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Related Experiment Video

Updated: Jun 20, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Action understanding as inverse planning.

Chris L Baker1, Rebecca Saxe1, Joshua B Tenenbaum1

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.

Cognition
|September 5, 2009
PubMed
Summary
This summary is machine-generated.

Humans infer agent actions by assuming rational planning, using Bayesian inverse planning to model goal inference. This framework quantitatively supports approximate rationality in human action understanding.

Related Experiment Videos

Last Updated: Jun 20, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Humans excel at inferring mental states (goals, beliefs) driving others' actions.
  • Existing theories use qualitative approaches like the 'intentional stance' or 'teleological stance'.

Purpose of the Study:

  • To propose and validate a computational framework for human action understanding.
  • To model mental state inference using Bayesian inverse planning.
  • To quantitatively assess human goal inference mechanisms.

Main Methods:

  • Developed a Bayesian inverse planning framework assuming agent rationality.
  • Inverted a rational planning model using Bayesian inference.
  • Conducted psychophysical experiments with animated stimuli in mazes.
  • Compared inverse planning models with varying goal priors against human inferences.

Main Results:

  • Provided quantitative evidence for approximate rationality in human goal inference.
  • Demonstrated the flexible nature of goal representations in human observers.
  • Validated the Bayesian inverse planning framework within a simplified paradigm.

Conclusions:

  • The framework offers a probabilistic formalization of action understanding.
  • Results support an approximately rational inference mechanism in humans.
  • The model can be extended for inferring beliefs and intentionality.