• ☰
  • Home
  • Introduction
  • Generative models
  • Conditioning
  • Causal and statistical dependence
  • Conditional dependence
  • Social cognition
  • Interlude - Bayesian data analysis
  • Interlude - Algorithms for inference
  • Rational process models
  • Learning as conditional inference
  • Learning with a language of thought
  • Hierarchical models
  • Occam's Razor
  • Mixture models
  • Learning (deep) continuous functions
  • Appendix - JavaScript basics
  • Appendix - Useful distributions

1. Models of causal reasoning

In the chapter, we discussed how to reason about causation using generative models. There is a separate question as to how humans actually do reason about causality, and what they mean when they say A caused B. Read out “How, whether, and why” by Tobi Gerstenberg, Noah Goodman, David Lagnado, & Joshua Tenenbaum. (You may find it useful to play around with this causality sandbox.

Reading questions:

a) According to Gerstenberg and colleagues, how do human notions of causality match the notion of causal dependence discussed in the chapter? How do they differ?

b) Gerstenberg and colleagues rely on a notion of counter-factual simulation. In what ways is this similar to embodiment or other classic simulation-based theories?

Extras

• Embodiment. For an overview of psychological theories based on embodied simulation, read this.

• Learning with causal models. For more discussion of how the kinds of models discussed here can inform psychological theory, read “Mechanisms of theory formation in young children” by Laura Schulz and Alison Gopnik.