Beyond covariation: Cues to causal structure.

This chapter argues for several interconnected theses. First, the fundamental way that people represent causal knowledge is qualitative in terms of causal structure. Second, people use a variety of cues to infer structure aside from statistical data (e.g., temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm or refute the model and (possibly) to parameterize it. The structure of a posited model influences how the statistical data are processed. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning. What is needed is a heuristic-based model that shares the strengths and weaknesses of a human learner and can take advantage of the rich causal information that the natural environment provides. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Accession Number: 2007-01301-010. Partial author list: First Author & Affiliation: Lagnado, David A.; Department of Psychology, University College London, London, United Kingdom. Release Date: 20080121. Correction Date: 20151207. Publication Type: Book (0200), Edited Book (0280). Format Covered: Print. Document Type: Chapter. ISBN: 0-19-517680-4, Hardcover; 978-0-19-517680-3, Hardcover. Language: English. Major Descriptor: Causality; Cues; Heuristics; Inference; Statistical Data. Minor Descriptor: Environment; Information; Knowledge Level; Learning; Reasoning; Temporal Order (Judgment). Classification: Cognitive & Perceptual Development (2820). Population: Human (10). Intended Audience: Psychology: Professional & Research (PS). References Available: Y. Page Count: 19.