Tess Feyen, Alda Mari, Paul Portner & Valentine Tinarrage (2024). Political orientation in media treatment of police violence: Evidence from modal adjectives. Discourse & Society, 0(0).doi:10.1177/09579265241303515

Résumé :
The goal of this paper is to unveil possible correlations between the political orientation of newspapers and their treatment of police violence. We consider three different news publications with diverging political orientations, namely Jacobin (Left), Breitbart (Right), and The New York Times (Center). We performed a corpus study that relies on two different categorizations: a new ontology for police violence situations, identifying a set of recurrent themes, and the use and distributions of modal adjectives across these themes as revealing stances toward them. Modal adjectives are highly polysemous, and our analysis distinguishes between the epistemic readings as relating to factual truth, and evaluative readings as relating to norms. Our study shows that left and right leaning journals share similar uses of themes, but differ in their use of modal adjectives. These results could suggest that political orientation is responsible for differences that lie in the stance that the newspapers adopt.

Carlo Geraci, Lena Pasalskaya, and Sharon Peperkamp (2024). Sign recognition: the effect of parameters and features in sign mispronunciations. Linguistics Vanguard.doi:10.1515/lingvan-2024-0070

Résumé :
We investigate the degree to which mispronounced signs can be accommodated by signers of French Sign Language (LSF). Using an offline judgment task, we examine both the individual contributions of three parameters – handshape, movement, and location – to sign recognition, and the impact of the individual features that were manipulated to obtain the mispronounced signs. Results indicate that signers judge mispronounced handshapes to be less damaging for well-formedness than mispronounced locations or movements. In addition to this macro-effect of parameter, individual features are found to carry different weights during sign recognition, mirroring what has been reported for phonological features in spoken languages. Together, these results thus further support an underlying a-modal phonological architecture for human language, including feature-based phonological representations.

Marvin Lavechin, Maureen de Seyssel, Hadrien Titeux, Guillaume Wisniewski, Hervé Bredin, Alejandrina Cristia, Emmanuel Dupoux. (2025). Simulating Early Phonetic and Word Learning Without Linguistic Categories. Dev Sci.. 28(2):e13606.doi:10.1111/desc.13606. PMID:39763096.

Résumé :
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants. Previous modeling work has shown that a distributional learning algorithm could reproduce perceptual changes in infants' early phonetic learning without acquiring phonetic categories. Taking this inquiry further, we propose that linguistic categories may not be needed for early word learning. We introduce STELA, a predictive coding algorithm designed to extract statistical patterns from continuous raw speech data. Our findings demonstrate that STELA can reproduce some developmental patterns of phonetic and word form learning without relying on linguistic categories such as phonemes or words nor requiring explicit word segmentation. Through an analysis of the learned representations, we show evidence that linguistic categories may emerge as an end product of learning rather than being prerequisites during early language acquisition.

Guilhem Marion, Fei Gao, Benjamin P. Gold, Giovanni M. Di Liberto, Shihab Shamma (2025). IDyOMpy: A new Python-based model for the statistical analysis of musical expectations, Journal of Neuroscience Methods, 415, 110347, ISSN 0165-0270.doi:10.1016/j.jneumeth.2024.110347.

Résumé :
Background: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.
New method: IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.
Results: We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (Pearce, 2018).
Comparison with existing methods and Conclusions : Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.

Silvia Polver, Nicole Miller-Viacava, Matthieu Fraticelli, Judith Gervain and Christian Lorenzi (2024). Developmental origins of natural sound perception. Front. Psychol. 15:1474961.doi:10.3389/fpsyg.2024.1474961

Résumé : Infants are exposed to a myriad of sounds early in life, including caregivers' speech, songs, human-made and natural (non-anthropogenic) environmental sounds. While decades of research have established that infants have sophisticated perceptual abilities to process speech, less is known about how they perceive natural environmental sounds. This review synthesizes current findings about the perception of natural environmental sounds in the first years of life, emphasizing their role in auditory development and describing how these studies contribute to the emerging field of human auditory ecology. Some of the existing studies explore infants' responses to animal vocalizations and water sounds. Infants demonstrate an initial broad sensitivity to primate vocalizations, which narrows to human speech through experience. They also show early recognition of water sounds, with preferences for natural over artificial water sounds already at birth, indicating an evolutionary ancient sensitivity. However, this ability undergoes refinement with age and experience. The few studies available suggest that infants' auditory processing of natural sounds is complex and influenced by both genetic predispositions and exposure. Building on these existing results, this review highlights the need for ecologically valid experimental paradigms that better represent the natural auditory environments humans evolved in. Understanding how children process natural soundscapes not only deepens our understanding of auditory development but also offers practical insights for advancing environmental awareness, improving auditory interventions for children with hearing loss, and promoting wellbeing through exposure to natural sounds.

Ruizhe Zhou, Benjamin Spector, Mora Maldonado (2025). An experimental assessment of the nall lexical gap. Proceedings of the Amsterdam Colloquium, 422-427.

Résumé :
Across the world’s languages, the quantificational concept not all (nall) is never lexicalized. This also holds for temporal (not always), modal (not must) and locative (not everywhere) domains. Existing accounts for these lexical gaps fall into two broad categories. According to Cognitive Markedness accounts, nall is not lexicalized because it expresses a cognitively unnatural concept. According to Communicative Efficiency accounts, nall is not intrinsically marked, but is not very useful for efficient communication. Across two artificial language learning experiments, we found no cognitive bias against nall, suggesting that its absence from the lexicon cannot be attributed to cognitive markedness.