamic predicts children's independent causal thinking when given more structured tasks. We summarize findings on exploring, explaining, and parent–child interaction (PCI) styles. We then present findings on how these measures related to one another, and finally how that dynamic predicts children's causal thinking. In studying children's exploring we described two types of behaviors of importance for causal thinking: (a) Systematic Exploration: Connecting gears to form a gear machine followed by spinning the gear machine. (b) Resolute Behavior: Problem‐solving behaviors, in which children attempted to connect or spin a particular set of gears, hit an obstacle, and then persisted to succeed (as opposed to moving on to another behavior). Older children engaged in both behaviors more than younger children, and the proportion of these behaviors were correlated with one another. Parents and children talked to each other while interacting with the exhibits. We coded causal language, as well as other types of utterances. Parents' causal language predicted children's causal language, independent of age. The proportion of parents' causal language also predicted the proportion of children's systematic exploration. Resolute behavior on the part of children did not correlate with parents' causal language, but did correlate with children's own talk about actions and the exhibit. We next considered who set goals for the play in a more holistic measure of parent–child interaction style, identifying dyads as parent‐directed, child‐directed, or jointly‐directed in their interaction with one another. Children in different parent–child interaction styles engaged in different amounts of systematic exploration and had parents who engaged in different amounts of causal language. Resolute behavior and the language related to children engaging in such troubleshooting, seemed more consistent across the three parent–child interaction styles. Using general linear mixed modeling, we considered relations within s...

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Section: Explanatory Conversation and Causal Thinkingmentioning

confidence: 99%

Exploration, Explanation, and Parent–Child Interaction in Museums

Callanan

Legare²,

Sobel³

et al. 2020

Monographs Society Res Child

106

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“…While it is true that controlling variables is the most efficient strategy under a variety of different assumptions, it is clearly inferior in sparse systems, especially when there are many variables. Our analysis thus demonstrates the importance of examining the ecological validity of strategies (Goldstein & Gigerenzer, 2002;Ruggeri & Lombrozo, 2015;Parpart et al, in press), that is, the fit between a strategy and the environment it is used in.…”

Section: Ecological Rationality Of Experimentation Strategiesmentioning

confidence: 80%

Section: Test Half or Test Multiple Variablesmentioning

confidence: 99%

“…This is CAUSAL EXPERIMENTATION 7 similar to a Test One strategy that turns on one switch at a time. In experiments using versions of the Twenty Questions game, both adults and, to a lesser degree, children have been shown to be able to use the Test Half method successfully (Nelson, Divjak, Gudmundsdottir, Martignon, & Meder, 2014;Ruggeri & Feufel, 2015;Ruggeri, Lombrozo, Griffiths, & Xu, 2016;Ruggeri & Lombrozo, 2015).Importantly, using a Test Half (or Test Multiple) strategy is sometimes considered a reasoning error (particularly in work on Control of Variables in the science education literature).For example, a child that chooses a test that simultaneously manipulates a plant's light exposure, and fertilizer, would be recorded as low-performing in a classic CV experiment (Klahr et al, 1993), because changing or setting many variables at once is thought to confound individual variables.The Test One and Test Half/Multiple strategies are typically studied in different kinds of psychological tasks. However, as demonstrated by the switch example, they can both be reasonable approaches for testing the causal impact of multiple variables.…”

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confidence: 99%

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Testing one or multiple: How beliefs about sparsity affect causal experimentation

Coenen¹,

Ruggeri²,

Bramley³

et al. 2018

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What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the "Control of Variables" or CV strategy). We demonstrate that CV is not always the most efficient method for learning. Using an optimal actor model which aims to minimize the average number of tests, we show that when a causal system is sparse (i.e., when the outcome of interest has few or even just one actual cause among the candidate variables) it is more efficient to test multiple variables at once. Across a series of behavioral experiments, we then show that people are sensitive to causal sparsity and adapt their strategies accordingly. When interacting with a non-sparse causal system (high proportion of actual causes among candidate variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse causal system they are more likely to test multiple variables at once. However, we also find that people sometimes use a CV strategy even when a system is sparse. IntroductionTo develop a causal understanding of the world, we often need to find out how multiple candidate variables affect an outcome of interest. This problem arises in everyday situations (e.g., "Which switch(es) control the bathroom fan?"), during scientific exploration ("Which of these treatments can affect disease x?"), and plays an important part in answering economic and social questions ("What is the impact of these policies on Gross Domestic Product?"). Often, the quickest and most effective method of resolving causal relationships is to conduct experiments that manipulate variables of a system (e.g., turning switches on or off) and to observe the resulting outcome. This kind of causal experimentation is often (but not always) required to decouple causation and correlation (Pearl, 2009;Woodward, 2005;Sloman, 2005).Both children and adults can systematically leverage the outcomes of interventions to test causal hypotheses that would be indistinguishable based on observation alone (Lagnado & Sloman, 2004; Lagnado, Waldmann, Hagmayer, & Sloman, 2006;Rottman & Keil, 2012;Schulz, Gopnik, & Glymour, 2007;Sloman & Lagnado, 2005;Waldmann & Hagmayer, 2005). Furthermore, people are sometimes able to come up with highly efficient experiments that optimize information gained per intervention or minimize the total number of tests needed on average to discover the true causal structure (Bramley, Lagnado, & Speekenbrink, 2015;Bramley, Dayan, Griffiths, & Lagnado, 2017; Coenen, Rehder, & Gureckis, 2015;Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003).Here we consider a specific learning situation in which people are asked to explore a causal system consisting of a number of independent variables (switches) and a dependent outcome (turning on a fan). These types of problems have played a central role in research on science education and cognitive development and are common in ...

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“…In many real-life situations, decisions have to be made under limited time, and it is known that adults adapt to time limitation using faster and simpler (Ben Zur & Brenitz, 1981;Payne, Bettmann, & Johnson, 1988), but not necessarily more effective (Belling, Suss, & Ward, 2015) strategies. Recent work has shown that children are ecological learnersthey modify their learning strategies to the characteristics of the task at hand (Horn, Ruggeri, & Pachur, 2016;Nelson, Divjak, Gudmundsdottir, Martignon, & Meder, 2014;Ruggeri & Lombrozo, 2015), and they do so already by age 4 (Ruggeri, Sim, & Xu, 2017). The effects of time limitation on the efficiency of children's reasoning are mixed.…”

Section: Time-limitation Effectsmentioning

confidence: 99%

Icon arrays help younger children's proportional reasoning

Ruggeri

Vagharchakian

2018

British J of Dev Psycho

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We investigated the effects of two context variables, presentation format (icon arrays or numerical frequencies) and time limitation (limited or unlimited time), on the proportional reasoning abilities of children aged 7 and 10 years, as well as adults. Participants had to select, between two sets of tokens, the one that offered the highest likelihood of drawing a gold token, that is, the set of elements with the greater proportion of gold tokens. Results show that participants performed better in the unlimited time condition. Moreover, besides a general developmental improvement in accuracy, our results show that younger children performed better when proportions were presented as icon arrays, whereas older children and adults were similarly accurate in the two presentation format conditions. Statement of contribution What is already known on this subject? There is a developmental improvement in proportional reasoning accuracy. Icon arrays facilitate reasoning in adults with low numeracy. What does this study add? Participants were more accurate when they were given more time to make the proportional judgement. Younger children's proportional reasoning was more accurate when they were presented with icon arrays. Proportional reasoning abilities correlate with working memory, approximate number system, and subitizing skills.

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Children adapt their questions to achieve efficient search

Cited by 114 publications

References 47 publications

Exploration, Explanation, and Parent–Child Interaction in Museums

Exploration, Explanation, and Parent–Child Interaction in Museums

Testing one or multiple: How beliefs about sparsity affect causal experimentation

Icon arrays help younger children's proportional reasoning

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