Noisy Newtons: Unifying process and dependency accounts of causal attribution

By Tobias Gerstenberg, Noah D. Goodman, David A. Lagnado, Josh Tenenbaum. 2012

75 citations recorded by the source corpus.

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Citations in the corpus

Visual Interaction Networks: Learning a Physics Simulator from Video (2017, background): “Overcoming this challenge will help close the gap between human and machine performance on important classes of behavior that depend on physical reasoning, such as model-based decision-making [3], physical inference [13], and counterfactual reasoning [10, 11].”
A Compositional Object-Based Approach to Learning Physical Dynamics (2016, background): “Bouncing balls have also been of interest in cognitive science to study causality and counterfactual reasoning, as in (Gerstenberg et al., 2012).”
Simulation as an engine of physical scene understanding (2013, background): “Probabilistic simulations may help explain how physical knowledge influences perceived scene layouts (26– 28), movement planning (29), causal inferences (11, 12), language semantics, and syntax (e.”
Intuitive Theories (2020, methodology): “The tight coupling between causal and counterfactual judgments in Gerstenberg et al. (2012) provides strong evidence for the role of counterfactual thinking in causal judgments.”
Causal Responsibility and Counterfactuals (2013, result): “The results from Gerstenberg and Lagnado (2010) and Zultan et al. (2012) confirm that the structural approach to responsibility successfully explains attributions to players who, in retrospect, would not have altered the team outcome.”
Mind Games: Game Engines as an Architecture for Intuitive Physics. (2017, background): “…of predictions across di ﬀ erent tasks and types of reasoning, including predicting how a scene will unfold over time [6, 74], interacting with a dynamic scene [75], reasoning about underlying physical properties [10, 11, 66], causal judgments [7], and quantitative infant physical reasoning [9].”
Relational inductive bias for physical construction in humans and machines (2018, background): “…are powerful tools (Silver et al., 2016), and cognitive psychology work has found evidence that humans use internal physics models for physical prediction (Battaglia et al., 2013), inference (Hamrick et al., 2016), causal perception (Gerstenberg et al., 2012), and motor control (Kawato, 1999).”
Reconciling intuitive physics and Newtonian mechanics for colliding objects. (2013, methodology): “inferences about unobserved variables in a way that is very similar to our noisy Newton framework (Gerstenberg et al., 2012; Hamrick et al., 2011; Smith & Vul, 2012).”
Inferring mass in complex scenes by mental simulation. (2016, unknown): “Indeed, recent work by Ullman et al. (2014) and Gerstenberg et al. (2012, 2014) has provided examples of how simulations might be used in simple 2D scenes to estimate the necessary probabilities for Bayesian inference .”
How, whether, why: Causal judgments as counterfactual contrasts (2015, methodology): “In previous work, in which we looked at less complex stimuli that featured a single collision event only, we found that participants’ causal judgments were closely related to their subjective degree of belief that the candidate cause made a difference to whether the outcome occurred (Gerstenberg et…”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, result): “This is consistent with suggestions from several recent studies of intuitive physical reasoning in adults [12, 164, 54, 144] and infants [174].”