BLISS: an Artificial Language for Learnability Studies

Abstract

To explore neurocognitive mechanisms underlying the human language faculty, cognitive scientists use artificial languages to control more precisely the language learning environment and to study selected aspects of natural languages. Artificial languages applied in cognitive studies are usually designed ad hoc, to only probe a specific hypothesis, and they include a miniature grammar and a very small vocabulary. The aim of the present study is the construction of an artificial language incorporating both syntax and semantics, BLISS. Of intermediate complexity, BLISS mimics natural languages by having a vocabulary, syntax, and some semantics, as defined by a degree of nonsyntactic statistical dependence between words. We quantify, using information theoretical measures, dependencies between words in BLISS sentences as well as differences between the distinct models we introduce for semantics. While modeling English syntax in its basic version, BLISS can be easily varied in its internal parametric structure, thus allowing studies of the relative learnability of different parameter sets.

A talkative Potts attractor neural network welcomes BLISS words

“…The network has the ability to spontaneously hop (or latch) across memory patterns (which have been stored as dynamical attractors), thus producing an infinite sequence of patterns, at least in some regimes [ 1 ]. We would like to train the network with a corpus of sentences in BLISS [ 2 ]. BLISS is a scaled-down synthetic language of intermediate complexity, with about 150 words and about 40 rewrite rules.…”