TITLE: Exploring Language Mechanisms: The Mass-Count Distinction and The Potts Neural Network
SPEAKER: Ritwik Kulkarni

ABSTRACT:

One of the epitomes of cognitive function is the ability to communicate through a language. Here, I
attempt to briefly explore language mechanisms in two aspects. First, the statistical properties of
syntax and semantics, and second, the neural mechanisms which could be of possible use in trying
to understand how the brain learns those particular statistical properties. In the first part we focus
our attention on a statistical study of the syntax and semantics of the mass-count distinction in
nouns (countable-uncountable nouns). Where we see that the classification rather than being
bimodal, is a graded distribution and it is similar across languages, but syntactic classes do not map
onto each other, nor do they reflect, beyond weak correlations, semantic attributes of the concepts.
These findings are in line with the hypothesis that much of the mass/count syntax emerges from
language- and even speaker-specific grammaticalisation. Further, we test the ability of a simple
neural network to learn the syntactic and semantic relations of nouns.

The second part, is dedicated to studying the properties of the Potts neural network. The Potts
neural network with its adaptive dynamics represents a simplified model of cortical mechanisms.
Among other cognitive phenomena, it intends to model language production by utilising the
latching behaviour seen in the network. We expect that a model of language processing should
robustly handle various syntactic- semantic correlations amongst the words of a language. With this
aim, we test the effect on storage capacity of the Potts network when the memories stored in it share
non trivial correlations. Lastly (if time permits), we look at another feature of the Potts neural
network, the indication that it may exhibit spin-glass characteristics. The network is consistently
shown to exhibit multiple stable degenerate energy states other than that of pure memories.