Uncertainty and Memory: How Context Influences Word Segmentation

In this article, we explore the concept of minimum description length (MDL) in the context of sequence learning. MDL is an approach that focuses on creating the most compact and efficient representations possible to describe a sequence of events. The authors explain that MDL is closely related to Bayesian inference, which is a statistical technique used to make predictions based on prior knowledge or beliefs.
In the realm of sequence learning, MDL can be applied in various ways. For instance, Planton et al. (2021) proposed using compressed, hierarchical representations of sequences to support learning. Maisto et al. (2015) incorporated a prior over hierarchical policies related to the description length of plans under those hierarchies. Lai et al. (2022) expanded a theory of policy compression to account for ignoring perceptual information while executing an action chunk, which helps reduce the amount of information needed to be processed. Solway et al. (2014) noted that their theory identifies hierarchies that minimize the description length of optimal behavior, even though it may seem counterintuitive.
The authors emphasize that MDL is a crucial strategy for making inference tractable, which means making complex calculations easier to understand and perform. By reusing program fragments or grammar structures, MDL can accelerate inference. Our induction model builds upon this idea.
In summary, MDL is an information-theoretic approach that focuses on creating the most efficient representations possible to describe a sequence of events. This technique has been applied in various ways in sequence learning and has been shown to be an effective way to make complex calculations easier to understand and perform.