Nelly Garcia, Joshua Reiss: An investigation of AI integration in sound designer workflows and experiences https://arxiv.org/abs/2605.27174 https://arxiv.org/pdf/2605.27174 https://arxiv.org/html/2605.27174

Brain signatures of semantic activation for words that do not exist. https://www.biorxiv.org/content/10.64898/2026.04.29.721646v1

Our new fluffy preprint: "A Soft Robotic Interface for Chick-Robot Affective Interactions" arxiv.org/abs/2604.08443 with work led by @juechen.bsky.social @preparedmindslab.bsky.social #ARQ @queenmarycbb.bsky.social

Young magpies learn to combine calls into "sentences" much like human toddlers: through listening to family and friends! Now available to read at @royalsocietypublishing.org @mandyridley.bsky.social @stephanielking.bsky.social @ceb-uwa.bsky.social royalsocietypublishing.org/rspb/article...

Ontogenetic evidence of socially learned call sequences in Western Australian magpies

Abstract. Combinatoriality is the capacity to combine discrete vocal elements into larger structures. Previously thought unique to human language, combinat

royalsocietypublishing.org

Christopher Mitcheltree, Vincent Lostanlen, Emmanouil Benetos, Mathieu Lagrange: SCRAPL: Scattering Transform with Random Paths for Machine Learning https://arxiv.org/abs/2602.11145 https://arxiv.org/pdf/2602.11145 https://arxiv.org/html/2602.11145

Our new pre-print shows how unsupervised clustering methods can identify biologically meaningful differences in early vocal production, with no human feedback. @antorrisi.bsky.social has led this interdisciplinary collaboration based on computational methods + #chicks 🐣 arxiv.org/abs/2601.12203

Our new study on remote touch .....✋ Touching Without Contact: We Physically Sense Objects Before Feeling Them - neurosciencenews.com/remote-touch... @zhengqichen.bsky.social @lauracrucianelli.bsky.social @elisabettaversace.bsky.social #LorenzoJamone ✋ --> 📹 youtu.be/6hpuLojesyQ?...

Touching Without Contact: We Physically Sense Objects Before Feeling Them - Neuroscience News

A new study shows that humans possess a form of “remote touch,” allowing them to detect hidden objects in sand before making direct contact.

neurosciencenews.com

Very excited and proud to share my postdoctoral research with @neurrriot.bsky.social looking at the context-specific encoding of social behavior 💃🕺 in hormone-sensitive, large-scale brain networks in mice! www.biorxiv.org/content/10.1... #neuroskyence #compneurosky 🧪 1/12

"I’m a Genocide Scholar. I Know It When I See It." www.nytimes.com/2025/07/15/o...

Opinion | I’m a Genocide Scholar. I Know It When I See It.

nytimes.com

Our paper on automatic sample identification (arxiv.org/abs/2506.14684) was accepted at @ismir_conf 2025! 🎵🎶 We propose an architecture that can detect music samples that have been reused in new compositions, even after pitch-shifting, time-stretching, and other transformations!🧵

The value of ecologically irrelevant animal cognition research Opinion by Scarlett Howard tinyurl.com/37aedmfx

I love this work from my colleagues from the psychology department!

Prepared Minds Lab@preparedmindslab.bsky.social · last yr.

This week for QMUL's #FestivalofEducation, the fantastic masters student Jane O’Sullivan presented a poster on #PositiveBalance using #PositivePsychology and #DigitalTechnologies to support adolescent wellbeing ⭐ @elisabettaversace.bsky.social @jieyinghuang.bsky.social @rosiedavis.bsky.social

Preprint from us: "Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors" https://arxiv.org/abs/2504.06710 -- Vincent Kather evaluates a big set of deep embeddings for #bioacoustics

Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors

In computational bioacoustics, deep learning models are composed of feature extractors and classifiers. The feature extractors generate vector representations of the input sound segments, called embeddings, which can be input to a classifier. While benchmarking of classification scores provides insights into specific performance statistics, it is limited to species that are included in the models' training data. Furthermore, it makes it impossible to compare models trained on very different taxonomic groups. This paper aims to address this gap by analyzing the embeddings generated by the feature extractors of 15 bioacoustic models spanning a wide range of setups (model architectures, training data, training paradigms). We evaluated and compared different ways in which models structure embedding spaces through clustering and kNN classification, which allows us to focus our comparison on feature extractors independent of their classifiers. We believe that this approach lets us evaluate the adaptability and generalization potential of models going beyond the classes they were trained on.

arxiv.org

Alex and Robyn had an amazing day engaging the children of Wapping High School in the fascinating world of animal behaviour! @asabeducation.bsky.social #nationalscienceweek #animalbehaviour #outreach