Explore versus store: Children strategically trade off reliance on exploration versus working memory during a complex task

Kenderla, Praveen; Kibbe, Melissa M.

doi:10.1016/j.jecp.2022.105535

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…This finding reveals the vicissitudes of learning and how underperformers might lag behind for no fault of their own. Unlike human infants who start learning about the world without any background and therefore rely on core principles and non-linguistic cues to accrue knowledge 43,44 , human adults may favor learning from explicit linguistic instruction rather than purely from experience. In our study, we treat humans as we would non-human animals in a standard laboratory task [45][46][47][48][49][50] .…”

Section: Discussionmentioning

confidence: 99%

Starting from Zero: Task Learning in Completely Naïve Individuals

Wang,

Williams,

Ogunlade

et al. 2023

Preprint

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Humans often learn about the world by observing, imitating, and receiving explicit instruction from others. Even when learning via trial-and-error or reflection, human adults often do so immersed in a relevant context. But what happens when most of this scaffolding is removed? How effectively can humans bootstrap learning in an information- and context impoverished environment? In eight pre-registered experiments, we sought to understand how effectively humans learn from ‘zero’ in a series of simple stimulus recognition tasks that subjects easily learned when provided with explicit instructions. When no explicit guidance was provided, fewer than half of the subjects learned the task despite receiving trial-by-trial feedback and monetary reward. Surprisingly, providing partial, explicit instructions about the structure of the task—a ubiquitous pedagogical approach—did not improve performance nor reduce individual variability. Instead, explicit instructions that constrained the action space (i.e., valid key presses) partially recovered performance but still pointed to action inhibition as particularly challenging to learn without explicit instructions. Our results suggest that individual differences emerge in impoverished environments without reflecting underlying capacity, and that a major driver of learning, even for the simplest of tasks, is a combination of luck and the nature and quality of scaffolding available to the learner.

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Section: Discussionmentioning

confidence: 99%

Starting from Zero: Task Learning in Completely Naïve Individuals

Wang,

Williams,

Ogunlade

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…There is now considerable evidence demonstrating that children do default to simpler modes of thinking when their information‐processing abilities are taxed (e.g., Doebel & Zelazo, 2015; Frye et al., 1995; Zelazo et al., 1996; Zelazo et al., 2003). For example, recently Kenderla and Kibbe (2023) demonstrated that 8‐ and 10‐year‐old children showed decreased reliance on working memory and greater dependence on manual exploration during a challenging virtual memory game. The goal of this game was to find three cards with shared and differing features.…”

Section: Introductionmentioning

confidence: 99%

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands

Benton,

Kamper,

Beaton

et al. 2023

Developmental Science

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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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Children’s Sensitivity to Difficulty and Reward Probability When Deciding to Take on a Task

Wang

Bonawitz

2022

Journal of Cognition and Development

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Explore versus store: Children strategically trade off reliance on exploration versus working memory during a complex task

Cited by 3 publications

References 43 publications

Starting from Zero: Task Learning in Completely Naïve Individuals

Starting from Zero: Task Learning in Completely Naïve Individuals

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands

Children’s Sensitivity to Difficulty and Reward Probability When Deciding to Take on a Task

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