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Constructing the world: Active causal learning in cognition

Bramley, NR; (2017) Constructing the world: Active causal learning in cognition. Doctoral thesis , UCL (University College London). Green open access

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Humans are adept at constructing causal models of the world that can support prediction, explanation, simulation-based reasoning, planning and control. In this thesis I explore how people learn about the causal world interacting with it, and how they represent and modify their causal knowledge as they gather evidence. Over 10 experiments and modelling, I show that interventional and temporal cues, along with top-down hierarchical constraints, inform the gradual evolution and adaptation of increasingly rich causal representations. Chapters 1 and 2 develop a rational analysis of the problems of learning and representing causal structure, and choosing interventions, that perturb the world in ways that reveal its structure. Chapters 3--5 focus on structure learning over sequences of discrete trials, in which learners can intervene by setting variables within a causal system and observe the consequences. The second half of the thesis generalises beyond the discrete trial learning case, exploring interventional causal learning in situations where events occur in continuous time (Chapters 6 and 7); and in spatiotemporally rich physical "microworlds" (Chapter 8). Throughout the experiments, I find that both children and adults are robust active causal learners, able to deal with noise and complexity even as normative judgment and intervention selection become radically intractable. To explain their success, I develop scalable process level accounts of both causal structure learning and intervention selection inspired by approximation algorithms in machine learning. I show that my models can better explain patterns of behaviour than a range of alternatives as well as shedding light on the source of common biases including confirmatory testing, anchoring effects and probability matching. Finally, I propose a close relationship between active learning and active aspects of cognition including thinking, decision making and executive control.

Type: Thesis (Doctoral)
Title: Constructing the world: Active causal learning in cognition
Event: University College London
Open access status: An open access version is available from UCL Discovery
Language: English
Keywords: causal, learning, causal learning, active learning, Bayesian inference, heuristics, Bayesian network, probability theory, mental models, model-based, thinking, causality, active causal learning, entropy, information theory, information, problem solving
UCL classification: UCL
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 Life Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1540252
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