Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling and Similitude01:12

Modeling and Similitude

246
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
246
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

371
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
371
Deductive Reasoning01:16

Deductive Reasoning

55.0K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.1K
The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
1.1K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.7K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bridging Computation and Representation in Associative Learning.

Computational brain & behavior·2026
Same author

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.

Nature communications·2026
Same author

Human-level learning of complex novel tasks as theory-based modelling, exploration and planning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules.

Advances in neural information processing systems·2026
Same author

Probabilistic forecasting guides dynamic decisions.

Psychological review·2026
Same author

Reverse engineering the centered self.

Psychological review·2026

Related Experiment Video

Updated: Jun 8, 2025

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision
07:29

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision

Published on: January 10, 2025

261

Blending simulation and abstraction for physical reasoning.

Felix A Sosa1, Samuel J Gershman2, Tomer D Ullman1

  • 1Department of Psychology, Harvard University, 52 Oxford St, Cambridge MA 02138, USA; Center for Brains, Minds, and Machines, MIT, 43 Vassar St, Cambridge 02139, USA.

Cognition
|November 5, 2024
PubMed
Summary

People use a flexible mental physics engine, blending simulation with efficient abstractions like linear path projection. This allows for quick understanding of physical events but can lead to systematic errors when abstractions are applied.

Keywords:
Intuitive physicsabstract reasoningheuristicssimulation

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

Related Experiment Videos

Last Updated: Jun 8, 2025

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision
07:29

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision

Published on: January 10, 2025

261
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

Area of Science:

  • Cognitive Science
  • Psychology
  • Physics

Background:

  • Understanding everyday physical events relies on cognitive processes.
  • Two main hypotheses exist: probabilistic simulation versus feature-based abstractions.
  • A unified model integrating these reasoning modes is lacking.

Purpose of the Study:

  • To develop and test a blended model of physical reasoning.
  • To investigate how simulation and abstraction are combined.
  • To predict and identify systematic errors in physical judgments.

Main Methods:

  • Developed a blended model combining simulation and visuo-spatial abstraction (linear path projection).
  • Conducted two experiments involving participants judging the trajectory of a falling ball.
  • Measured response times and analyzed judgment accuracy.

Main Results:

  • Response times were longer when straight-line paths were unavailable, contradicting pure simulation.
  • Participants made errors consistent with linear path projection, indicating abstraction use.
  • Evidence supports adaptive invocation of efficient abstractions.

Conclusions:

  • Human physical reasoning is flexible, utilizing a mental physics engine.
  • Cognitive processes adaptively employ efficient abstractions when beneficial.
  • The blended model explains how simulation and abstraction interact in physical judgments.