Internal physics models guide probabilistic judgments about object dynamics

By Jessica B. Hamrick. 2011

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Citations in the corpus

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).”
Flexible Neural Representation for Physics Prediction (2018, background): “Recent research in computational cognitive science has posited that humans run physics simulations in their mind [5, 3, 20, 48, 21].”
A Compositional Object-Based Approach to Learning Physical Dynamics (2016, methodology): “Top-down and bottom-up approaches A recent set of top-down approaches investigate probabilistic game physics engines as computational models for physical simulation in humans (Bates et al., 2015; Battaglia et al., 2013; Hamrick et al., 2011; Ullman et al., 2014).”
Learning to Poke by Poking: Experiential Learning of Intuitive Physics (2016, background): “…is to represent the world in terms of a ﬁxed set of physical parameters such as mass, friction coefﬁcient, normal forces etc and use a physics simulator for computing object dynamics (Kolev & Todorov, 2015; Mottaghi et al., 2015; Wu et al., 2015; Hamrick et al., 2011) from this representation.”
Learning Visual Predictive Models of Physics for Playing Billiards (2015, background): “The work of (Hamrick et al., 2011) provided evidence that human judgement of how dynamical systems evolve in future can be explained by the hypothesis that humans use internal models of physics.”
"What Happens If..." Learning to Predict the Effect of Forces in Images (2016, background): “In fact, even with a static image [10,1], humans can perform a mental simulation of the future states and reliably predict the dynamics of the interactions.”
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images (2015, background): “Theories in perception and cognition attribute this capability, among many explanations, to previous experience [9] and existence of an underlying physical abstraction [14].”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, background): “common sense learnt from non-visual sources) benefits physical reasoning [23, 49], reasoning about intentions [40] and object functionality [50].”
To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction (2016, positive): “Based on the scene simulation framework used in [13,5], we generate synthetic data in our experiment with rectangular cuboid blocks as basic elements.”
Inferring mass in complex scenes by mental simulation. (2016, unknown): “(1) using their ''intuitive physics engine " (IPE), as proposed by Battaglia et al. (2013) and Hamrick et al. (2011).”
Detecting potential falling objects by inferring human action and natural disturbance (2014, background): “[8] showed that knowledge of Newtonian principles and probabilistic representations are generally applied for human physical reasoning, and the intuitive physics model is an important perspective for human-level complex scene understanding.”
Consistent physics underlying ballistic motion prediction (2013, methodology): “…informs how we use physics to plan our actions or make judgments about the world (e.g., Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2012; Hamrick, et al., 2011), while research into how people describe physical events informs how we form concepts about the workings of the world (e.g.,…”
Noisy Newtons: Unifying process and dependency accounts of causal attribution (2012, background): “Hamrick et al. (2011) have shown that people's stability judgments about towers of blocks is closely in line with a noisy model of Newtonian physics.”
Interpretable Intuitive Physics Model (2018, background): “Humans demonstrate the profound ability to understand the underlying physics of the world [9,10] and use it to predict the future.”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “, a precarious stack of rocks is about to fall in a particular direction) [67, 68].”
Visual Stability Prediction and Its Application to Manipulation (2016, methodology): “g) Inspiration from Human Studies: Research in [24],”