The placement of the head that minimizes online memory: a complex systems approach

TL;DR

This paper models the placement of syntactic heads to minimize online memory costs, showing the optimal position is at the center, and explores how this influences the evolution and stability of word orders in languages from a complex systems perspective.

Contribution

It introduces a complex systems approach to understanding how online memory minimization shapes word order evolution and stability, highlighting the central placement of heads as optimal.

Findings

Center placement minimizes online memory cost regardless of the cost function.

Word order evolution tends to move from SOV towards more central configurations.

The stability of SVO order is due to the shape of the online memory landscape and avoidance of regression to SOV.

Abstract

It is well known that the length of a syntactic dependency determines its online memory cost. Thus, the problem of the placement of a head and its dependents (complements or modifiers) that minimizes online memory is equivalent to the problem of the minimum linear arrangement of a star tree. However, how that length is translated into cognitive cost is not known. This study shows that the online memory cost is minimized when the head is placed at the center, regardless of the function that transforms length into cost, provided only that this function is strictly monotonically increasing. Online memory defines a quasi-convex adaptive landscape with a single central minimum if the number of elements is odd and two central minima if that number is even. We discuss various aspects of the dynamics of word order of subject (S), verb (V) and object (O) from a complex systems perspective and suggest that word orders tend to evolve by swapping adjacent constituents from an initial or early SOV configuration that is attracted towards a central word order by online memory minimization. We also suggest that the stability of SVO is due to at least two factors, the quasi-convex shape of the adaptive landscape in the online memory dimension and online memory adaptations that avoid regression to SOV. Although OVS is also optimal for placing the verb at the center, its low frequency is explained by its long distance to the seminal SOV in the permutation space.