Biology subjects

Titone, L.

Publications and source records attributed to Titone, L..

2 recordsLinked to original sources

bioRxiv2024

The ARC Toolbox: Artificial Languages with Rhythmicity Control

Infants and adults show the remarkable ability to learn from statistical regularities in the environment. Seminal studies on statistical learning in language acquisition suggested that transitional probabilities between syllables are decisive for word learning. Yet, recent work cautioned that acoustic and phonological regularities confound transitional probabilities, compromising interpretability. Furthermore, prior linguistic background can impact the learning of a new (artificial) language. To control for such confounds, we developed an open-source Python toolbox that generates Artificial Languages with Phonological and Acoustic Rhythmicity Controls (ALPARC). First, we explain all functionalities of ALPARC and provide a step-by-step guide. Then, we demonstrate how ALPARC generates syllable streams encompassing pseudowords that are tailored to critical statistics of real languages. Our results show that ALPARC streams attain stationary transitional probability distributions and reduce acoustic and phonological confounds relative to stimuli used in prior studies. We conclude that ALPARC is a useful tool to overcome current uncertainties in future SL studies.

neuroscience↗

bioRxiv2023

Phase-dependent word perception emerges from region-specific sensitivity to the statistics of language

Neural oscillations reflect fluctuations in excitability, which biases the percept of ambiguous sensory input. Why this bias occurs is still not fully understood. We hypothesized that neural populations representing likely events are more sensitive, and thereby become active on earlier oscillatory phases, when the ensemble itself is less excitable. Perception of ambiguous input presented during less-excitable phases should therefore be biased towards frequent or predictable stimuli that have lower activation thresholds. Here, we show with computational modelling, psychophysics, and magnetoencephalography such a frequency bias in spoken word recognition; a computational model matched the double dissociation found with MEG, where the phase of oscillations in the superior temporal gyrus (STG) and medial temporal gyrus (MTG) biased word-identification behavior based on phoneme and lexical frequencies, respectively. These results demonstrate that oscillations provide a temporal ordering of neural activity based on the sensitivity of separable neural populations.

neuroscience↗