Human language exhibits lawful structure at the level of words (frequency, vocabulary growth) and word pairs (co-occurrence across distance). Here we show that the arrangement of words in sequence -- a central determinant of meaning -- obeys a comparable law. Using large language models as probabilistic probes, we measured the reduction in target perplexity conferred by prior context at distance d beyond that of the same words scrambled; this difference, the contextual persistence function P(d), isolates the influence of arrangement. Across ten corpora spanning six language families and written and spoken modalities, P(d) decayed approximately as 1/d ($P(d) \propto d^{-\alpha}$, mean $\alpha = 1.04$; median $r^2 = 0.96$). The effect vanished in scrambled and synthetic controls, replicated across independent probes, and did not appear in genomic or protein sequences under domain-native models. An exponent near 1 distributes contextual influence approximately uniformly across logarithmic timescales. The results establish a scaling law of contextual persistence in human language.
Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it contribute as much to explaining when children acquire individual words? Frequency reflects a learner's cumulative exposure to a word, whereas surprisal reflects how pre...
A scale-dependent transition between two ID regimes is found: at low lexical diversity, conditions with fewer unique final words produce higher ID, while at high lexical diversity, this ordering reverses, and conditions with more unique words produce higher ID.
Arwa Osman, Marco Baroni, Iuri Macocco· 0 citations
The central contributions of this paper articulate the conditions under which distributional predictability threatens the internal validity of an experiment and provide concrete recommendations for how to control for this potential confound.
Sean Trott, James A. Michaelov, Cameron R. Jones et al.· Open Mind· 0 citations
It is concluded that explaining how language can emerge from neural population codes, in both biological and artificial systems, will not be achieved through the incremental refinement of algebraic-symbolic theories but will demand new theoretical paradigms.
Samuel A. Nastase, Zaid Zada, A. Goldberg et al.· Neuron· 1 citation
Since the 1990s, the fields of numerical cognition and psycholinguistics have evolved largely independently. However, the research since then demonstrates that language and numbers are intrinsically related, particularly in multilingual contexts. Language characteristics (e.g., morpho-syntactic properties) and language...
Rémy Lachelin, Christine Schiltz, M. Marinova· Brain and Language· 0 citations
Surprisal, the negative log probability of a word given its context, is the dominant computational metric for quantifying reading difficulty and a common item difficulty estimator in reading research. Yet how the language model family, surprisal granularity, and corpus type jointly shape the surprisal–reading time link...
Shu-Ting Liu, Yong Mei· Journal of Intelligence· 0 citations
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