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Dive into the research topics where Rachel W. Magid is active.

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Featured researches published by Rachel W. Magid.


Open Mind | 2018

Four- and 5-Year-Olds Infer Differences in Relative Ability and Appropriately Allocate Roles to Achieve Cooperative, Competitive, and Prosocial Goals

Rachel W. Magid; Mary Depascale; Laura Schulz

Preschoolers are sensitive to differences in individuals’ access to external resources (e.g., tools) in division of labor tasks. However, little is known about whether children consider differences in individuals’ internal resources (e.g., abilities) and whether children can flexibly allocate roles across different goal contexts. Critically, factors that are relevant to role allocation in collaborative contexts may be irrelevant in competitive and prosocial ones. In three preregistered experiments, we found that 4- and 5-year-olds (mean: 54 months; range: 42–66 months; N = 132) used age differences to infer relative ability and appropriately allocate the harder and easier of two tasks in a dyadic cooperative interaction (Experiment 1), and appropriately ignored relative ability in competitive (Experiment 2) and prosocial (Experiment 3) contexts, instead assigning others the harder and easier roles, respectively. Thus, 3-and-a-half- to 5-year-olds evaluate their own abilities relative to others and effectively allocate roles to achieve diverse goals.


Developmental Science | 2018

Changing minds: Children's inferences about third party belief revision

Rachel W. Magid; Phyllis Yan; Max H. Siegel; Joshua B. Tenenbaum; Laura Schulz

Abstract By the age of 5, children explicitly represent that agents can have both true and false beliefs based on epistemic access to information (e.g., Wellman, Cross, & Watson, 2001). Children also begin to understand that agents can view identical evidence and draw different inferences from it (e.g., Carpendale & Chandler, 1996). However, much less is known about when, and under what conditions, children expect other agents to change their minds. Here, inspired by formal ideal observer models of learning, we investigate childrens expectations of the dynamics that underlie third parties’ belief revision. We introduce an agent who has prior beliefs about the location of a population of toys and then observes evidence that, from an ideal observer perspective, either does, or does not justify revising those beliefs. We show that childrens inferences on behalf of third parties are consistent with the ideal observer perspective, but not with a number of alternative possibilities, including that children expect other agents to be influenced only by their prior beliefs, only by the sampling process, or only by the observed data. Rather, children integrate all three factors in determining how and when agents will update their beliefs from evidence.


Cognitive Development | 2015

Imagination and the generation of new ideas

Rachel W. Magid; Mark Sheskin; Laura Schulz


Cognitive Science | 2014

Black boxes: Hypothesis testing via indirect perceptual evidence

Max H. Siegel; Rachel W. Magid; Joshua B. Tenenbaum; Laura Schulz


Cognition | 2017

Moral alchemy: How love changes norms

Rachel W. Magid; Laura Schulz


Cognition | 2017

“I use it when I see it”: The role of development and experience in Deaf and hearing children’s understanding of iconic gesture

Rachel W. Magid; Jennie E. Pyers


Cognitive Science | 2017

Intuitive psychophysics: Children's exploratory play quantitatively tracks the discriminability of alternative hypotheses.

Rachel W. Magid; Max H. Siegel; Josh Tenenbaum; Laura Schulz


Cognitive Science | 2017

Preschoolers appropriately allocate roles based on relative ability in a cooperative interaction.

Rachel W. Magid; Mary Depascale; Laura Schulz


Cognitive Science | 2015

Quit while you're ahead: Preschoolers' persistence and willingness to accept challenges are affected by social comparison.

Rachel W. Magid; Laura Schulz


Cognitive Science | 2014

Preschoolers expect others to learn rationally from evidence

Phyllis Yang; Rachel W. Magid; Laura Schulz

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Laura Schulz

Massachusetts Institute of Technology

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Max H. Siegel

Massachusetts Institute of Technology

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Joshua B. Tenenbaum

Massachusetts Institute of Technology

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Mary Depascale

Massachusetts Institute of Technology

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Josh Tenenbaum

Massachusetts Institute of Technology

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Phyllis Yan

University of Michigan

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