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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
Published on: September 3, 2021
On the origin of long-range correlations in texts
Eduardo G Altmann1, Giampaolo Cristadoro, Mirko Degli Esposti
1Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany. edugalt@pks.mpg.de
Summary
Long-range correlations in language emerge from structured linguistic levels, creating bursty event sequences around key topics. These findings apply broadly to hierarchical systems, offering insights into complex information flow.
Area of Science:
- Linguistics
- Complex Systems Science
- Statistical Physics
Background:
- Human language reflects complex interactions with the world, requiring high-dimensional information representation.
- Linguistic output exhibits strong temporal correlations, notably long-range correlations in literary texts, the mechanisms of which remain poorly understood.
Purpose of the Study:
- To elucidate the origin and propagation of long-range correlations in natural language.
- To demonstrate how these correlations manifest from structured linguistic levels down to basic textual elements.
- To identify generalizable mechanisms applicable to other hierarchical systems.
Main Methods:
- Combined theoretical calculations with empirical data analysis of linguistic structures.
- Investigated the flow of correlations from higher linguistic levels to lower-level components like words and letters.
- Analyzed the temporal dynamics of correlations, particularly around semantically relevant topics.
Main Results:
- Demonstrated that long-range correlations originate from structured linguistic hierarchies.
- Showed correlations manifest as bursty sequences of events when focusing on semantically rich content.
- Identified underlying mechanisms driving these correlated linguistic phenomena.
Conclusions:
- Long-range correlations in text are a consequence of hierarchical linguistic organization.
- The bursty nature of correlations around topics provides a key insight into information processing in language.
- The identified mechanisms offer a general framework for understanding complex dynamics in hierarchical systems.
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