To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources.
This article presents a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations. This model predicts systematically different learned concepts when the same evidence is processed in different orders, without any extra assumptions about previous beliefs or background knowledge. Across four behavioural experiments (total n = 570), we demonstrate strong curriculum-order and conceptual garden-pathing effects that closely resemble our model predictions and differ from those of alternative accounts. Taken together, this work offers a computational account of how past experiences shape future conceptual discoveries and showcases the importance of curriculum design in human inductive concept inferences.
The authors conclude this paper:
We propose a formalization of bootstrap learning that supercharges Bayesian-symbolic concept-learning frameworks with an effective cache-and-reuse mechanism. This model replaces a fixed set of conceptual primitives with a dynamic concept library enabled by adaptor grammars, facilitating incremental discovery of complex concepts under helpful curricula despite finite computational resources.
We show how compositional concepts evolve as cognitively bounded learners bootstrap from earlier conclusions over batches of data, and how this process gives rise to systematically different interpretations of the same evidence depending on the order in which it is processed. Being a Bayesian-symbolic model, our approach accounts for both the causal concepts people synthesized and the generalization predictions they made.
People often exhibit a general path dependence in their progression of ideas. We show that this follows naturally when a bootstrap learner progresses in a space of compositional concepts, constructing complex ideas ‘piece by piece’ with limited cognitive resources. Crucially, we focus on how reuse of earlier concepts bootstraps the discovery of more complex compositional concepts using sampling-based inference.
This builds on other sampling-based approximations to rational models that demonstrate how memory and computational constraints create focal hypotheses in the early stages of learning, and impair a learner’s ability to accommodate data they later encounter. Going beyond this earlier work, we show how people exceed their immediate inferential limitations via reuse and composition of earlier discoveries through an evolving library of concepts. Our proposal also relates to the observation that amortized inference can explain how solving a subquery improves performance in solving complex nested queries.
While our model instantiates reuse in a compositional space by caching conceptual building blocks in a latent concept library, there is potential to explore the connection between our formalization with amortized inference in terms of how reuse of partial computation might shape the approximation of the full posterior.
… … rather than passive information receivers, it seems far more plausible that people have inductive biases of attention and action that shape how they select which subset of a complex situation to process first, and then build on that to make sense of the whole picture.
Future work may extend our framework to active learning scenarios to study such information-seeking behaviours and self-directed curriculum design patterns in the domain of concept learning. Moreover, cache and reuse is a useful way to refactor representations.
Recent research in neuroscience is starting to unravel how the brain may perform non-parametric Bayesian computations and latent causal inference, and has uncovered representational similarities between artificial neural networks and brain activity. Along these lines, neural evidence for the reuse of computational pathways across tasks would seem to support our thesis and further enrich our understanding of how the brain grows its conceptual systems and world models. One challenge for the symbolic framing adopted here comes from the fact that our conceptual representations are intimately tied in with their embodied sensorimotor features and consequences. We look forward to more integrated models that capture how symbolic operations of composition and caching interface with such deeply embodied representations.
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In sum, we argue for the central role of bootstrap learning in human inductive inference and propose a process-level computational account of conceptual bootstrapping. Our work puts forward cache and reuse as a key cognitive inference algorithm and elucidates the importance of active information parsing for bounded reasoners grappling with a complex environment. Our findings stress the importance of curriculum design in teaching, and to facilitate communication of scientific theories. We hope this work will inspire not only social and cognitive sciences, but also the development of more data-efficient and human-like artificial learning algorithms.
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