The Oxford handbook of causal reasoning

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Generalization in anti-causal learning (2018, background): “It has been conjectured that humans internally build powerful causal models for simulations and approximate physical mechanisms through intuitive theories (Krynski and Tenenbaum, 2007; Waldmann et al., 2006; Hamrick et al., 2011; Battaglia et al., 2013; Gerstenberg and Tenenbaum, 2017).”
Towards Continual Reinforcement Learning: A Review and Perspectives (2020, background): “…recent work has demonstrated that the successor representation model from the RL literature (Dayan, 1993) may provide a more accurate model to reflect the way humans balance their use of both model-based and model-free learning (Momennejad, Russek, Cheong, Botvinick, Daw, and Gershman, 2017).”
Tell me why! - Explanations support learning of relational and causal structure (2021, background): “Causality: Humans focus on causal structure, even as children (Gopnik et al., 1999; Gopnik and Sobel, 2000), and our causal understanding is closely linked to explanations (Lombrozo and Vasilyeva, 2017).”
Tell me why! Explanations support learning relational and causal structure (2021, background): “Causality: Humans focus on causal structure, even as children (Gopnik et al., 1999; Gopnik & Sobel, 2000), and our causal understanding is closely linked to explanations (Lombrozo & Vasilyeva, 2017).”