Jump to ContentJump to Main Navigation

You are looking at 1-10 of 26 items

  • Keywords: causal learning x
Clear All Modify Search

View:

Integrating Top-down and Bottom-up Approaches to Children’s Causal Inference

David M. Sobel

in Neoconstructivism: The New Science of Cognitive Development

Published in print:
2009
Published Online:
February 2010
ISBN:
9780195331059
eISBN:
9780199864072
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195331059.003.0008
Subject:
Psychology, Cognitive Psychology, Developmental Psychology

This chapter suggests a description of causal inference based on Bayesian inference, which illustrates how children engage in causal learning. This description is meant at the computational level of ... More


Beyond Covariation: Cues to Causal Structure

David A. Lagnado, Michael R. Waldmann, York Hagmaye, and Steven A. Sloman

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0011
Subject:
Psychology, Developmental Psychology

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected ... More


DISCOVERING CAUSAL RELATIONSHIPSH

Jon Williamson

in Bayesian Nets and Causality: Philosophical and Computational Foundations

Published in print:
2004
Published Online:
September 2007
ISBN:
9780198530794
eISBN:
9780191712982
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198530794.003.0008
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy

There are two main proposals for discovering causal relationships: a hypothetico-deductive and an inductive approach. Neither account is fully satisfactory. Various specific approaches are discussed, ... More


A Philosopher Looks at Tool Use and Causal Understanding

James Woodward

in Tool Use and Causal Cognition

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199571154
eISBN:
9780191731259
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199571154.003.0002
Subject:
Psychology, Developmental Psychology, Evolutionary Psychology

This chapter presents a kind of typology of different sorts of abilities that might be associated with the notion of causal understanding, the acquisition of causal beliefs, causally informed action ... More


The Evolutionary Origins of Causal Cognition: Learning and Using Causal Structures

Brian J. Edwards, Benjamin M. Rottman, and Laurie R. Santos

in Tool Use and Causal Cognition

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199571154
eISBN:
9780191731259
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199571154.003.0006
Subject:
Psychology, Developmental Psychology, Evolutionary Psychology

This chapter explores the origins of our species' drive to explain the causal world. It begins by reviewing the types of information that humans use to learn causal structures. Specifically, it ... More


Two Proposals for Causal Grammars

Thomas L. Griffiths and Joshua B. Tenenbaum

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0021
Subject:
Psychology, Developmental Psychology

A causal theory can be thought of as a grammar that generates events, and that can be used to parse events to identify underlying causal structure. This chapter considers what the components of such ... More


Intuitive Theories as Grammars for Causal Inference

Joshua B. Tenenbaum, Thomas L. Griffiths, and Sourabh Niyogi

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0020
Subject:
Psychology, Developmental Psychology

This chapter presents a framework for understanding the structure, function, and acquisition of causal theories from a rational computational perspective. Using a “reverse engineering” approach, it ... More


Introduction

Alison Gopni and Laura Schulz

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0001
Subject:
Psychology, Developmental Psychology

This chapter provides a simple, clear, and (hopefully) amusing introduction to causal model and Bayes nets theories in computer science, the interventionist account of causation in philosophy, and ... More


The Relationship between Children’s Causal and Counterfactual Judgements

Teresa McCormack, Caren Frosch, and Patrick Burns

in Understanding Counterfactuals, Understanding Causation: Issues in Philosophy and Psychology

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199590698
eISBN:
9780191731242
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199590698.003.0003
Subject:
Philosophy, Philosophy of Mind, Metaphysics/Epistemology

In this chapter, we distinguish between two ways in which counterfactual and causal judgements might be linked. According to a psychological relatedness view, counterfactual and causal judgements are ... More


Causal Learning: Psychology, Philosophy, and Computation

Alison Gopnik and Laura Schulz (eds)

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.001.0001
Subject:
Psychology, Developmental Psychology

This book outlines the recent revolutionary work in cognitive science formulating a “probabilistic model” theory of learning and development. It provides an accessible and clear introduction to the ... More


View: