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  1. The development of human causal learning and reasoning.M. K. Goddu & Alison Gopnik - 2024 - Nature Reviews Psychology 3:319-339.
    Causal understanding is a defining characteristic of human cognition. Like many animals, human children learn to control their bodily movements and act effectively in the environment. Like a smaller subset of animals, children intervene: they learn to change the environment in targeted ways. Unlike other animals, children grow into adults with the causal reasoning skills to develop abstract theories, invent sophisticated technologies and imagine alternate pasts, distant futures and fictional worlds. In this Review, we explore the development of human-unique causal (...)
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  • A developmental account of curiosity and creativity.Julie Vaisarova & Kelsey Lucca - 2024 - Behavioral and Brain Sciences 47:e116.
    Ivancovsky et al.'s Novelty-Seeking Model suggests several mechanisms that might underlie developmental change in creativity and curiosity. We discuss how these implications both do and do not align with extant developmental findings, suggest two further elements that can provide a more complete developmental account, and discuss current methodological barriers to formulating an integrated developmental model of curiosity and creativity.
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  • Expanding horizons in reinforcement learning for curious exploration and creative planning.Dale Zhou & Aaron M. Bornstein - 2024 - Behavioral and Brain Sciences 47:e118.
    Curiosity and creativity are expressions of the trade-off between leveraging that with which we are familiar or seeking out novelty. Through the computational lens of reinforcement learning, we describe how formulating the value of information seeking and generation via their complementary effects on planning horizons formally captures a range of solutions to striking this balance.
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  • Flexible information-seeking in chimpanzees.Alexandra G. Rosati, Elisa Felsche, Megan F. Cole, Rebeca Atencia & Joshua Rukundo - 2024 - Cognition 251 (C):105898.
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  • Meta-learned models as tools to test theories of cognitive development.Kate Nussenbaum & Catherine A. Hartley - 2024 - Behavioral and Brain Sciences 47:e157.
    Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.
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