Inferring mass in complex scenes by mental simulation

Hamrick, Jessica B.; Battaglia, Peter W.; Griffiths, Thomas L.; Tenenbaum, Joshua B.

doi:10.1016/j.cognition.2016.08.012

Cited by 81 publications

(119 citation statements)

References 55 publications

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“…There is abundant evidence indicating that people can deal with complex dynamic environments even with sparse sensory information (Battaglia et al 2013;Hamrick et al 2016;Lacquaniti and Maioli 1989;Sanborn et al 2013;. This indicates that successful interactions with objects in our daily routine imply an underlying model of the physical properties and forces involved, a model that can surrogate missing or ambiguous sensory information (Wolpert & Kawato 1998).…”

Section: Introductionmentioning

confidence: 99%

Body orientation contributes to modelling the effects of gravity for target interception in humans

Scaleia

Lacquaniti

Zago

2019

The Journal of Physiology

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Key points It is known that interception of targets accelerated by gravity involves internal models coupled with visual signals. Non‐visual signals related to head and body orientation relative to gravity may also contribute, although their role is poorly understood. In a novel experiment, we asked pitched observers to hit a virtual target approaching with an acceleration that was either coherent or incoherent with their pitch‐tilt. Initially, the timing errors were large and independent of the coherence between target acceleration and observer's pitch. With practice, however, the timing errors became substantially smaller in the coherent conditions. The results show that information about head and body orientation can contribute to modelling the effects of gravity on a moving target. Orientation cues from vestibular and somatosensory signals might be integrated with visual signals in the vestibular cortex, where the internal model of gravity is assumed to be encoded. Abstract Interception of moving targets relies on visual signals and internal models. Less is known about the additional contribution of non‐visual cues about head and body orientation relative to gravity. We took advantage of Galileo's law of motion along an incline to demonstrate the effects of vestibular and somatosensory cues about head and body orientation on interception timing. Participants were asked to hit a ball rolling in a gutter towards the eyes, resulting in image expansion. The scene was presented in a head‐mounted display, without any visual information about gravity direction. In separate blocks of trials participants were pitched backwards by 20° or 60°, whereas ball acceleration was randomized across trials so as to be compatible with rolling down a slope of 20° or 60°. Initially, the timing errors were large, independently of the coherence between ball acceleration and pitch angle, consistent with responses based exclusively on visual information because visual stimuli were identical at both tilts. At the end of the experiment, however, the timing errors were systematically smaller in the coherent conditions than the incoherent ones. Moreover, the responses were significantly (P = 0.007) earlier when participants were pitched by 60° than when they were pitched by 20°. Therefore, practice with the task led to incorporation of information about head and body orientation relative to gravity for response timing. Instead, posture did not affect response timing in a control experiment in which participants hit a static target in synchrony with the last of a predictable series of stationary audiovisual stimuli.

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Section: Introductionmentioning

confidence: 99%

Body orientation contributes to modelling the effects of gravity for target interception in humans

Scaleia

Lacquaniti

Zago

2019

The Journal of Physiology

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“…Our studies thus provide further evidence for probabilistic inference in physics-engine-like simulations as a general framework for modeling how people perceive and predict the physics of complex scenes (e.g. Battaglia et al, 2013;Smith & Vul, 2013;Hamrick et al, 2016), and show how these accounts can be extended to model how people learn the parameters of physical theories from observing dynamic interactions.…”

Section: Discussionmentioning

confidence: 57%

Learning Physical Parameters From Dynamic Scenes

Ullman¹

2018

Preprint

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Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

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“…Future work addressing both of these questions will shed light on the precise nature of the intuitive physical reasoning system. In particular, individuals with WS should be tested on a wider array of physical reasoning tasks (and more closely matched non-physical tasks) (e.g., Heider and Simmel, 1944; Michotte, 1963; Leslie and Keeble, 1987; Oakes, 1994; Baillargeon, 1998; Kotovsky and Baillargeon, 2000; Luo et al, 2009; Smith and Vul, 2013; Fischer et al, 2013; Hamrick et al, 2016), to identify the specificity of intuitive physics deficit, and how it might related to other deficits involving reasoning about spatial configurations.…”

Section: Discussionmentioning

confidence: 99%

Dissociating intuitive physics from intuitive psychology: Evidence from Williams syndrome

et al. 2017

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Prior work suggests that our understanding of how things work (“intuitive physics”) and how people work (“intuitive psychology”) are distinct domains of human cognition. Here we directly test the dissociability of these two domains by investigating knowledge of intuitive physics and intuitive psychology in adults with Williams syndrome (WS) – a genetic developmental disorder characterized by severely impaired spatial cognition, but relatively spared social cognition. WS adults and mental-age matched (MA) controls completed an intuitive physics task and an intuitive psychology task. If intuitive physics is a distinct domain (from intuitive psychology), then we should observe differential impairment on the physics task for individuals with WS compared to MA controls. Indeed, adults with WS performed significantly worse on the intuitive physics than the intuitive psychology task, relative to controls. These results support the hypothesis that knowledge of the physical world can be disrupted independently from knowledge of the social world.

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Inferring mass in complex scenes by mental simulation

Cited by 81 publications

References 55 publications

Body orientation contributes to modelling the effects of gravity for target interception in humans

Body orientation contributes to modelling the effects of gravity for target interception in humans

Learning Physical Parameters From Dynamic Scenes

Dissociating intuitive physics from intuitive psychology: Evidence from Williams syndrome

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