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Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning

Bramley, NR; Dayan, P; Griffiths, TL; Lagnado, DA; (2017) Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning. Psychological Review , 124 (3) pp. 301-338. 10.1037/rev0000061. Green open access

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Abstract

Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record

Type: Article
Title: Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1037/rev0000061
Publisher version: http://dx.doi.org/10.1037/rev0000061
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Active learning; causal learning; theory change; resource rationality; intervention
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
URI: http://discovery.ucl.ac.uk/id/eprint/1518681
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