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Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning
Deon T Benton1, David Kamper2, Rebecca M Beaton1
1Department of Psychology and Human Development, Vanderbilt University, Nashville, USA.
Insights
Five and 6-year-olds can adjust causal beliefs with low cognitive load, but not with high load. Associative learning better explains this retrospective reevaluation than Bayesian inference.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Neuroscience
Background:
- Causal reasoning is crucial for learning about the world.
- The underlying cognitive mechanisms, particularly in children, are not fully understood.
- It is debated whether children's causal inferences rely on Bayesian inference or associative learning.
Purpose of the Study:
- To investigate if 5- and 6-year-olds can retrospectively reevaluate causal status of objects.
- To determine the impact of information processing demands on this reevaluation.
- To ascertain whether associative learning or Bayesian inference best explains children's retrospective causal judgments.
Main Methods:
- Two experiments were conducted with 5- and 6-year-old children.
- Participants reasoned about 3-4 objects under varying cognitive loads.
- Computational models based on associative learning and Bayesian inference were used to interpret results.
Main Results:
- Children demonstrated retrospective reevaluation under minimal information-processing demands (Experiment 1).
- This ability was absent under greater information-processing demands (Experiment 2).
- Performance was better explained by associative learning than Bayesian inference.
Conclusions:
- Children's capacity for retrospective causal reevaluation is constrained by cognitive load.
- Associative learning provides a more robust explanation for these causal judgments in young children.
- Findings advance understanding of the cognitive mechanisms underlying children's causal reasoning.
Abstract:
Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.
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