Mechanical reasoning by mental simulation.

By Mary Hegarty. 2004

464 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…ones based purely on correlations (Ahn, 1998; Anh and Kalish, 2000; Ahn, Kalish, Medin, and Gelman, 1995; Bechtel and Abrahamsen, 2005; Croskerry, 2009; De Kleer and Brown, 2014; Glennan, 2002; Hegarty, 2004; Hoffman, Klein, and Miller, 2011; Klein, et al., 2014; Szpunar, Sprenng, and Schachter,”
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].”
Learning Physical Intuition of Block Towers by Example (2016, background): “A number of works in cognitive science have explored intuitive physics, for example, in the context of liquid dynamics (Bates et al., 2015), ballistic motion (Smith et al., 2013) and gears and pulleys (Hegarty, 2004).”
Learning to Perform Physics Experiments via Deep Reinforcement Learning (2016, neutral): “There is substantial evidence connecting the behavior of human judgments to inference over an explicit latent physics model (Hegarty, 2004; Hamrick et al., 2011; Battaglia et al., 2013).”
Metacontrol for Adaptive Imagination-Based Optimization (2017, background): “…that humans rely on rich generative models of the world for planning (Gl¨ascher et al., 2010), control (Wolpert & Kawato, 1998), and reasoning (Hegarty, 2004; Johnson-Laird, 2010; Battaglia et al., 2013), that they adapt the amount of computation they perform with their model to the demands…”
Simulation as an engine of physical scene understanding (2013, methodology): “These runnable mental models have been invoked to explain aspects of high-level physical and mechanical reasoning (16, 17) and implemented computationally in classic artificial intelligence systems (18–20).”
Analogues of mental simulation and imagination in deep learning (2019, unknown): “Mental simulation is also seen as general, flexible, and compositional, supporting behavior across a wide range of different tasks [4], capturing a large body of commonsense knowledge [5,12], and operating over multiple levels of abstraction [86].”
Mental models and cognitive change (2013, background): “Much of our reasoning in daily life is a kinematic simulation of the world, and we draw conclusions or make decisions based on these simulations (Hegarty, 2004).”
The Import of Knowledge Export: Connecting Findings and Theories of Transfer of Learning (2012, background): “Hegarty, M., & Sims, V. K. (1994). Individual differences in mental animation during mechanical reasoning. Memory & Cognition, 22, 411–430. Hendrickson, G., & Schroeder, W. H. (1941). Transfer training in learning to hit a submerged target.”
Learning with summaries: Effects of representation mode and type of learning activity on comprehension and transfer (2013, background): “…pictorial summaries, in contrast to verbal summaries, make spatial and structural relations explicit, we expected the differences in representation mode (of verbal and pictorial summaries) to affect a learner's internal spatial representation of the text (Hegarty, 2004; Leopold & Leutner, 2012).”
Mind's Eye: Grounded Language Model Reasoning through Simulation (2022, background): “The cognitive analog for Mind’s Eye might be the mental visualization process, also known as “the mind’s eye” (Battaglia et al., 2013; Hegarty, 2004), which often relates to motor processes (Wexler et al.”
Learning to build physical structures better over time (2020, background): “…from more recent work on intuitive physics is that people reason about how physical systems evolve over time via mental simulation, which may provide a noisy approximation to real physical dynamics (Battaglia, Hamrick, & Tenenbaum, 2013; Sanborn, Mansinghka, & Griffiths, 2013; Hegarty, 2004).”
Intuitive Theories (2020, methodology): “Intentions create a strong dependence relationship between actor and outcome (Heider, 1958; Hommel, this volume; Malle, 2008). If Brian intends to kill Jack and his first shot misses, he is most likely going to shoot another time to achieve his goal. If Brian accidentally shot at Jack and missed, th”
Thinking with external representations (2010, background): “Tesla said that when he was designing a device, he would run a simulation of it in his head for a few weeks to see which parts were most subject to wear (Hegarty 2004, p. 281, citing Shepherd).”
Components of Spatial Intelligence (2010, unknown): “…that mechanical reasoning involves first decomposing the task into one of inferring the motion of individual components and then using mental image transformations to simulate the motion of each component in order of the causal chain of events in the machine's functioning (Hegarty, 1992, 2004).”
Humans predict liquid dynamics using probabilistic simulation (2015, methodology): “…cognitive model is based on a commonly used method for simulating fluids via collections of interacting particles, which we chose because it can handle a wide variety of materials, includes natural methods for adjusting the computational resource demands by varying the number of particles and…”