Work

A non-Bayesian Account of the "Causal Reasoning" in Sobel, Tenenbaum & Gopnik (2004)

Year: 2005

Type: article

Abstract: Sobel, Tenenbaum & Gopnik (2004) investigated the development of causal inferences in preschoolers in three experiments with tasks adapted from conditioning literature (backwards blocking and screening-off) and concluded from this indirect evidence that children develop a mechanism for Bayesian structure learning.It is proposed that (a) the differential performances in the two tasks are more likely due to differential memory demands, and (b) the observed developmental differences between 3½ and 4½-year old children may be due to maturation of the memory system, with higher retroactive interference in younger children and lower retroactive interference in older children.This account is supported by simulations with Ans & Rousset's (1997, 2000) memory self-refreshing neural networks architecture.The implications of the account proposed here on a theory of causal relation learning are discussed.

Source: eScholarship (California Digital Library)

Authors: Masanori Nakagawa, Kayo Sakamoto, Asuka Terai

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Centre National de la Recherche Scientifique, National Institutes of Health, National Institute of Mental Health

Awards: Framework for Modeling Human Cognition, G6650704800

OpenAlex ID: W2589855323

FWCI:

Cites: 12

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Related to: 10