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Children's failure to control variables may reflect adaptive decision-making.

ABSTRACT: Changing one variable at a time while controlling others is a key aspect of scientific experimentation and a central component of STEM curricula. However, children reportedly struggle to learn and implement this strategy. Why do children's intuitions about how best to intervene on a causal system conflict with scientific practices? Mathematical analyses have shown that controlling variables is not always the most efficient learning strategy, and that its effectiveness depends on the "causal sparsity" of the problem, i.e., how many variables are likely to impact the outcome. We tested the degree to which 7- to 13-year-old children (n = 104) adapt their learning strategies based on expectations about causal sparsity. We report new evidence demonstrating that some previous work may have undersold children's causal learning skills: Children can perform and interpret controlled experiments, are sensitive to causal sparsity, and use this information to tailor their testing strategies, demonstrating adaptive decision-making.

SUBMITTER: Bramley NR

PROVIDER: S-EPMC9722808 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Children's failure to control variables may reflect adaptive decision-making.

Bramley Neil R NR Jones Angela A Gureckis Todd M TM Ruggeri Azzurra A

Psychonomic bulletin & review 20220713 6

Changing one variable at a time while controlling others is a key aspect of scientific experimentation and a central component of STEM curricula. However, children reportedly struggle to learn and implement this strategy. Why do children's intuitions about how best to intervene on a causal system conflict with scientific practices? Mathematical analyses have shown that controlling variables is not always the most efficient learning strategy, and that its effectiveness depends on the "causal spar ...[more]

PMID: 35831679

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Project description:Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions.

Dataset Information

Children's failure to control variables may reflect adaptive decision-making.

Publications

Children's failure to control variables may reflect adaptive decision-making.

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