My work pictured by AI – Sebastian Sauppe
In the style of Hieronmyus Bosch. ©With Midjourney - AI & NCCR Evolving Language.
"Neural signatures of syntactic variation in speech planning." - By Sebastian Sauppe
What is this work about? Planning to speak is a challenge for the brain, and the challenge varies between and within languages. Yet, little is known about how neural processes react to these variable challenges beyond the planning of individual words. Here, we examine how fundamental differences in syntax shape the time course of sentence planning. Most languages treat alike (i.e., align with each other) the 2 uses of a word like “gardener” in “the gardener crouched” and in “the gardener planted trees.” A minority keeps these formally distinct by adding special marking in 1 case, and some languages display both aligned and nonaligned expressions. Exploiting such a contrast in Hindi, we used electroencephalography (EEG) and eye tracking to suggest that this difference is associated with distinct patterns of neural processing and gaze behavior during early planning stages, preceding phonological word form preparation. Planning sentences with aligned expressions induces larger synchronization in the theta frequency band, suggesting higher working memory engagement, and more visual attention to agents than planning nonaligned sentences, suggesting delayed commitment to the relational details of the event. Furthermore, plain, unmarked expressions are associated with larger desynchronization in the alpha band than expressions with special markers, suggesting more engagement in information processing to keep overlapping structures distinct during planning. Our findings contrast with the observation that the form of aligned expressions is simpler, and they suggest that the global preference for alignment is driven not by its neurophysiological effect on sentence planning but by other sources, possibly by aspects of production flexibility and fluency or by sentence comprehension. This challenges current theories on how production and comprehension may affect the evolution and distribution of syntactic variants in the world’s languages.
The first word that came to mind when seeing the AI-generated picture? Seeing into the mind.
Explore more illustrations!
My work pictured by AI – Yaqing Su
In the style of cubism. ©With Midjourney - AI & NCCR Evolving Language.
"A deep hierarchy of predictions enables on-line meaning extraction in a computational model of human speech comprehension." - By Yaqing Su
What is this work about? Real-time speech comprehension poses great challenges for both the brain and language models. We show that hierarchically organized predictions integrating nonlinguistic and linguistic knowledge provide a more comprehensive account of behavioral and neurophysiological response to speech, compared to next-word predictions as generated by GPT2.
The first word that came to mind when seeing the AI-generated picture? Embedded.
Explore more illustrations!
My work pictured by AI – Nikhil Phaniraj
In a futuristic style. ©With Midjourney – AI & Nikhil Phaniraj.
My work pictured by AI – Alejandra Hüsser
In the style of surrealism. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Daniel Friedrichs
In the style of Edward Hopper. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Théophane Piette
In the style of Henri Rousseau. ©With Midjourney - AI & NCCR Evolving Language.
"Animal’s Brain can follow the beat: investigating the link between vocal rhythm and brain oscillations." - By Théophane Piette
What is this work about? The relationship between speech rhythmicity and neural oscillations is an important component of speech perception, and especially of comprehension. However, even though the presence of the same rhythm has been described in non-human primates, and neural oscillations are a basic property of animals’ brains, we still do not know how the brain of animals is processing rhythmic information. Therefore, by identifying similarities and differences in rhythm, as well as its connection with brain oscillations in animal species, we hope to uncover the common rules that govern the rhythmic production and processing of vocal signals in animals. These results will help us understand how speech fits or detached itself from these basic rules, giving us new insight into the evolution of language complex hierarchical structure and a better understanding of brains’ perception mechanisms of vocal signals.
The first word that came to mind when seeing the AI-generated picture? /
https://evolvinglanguage.ch/my-work-pictured-by-ai/
My work pictured by AI – Jessie C. Adriaense
In the style of William Blake. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Kinkini Bhadra
In the style of Pablo Picasso. ©With Midjourney – AI & Kinkini Bhadra.
My work pictured by AI – Fabio J. Fehr
In the style of comics and superheros. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Sarah Saneei
In a steampunk style. ©With Midjourney - AI & Sarah Saneei.
"Computations supporting language functions and dysfunctions in artificial and biological neural networks." - By Sarah Saneei
What is this work about? It’s a research with the aim of finding the best stimuli (input) that can be provided with the brain to have the same brain signal results (best activation of Neurons) using deep learning approaches. We’ll use fMRI and ECOG to prepare the data for the model and as inputs, we plan to use texts and audio.
The first word that came to mind when seeing the AI-generated picture? /
Explore more illustrations!
My work pictured by AI – Volker Dellwo
In the style of Surrealism. ©With Midjourney - AI & NCCR Evolving Language.
"Mothers reveal more of their vocal identity when talking to babies." - By Volker Dellwo
What is this work about? Voice timbre – the unique acoustic information in a voice by which its speaker can be recognized – is particularly critical in mother-infant interaction. Vocal timbre is necessary for infants to recognize their mothers as familiar both before and after birth, providing a basis for social bonding between infant and mother. The exact mechanisms underlying infant voice recognition are unknown. Here, we show – for the first time – that mothers’ vocalizations contain more detail of their vocal timbre through adjustments to their voices known as infant-directed speech (IDS) or baby talk, resulting in utterances in which individual recognition is more robust. Using acoustic modelling (k-means clustering of Mel Frequency Cepstral Coefficients) of IDS in comparison with adult-directed speech (ADS), we found across a variety of languages from different cultures that voice timbre clusters in IDS are significantly larger to comparable clusters in ADS. This effect leads to a more detailed representation of timbre in IDS with subsequent benefits for recognition. Critically, an automatic speaker identification Gaussian-mixture model based on Mel Frequency Cepstral Coefficients showed significantly better performance when trained with IDS as opposed to ADS. We argue that IDS has evolved as part of a set of adaptive evolutionary strategies that serve to promote indexical signalling by caregivers to their offspring which thereby promote social bonding via voice and acquiring language.
Comment about the picture from the author? The study is about ‘voice recognition’ and the advantage that infant-directed speech offers in learning a voice. I am not sure someone would conclude this from looking at the pictures.
Explore more illustrations!
My work pictured by AI – Nikhil Phaniraj
In a futuristic style. ©With Midjourney – AI & Nikhil Phaniraj.
My work pictured by AI – Monica Lancheros
In the style of cubism. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Sebastian Sauppe
My work pictured by AI – Chantal Oderbolz
In the style of William Eggleston. ©With Midjourney - AI & NCCR Evolving Language.
"Tracking the prosodic hierarchy in the brain." - By Chantal Oderbolz
What is this work about? The speech signal carries hierarchically organized acoustic and linguistic information. Recent research suggests that the brain uses brain waves, called cortical oscillations, to process this information. Especially oscillations in the theta frequency range (4-8 Hz) have been found to be important: Theta oscillations process acoustic energy in the speech signal associated with the timing of syllables. However, there is also slower information in the speech signal that corresponds to stress and intonation patterns and are part of the prosody – the rhythm and melody – of a language.
To better understand how the brain processes these different levels at the same time, we conducted an experiment with 30 participants who listened to German sentences with manipulated stress and intonation patterns. We found that the brain is able to simultaneously process the syllable, stress and intonation patterns of speech. However, changes in stress patterns disrupted the brain’s ability to track syllables with theta oscillations. Conversely, the brain was able to compensate for changes in intonation patterns by using linguistic knowledge. Additionally, we found that individuals varied in their ability to process the prosodic structure of the speech signal, with some participants better able to compensate for acoustic changes than others. Overall, our results support the idea that the brain uses a hierarchical organization of cortical oscillations to process the speech signal.
The first word that came to mind when seeing the AI-generated picture? Nostalgia.
Explore more illustrations!
My work pictured by AI – Elisa Pellegrino
In the style of Joan Miro. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Théophane Piette
In the style of Henri Rousseau. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Nikhil Phaniraj
In a futuristic style. ©With Midjourney – AI & Nikhil Phaniraj.
My work pictured by AI – EduGame Team
In the style of fantasy and Sci-fi. ©With Midjourney - AI & NCCR Evolving Language.
"EduGame: leveraging game-based technologies to facilitate reading acquisition." - By EduGame Team
What is this work about? Reading skills are crucial for a child’s academic and everyday success, and we want to make learning to read fun and engaging. Our game-based technology intervention trains attentional control and executive processes for efficient reading acquisition. But that’s not all – we’re also taking a comparative approach to understand the processes that mediate reading acquisition across languages. Our app-based assessment battery, CRAB, will help us evaluate the effectiveness of our intervention. The best part? The intervention is short, tablet-based, and available in Italian, French, and German – the main languages of the Swiss educational system. Join us in revolutionizing reading acquisition and understanding the complexities of language learning! Join us in our quest to improve reading skills for children around the world!
The first word that came to mind when seeing the AI-generated picture?/
Explore more illustrations!
My work pictured by AI – Nikhil Phaniraj
In a futuristic style. ©With Midjourney – AI & Nikhil Phaniraj.
My work pictured by AI – Diana Mazzarella
In the style of René Magritte. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Théophane Piette
In the style of Henri Rousseau. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Volker Dellwo
In the style of Surrealism. ©With Midjourney - AI & NCCR Evolving Language.
"Mothers reveal more of their vocal identity when talking to babies." - By Volker Dellwo
What is this work about? Voice timbre – the unique acoustic information in a voice by which its speaker can be recognized – is particularly critical in mother-infant interaction. Vocal timbre is necessary for infants to recognize their mothers as familiar both before and after birth, providing a basis for social bonding between infant and mother. The exact mechanisms underlying infant voice recognition are unknown. Here, we show – for the first time – that mothers’ vocalizations contain more detail of their vocal timbre through adjustments to their voices known as infant-directed speech (IDS) or baby talk, resulting in utterances in which individual recognition is more robust. Using acoustic modelling (k-means clustering of Mel Frequency Cepstral Coefficients) of IDS in comparison with adult-directed speech (ADS), we found across a variety of languages from different cultures that voice timbre clusters in IDS are significantly larger to comparable clusters in ADS. This effect leads to a more detailed representation of timbre in IDS with subsequent benefits for recognition. Critically, an automatic speaker identification Gaussian-mixture model based on Mel Frequency Cepstral Coefficients showed significantly better performance when trained with IDS as opposed to ADS. We argue that IDS has evolved as part of a set of adaptive evolutionary strategies that serve to promote indexical signalling by caregivers to their offspring which thereby promote social bonding via voice and acquiring language.
Comment about the picture from the author? The study is about ‘voice recognition’ and the advantage that infant-directed speech offers in learning a voice. I am not sure someone would conclude this from looking at the pictures.
Explore more illustrations!
My work pictured by AI – Jamil Zaghir
In a futuristic style. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Fabio J. Fehr
In the style of comics and superheros. ©With Midjourney – AI & NCCR Evolving Language.
My work pictured by AI – Adrian Bangerter
In the style of Aubrey Beardsley. ©With Midjourney – AI & NCCR Evolving Language.
