Representational principles of function generalization
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Date
30/11/2020Author
León-Villagrá, Pablo
Metadata
Abstract
Generalization is at the core of human intelligence. When the relationship between continuous-valued data is generalized, generalization amounts to function
learning. Function learning is important for understanding human cognition, as
many everyday tasks and problems involve learning how quantities relate and
subsequently using this knowledge to predict novel relationships. While function learning has been studied in psychology since the early 1960s, this thesis
argues that questions regarding representational characteristics have not been
adequately addressed in previous research.
Previous accounts of function learning have often proposed one-size-fits-all
models that excel at capturing how participants learn and extrapolate. In these
models, learning amounts to learning the details of the presented patterns. Instead, this thesis presents computational and empirical results arguing that participants often learn abstract features of the data, such as the type of function or
the variability of features of the function, instead of the details of the function.
While previous work has emphasized domain-general inductive biases and
learning rates, I propose that these biases are more flexible and adaptive than
previously suggested. Given contextual information that sequential tasks share
the same structure, participants can transfer knowledge from previous training
to inform their generalizations.
Furthermore, this thesis argues that function representations can be composed
to form more complex hypotheses, and humans are perceptive to, and sometimes
generalize according to these compositional features. Previous accounts of function learning had to postulate a fixed set of candidate functions that form a partic ipants’ hypothesis space, which ultimately struggled to account for the variety of
extrapolations people can produce. In contrast, this thesis’s results suggest that
a small set of broadly applicable functions, in combination with compositional
principles, can produce flexible and productive generalization.