Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

By Alison Gopnik, Henry Wellman. 2012

621 citations recorded by the source corpus.

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Subfields: Artificial Intelligence, Cultural Studies, Developmental and Educational Psychology

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Citations in the corpus

Automatically Composing Representation Transformations as a Means for Generalization (2018, background): “…cognitive development: abstract structural priors serve as a scaffolding within which concrete, domain-speciﬁc learning takes place [63, 78], but domain-speciﬁc learning about the continuous semantics of the world can also provide feedback to update the more discrete structural priors [16, 30].”
A decision network account of reasoning about other people's choices. (2015, background): “Gopnik and Wellman (2012) argue that Bayes nets provide a way to formalize the central ideas of the theory theory, and their reasons apply equally well to decision networks.”
Concepts in a Probabilistic Language of Thought (2014, background): “Intuitive theories (Carey, 2009; Gopnik & Wellman, 2012; Wellman & Gelman, 1992), like their more familiar scientific counterparts, are comprised of a system of interrelated and inter-defined concepts articulating a basic ontology of entities, the properties of and relations between those entities,…”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, methodology): “Our modeling framework takes as a starting point the computational-level view of theory learning as rational statistical inference over hierarchies of structured representations [178, 68].”
When Younger Learners Can Be Better (or at Least More Open-Minded) Than Older Ones (2015, methodology): “You might form the overhypothesis, or “framework theory” (Gopnik & Wellman, 2012), that illnesses have biological causes.”
Constraints on Hypothesis Selection in Causal Learning (2015, background): “The last fifteen years have produced a spate of research highlighting the kinds of epistemic practices that allow children to effectively navigate their complex world (see Gopnik & Wellman, 2012; Schulz, 2012b; Tenenbaum et al., 2011, for reviews.).”
Handbook of Research on Learning and Instruction (2011, methodology): “Under this influence, scholars have applied three models to explain and guide learning in the psychomotor domain: information processing, expertise-novice comparison, and dynamical systems.”
Theory of mind: The state of the art* (2018, background): “But also for some of us, the phrase aptly fit an emerging theory theory: the theoretical claim that children’s conceptual development constitutes naïve theory development (Gopnik & Wellman, 1992, 1994, 2012; Wellman, 1990).”
Intuitive Theories (2020, neutral): “Inspired by these successes, some have proposed that the CBN framework is a candidate for representing people's intuitive theories (Danks, 2014; Glymour, 2001; Gopnik & Wellman, 2012).”
Finding new facts; thinking new thoughts. (2012, background): “A decade of empirical support speaks to the success of this approach (Gopnik & Wellman, 2012; Schulz, 2012).”
The Child as Econometrician: A Rational Model of Preference Understanding in Children (2014, positive): “The model also shows how conceptual change in preference understanding is consistent with Bayesian inference, adding to a growing body of literature demonstrating that Bayesian methods provide elegant explanations for conceptual change [18].”
Imagination and the generation of new ideas (2015, background): “Decades of work in cognitive development have investigated the processes underlying theory change and conceptual change (Carey, 2009; Gopnik & Wellman, 2012; Schulz, 2012).”
To belief or not belief: Children’s theory of mind (2014, background): “The upshot of this section is that pattern detection is something that can occur without effort or even conscious awareness, and that the basic ability to recognize patterns in multiple domains is present in newborn babies (also see Gopnik & Wellman, 2012).”