Deciding What to Do: Developments in Children’s Spontaneous Monitoring of Cognitive Demands

Niebaum, Jesse C.; Munakata, Yuko

doi:10.1111/cdep.12383

Cited by 16 publications

(11 citation statements)

References 47 publications

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“…By these accounts, participants should treat multi-task sets that recruit more shared representations as more costly than sets that recruit more separated representations (Musslick & Cohen, 2021). Developmental studies using this task may also help understand the interplay between learning, information-seeking, and effort (Chevalier, 2018; Munakata et al, 2012; J. Niebaum & Munakata, 2020; J.…”

Section: Discussionmentioning

confidence: 99%

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Bustamante

Oshinowo

Lee

et al. 2022

Preprint

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Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards, have largely been studied separately for cognitive or physical effort, yet most real world actions incur both cognitive and physical effort costs. What is the relationship between cognitive and physical effort costs? Here we attempt to formalize the mechanisms underlying effort-based decisions and address methodological challenges to isolate and measure the factors contributing to such decisions (including sensitivity to reward and effort costs). Patch foraging is an ecologically valid reward rate maximization problem with well developed theoretical tools to understand choices. We developed the Effort Foraging Task to isolate and quantify the cost of both cognitive and physical effort using a computational model. We embedded cognitive or physical effort costs into a patch foraging sequential decision task. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common terms across different domains. We found patterns of correlation of both cognitive and physical effort costs with self-reported cognitive function, anhedonia, depression, anxiety, and fatigue. This suggests that our task captures decision mechanisms closely associated with real-world motivation, and can be used to study individual variation in effort-based decisions across different domains of cost.

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Section: Discussionmentioning

confidence: 99%

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Bustamante

Oshinowo

Lee

et al. 2022

Preprint

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“…One possibility is that children were motivated to seek rewards (i.e., positive feedback from answering correctly) after being compared to a peer (Magid & Schulz, 2015). Another possibility is that children were driven to reduce their cognitive effort, choosing questions that could be answered very quickly and without much thought (see Halberda & Feigenson, 2008; Niebaum & Munakata, 2020). Yet another possibility is that children were driven to repair their reputation following an unfavorable comparison (see Shaw et al, 2014 for an example within the literature on fairness).…”

Section: Discussionmentioning

confidence: 99%

“…At the same time, strategically dividing cognitive labor may be more difficult than dividing physical labor for young children because of potential differences at each of the three theorized steps outlined above. First, children could have more difficulty reasoning about another's chances of success at tasks requiring cognitive skill than physical skill (Niebaum & Munakata, 2020). Cognitive skills like knowledge or mental capacity have few, if any, concrete correlates available prior to observing their success or failure.…”

Section: Strategic Division Of Cognitive Labormentioning

confidence: 99%

Mini managers: Children strategically divide cognitive labor among collaborators, but with a self‐serving bias

Baer

Odic

2021

Child Development

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Strategic collaboration according to the law of comparative advantage involves dividing tasks based on the relative capabilities of group members. Three experiments (N = 405, primarily White and Asian, 45% female, collected 2016-2019 in Canada) examined how this strategy develops in children when dividing cognitive labor. Children divided questions about numbers between two partners. By 7 years, children allocated difficult questions to the skilled partner (Experiment 1, d = 1.42; Experiment 2, d = 0.87). However, younger children demonstrated a self-serving bias, choosing the easiest questions for themselves. Only when engaging in a third-party collaborative task did 5-year-olds assign harder questions to the more skilled individual (Experiment 3, d = 0.55). These findings demonstrate early understanding of strategic collaboration subject to a self-serving bias.

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“…The link between cognition and complexity is invoked often, as the quote at the top of the paper suggests (see also [90][91][92][93][94]). However, it is not always clear if the ideas are applied consistently, as neither the field of cognition nor the field of complexity is straightforward.…”

Section: Cross-tabulation Of Knowledge and Complexitymentioning

confidence: 99%

Exploring Links between Complexity Constructs and Children’s Knowledge Formation: Implications for Science Learning

Droboniku¹,

Kloos²,

Vanderelst³

et al. 2021

Theory of Complexity - Definitions, Models, and Applications

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This essay brings together two lines of work—that of children’s cognition and that of complexity science. These two lines of work have been linked repeatedly in the past, including in the field of science education. Nevertheless, questions remain about how complexity constructs can be used to support children’s learning. This uncertainty is particularly troublesome given the ongoing controversy about how to promote children’s understanding of scientifically valid insights. We therefore seek to specify the knowledge–complexity link systematically. Our approach started with a preliminary step—namely, to consider issues of knowledge formation separately from issues of complexity. To this end, we defined central characteristics of knowledge formation (without considerations of complexity), and we defined central characteristics of complex systems (without considerations of cognition). This preliminary step allowed us to systematically explore the degree of alignment between these two lists of characteristics. The outcome of this analysis revealed a close correspondence between knowledge truisms and complexity constructs, though to various degrees. Equipped with this insight, we derive complexity answers to open questions relevant to science learning.

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Deciding What to Do: Developments in Children’s Spontaneous Monitoring of Cognitive Demands

Cited by 16 publications

References 47 publications

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Mini managers: Children strategically divide cognitive labor among collaborators, but with a self‐serving bias

Exploring Links between Complexity Constructs and Children’s Knowledge Formation: Implications for Science Learning

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