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Haemy Lee Masson

@hleemasson.bsky.social

Social cognitive neuroscientist studying social touch perception in autism and mental health, neuroimaging methods, and naturalistic social vision. Asst Prof. at Durham University, UK

78 Posts1222 Follower214 Folgt
Haemy Lee Masson@hleemasson.bsky.social · 7mo ago

Led by master's student Macy, with collaboration from @kerenmaclennan.bsky.social and @mmellon.bsky.social. @durhampsych.bsky.social. 1. Autistic adults engage in social touch as frequently as non-autistic adults, with similar satisfaction. journals.sagepub.com/doi/10.1177/...

Autistic adults prefer social touch less but engage just as much: The impact of social context, relationship type, sensory responsivity, and psychological factors - Macy Tsz Wing Li, Manuel Mello, Ker...

Autistic people face touch-related challenges, yet the factors influencing these difficulties are not well understood. We developed a novel instrument to captur...

journals.sagepub.com

Andrea Costantino@costantinoai.bsky.social · 9mo ago

Super excited to share a new preprint! We asked a simple-but-big question: What changes in the brain when someone becomes an expert? Using chess ♟️ + fMRI 🧠 + representational geometry & dimensionality 📈, we ask: 1️⃣ WHAT information is encoded? 2️⃣ HOW is it structured? 3️⃣ WHERE is it expressed? 1/n

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Xavier Job@xavierjob.bsky.social · last yr.

📣 New preprint 📣 The brain attenuates self-touch, but how does this unfold at the neural level before the touch? We used MEG to find out 🧠 👉👈

Motor prediction reduces beta-band power and enhances cerebellar-somatosensory connectivity before self-touch to enable its attenuation

Prevailing theories suggest that the brain uses an internal forward model to predict tactile input during voluntary movements, thereby reducing the intensity of the reafferent tactile sensation, a phe...

biorxiv.org

Konstantina Kilteni@kkilteni.bsky.social · last yr.

New article out at Science Advances! Three challenges, five questions, and multiple implications of tickle. www.science.org/doi/10.1126/... @dondersinst.bsky.social @ki.se @erc.europa.eu #neuroskyence #psychscisky #Sensorimotor #ERCResearch #ticklelab

The extraordinary enigma of ordinary tickle behavior: Why gargalesis still puzzles neuroscience

Tickle sensation remains one of the biggest unresolved mysteries in neuroscience—one that scientists are only beginning to unravel.

science.org

Leyla Isik@lisik.bsky.social · last yr.

I’m happy to be at #VSS2025 and share what our lab has been up to this year! I’m also honored to receive this year’s young investigator award and will give a short talk at the awards ceremony Monday

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Rebecca Saxe @rebeccasaxe.bsky.social · last yr.

Please help: Do you know a baby, less than 12 months old, who can walk? We are trying to complete a project, 7 years in the works. And all we need are 3 infants who are less than a year old and can walk, to do a short online looking-time study. 🆘

Mark Thornton@markthornton.bsky.social · last yr.

New paper out at @naturehumbehav.bsky.social! This is the most comprehensive investigation ever conducted into how people think about relationships: a true tour de force! We identify 5 dimensions and 3 categories that organize relationship concepts. Proud to have made a (small) contribution to it!

a, Three behavioural tasks and their corresponding categories (via UMAP and k-means clustering). The dimensional survey indicated three high-order categories (hostile, private and public, abbreviated as HPP), whereas both cognitive tasks identified six canonical relationship types. b, Text analysis on categorical labels in the free sorting task revealed the label hierarchy: the hierarchical clustering algorithm first derived three HPP categories; next, the ‘public’ cluster was further split into ‘transactional’ and ‘power’, and the ‘private’ cluster was subdivided into ‘family’, ‘romantic’ and ‘affiliative’. The word clouds in the treemap display label names in each cluster. The radar plots illustrate the average scores on the FAVEE dimensions across relationships in each category. c, A dimension–category hybrid model was evaluated by applying k-means clustering to the 5D FAVEE embedding space. Three HPP clusters were found, suggesting that HPP could emerge from the FAVEE space. d, The three building blocks of the relationship concept system and their internal associations. The flow chart illustrates how people represent relationships via the joint framework of dimensions and categories. Five FAVEE dimensions can be understood as filters (for example, all relationships can be filtered into hostile and solidarity relationships via the valence dimension), and relationship categories are formed by uneven distributions projected on dimensions (see the details in Supplementary Fig. 14). Our data suggest that six canonical relationship types (in the grey circles) originate from three HPP categories (in the dashed circles), which inherently emerge from the 5D FAVEE space (arrows).a, Three representational geometries were derived from the full-feature model (all evaluative features), the dimensional model (FAVEE) and the categorical model (HPP). Using leave-one-region-out cross-validation, high similarities of representational geometries (all r > 0.687) were observed across 19 global regions (black areas on the world map), suggesting that FAVEE dimensions and HPP categories are commonly shared across the world. The box plots show the median (horizontal line) and the interquartile range (IQR) ± 1.5 × IQR. Here the full-feature model represents the noise ceiling of cross-region representational similarity. b, Computational modelling of representational geometries using RSA multiple regression. A set of ecological, biological and sociocultural RDMs were fit to predict the cross-regional relationship RDM, and religion and modernization level were the only two factors that could significantly predict the cross-regional variability of representational geometries. The asterisks indicate significant regression coefficients according to the Mantel test, and the dashed lines indicate noise ceilings (Methods). c, Significant correlations between cross-region pairwise dissimilarity of representational geometry and cross-region pairwise dissimilarity in religion and modernization level. The shaded area represents the 95% confidence interval. *P < 0.05; **P < 0.01; ***P < 0.001; one-sided permutation tests.a, Pipeline for generating PLM embeddings. The query (dashed box) was formulated as ‘[DESC] The most salient feature of the relationship [TERM] is [MASK]’, where the [DESC] component was generated by GPT-4 and the [TERM] component was replaced with one of the 258 relationships. The [MASK] component was filled by the PLM with words that are most likely to occur there, given the context. The last layer vector was extracted on the position of [MASK] for each of 258 relationships, resulting in a 258 × 768 matrix. The resemblance of human representations and PLM representations (pretrained on a modern Chinese corpus) was 0.553. b, PCA on modern PLM embeddings. By correlating human ratings on 33 dimensional features with the first seven principal components of PLM embeddings, we found that PLM representations had captured corresponding features of FAVEE structures. c, Correspondence between PCA on human ratings and PCA on modern PLM embeddings (in purple) and ancient PLM embeddings (in green). d, Generalizability of the FAVEE-HPP model in ancient and modern times. RSA correlation on RDMs suggested that the FAVEE dimensions and HPP categories can significantly predict relationship representations in historical and modern times. The box plots indicate RSA correlation distributions at chance level by permutation, and the dashed lines indicate noise ceilings. e, Model comparison analysis suggested that the FAVEE model outperformed 15 other existing theories in predicting ancient and modern PLM embeddings. Broadly, FAVEE was significantly better than any random combination of theoretical features, Ancient–modern differences. Using linear combinations of the five FAVEE components as regressors to predict ancient and modern PLM embeddings, we found substantial changes on ‘formality’ and ‘equality’ for their explained variance in ancient and modern PLM representations, suggesting that these two dimensions may contribute differently to relationship conceptualization across time.PCA loadings on 30 theoretical features derived from multidisciplinary literature. Dark colours on the colour bar represent larger values, with blue indicating negative values and red indicating positive values. b, 159 relationships were plotted in the 5D space on the basis of their scores on each dimension. PCA loadings and relationship scores jointly suggested that RC1 corresponded to formality (formal versus informal), RC2 to activeness (close versus distant), RC3 to valence (harmony versus conflict), RC4 to exchange (concrete versus symbolic) and RC5 to equality (unequal versus equal). Each axis is labelled with the variance explained for the corresponding dimension.
Jamie Reilly 🦜@reilly-coglab.com · last yr.

It’s out! Our article in Nature Human Behavior led by Yin Wang on the conceptual structure of human relationships across cultures and time. This work is breathtaking nam10.safelinks.protection.outlook.com?url=https%3A...

Angela Woods@literarti.bsky.social · last yr.

Naomi Booth’s stunning new novel - raw content - is among many other things an extraordinary insight into maternal OCD. It was such a privilege to talk with her tonight about risk, writing, Yorkshire, rapture & the shock of birth at a Collected Books event www.hachette.co.uk/titles/naomi...

raw content

'raw content is a work of dark, aching beauty' Helen Jukes, author of Mother Animal 'A stunning mix of horror and tenderness, love and despair. It's rare ...

hachette.co.uk

Haemy Lee Masson@hleemasson.bsky.social · last yr.

Published in Molecular Autism. This work examined how autistic adults process social touch at the brain network level. The connectivity strength is linked to a greater preference for social touch and social responsiveness—suggesting neural compensatory mechanisms. link.springer.com/article/10.1...

Dynamic functional adaptations during touch observation in autism: connectivity strength is linked to attitudes towards social touch and social responsiveness - Molecular Autism

Background Autistic adults experience differences in social interactions involving physical contact. Brain imaging studies suggest that these differences may be related to atypical brain responses to ...

link.springer.com

Haemy Lee Masson@hleemasson.bsky.social · 2y ago

Happy to share my latest work on dynamic functional connectivity changes during social vs. non-social touch in autism. This study reveals atypical context-dependent modulation of connectivity associated with autism during touch observation. www.biorxiv.org/content/10.1...

Leonhard Schilbach@leoschilbach.bsky.social · 2y ago

The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes bmcpsychiatry.biomedcentral.com/articles/10....

The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes - BMC Psychiatry

Background A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathway...

bmcpsychiatry.biomedcentral.com

Simon Eickhoff@sbe.bsky.social · 2y ago

The limiting factor for MRI based prediction of behavioral traits may be neither the imaging nor the pipeline or learning algorith but rather the reliability of the target phenotypes Thought provoking work my Martin Gell et al: www.nature.com/articles/s41...

How measurement noise limits the accuracy of brain-behaviour predictions - Nature Communications

Our ability to identify associations between behaviour and brain imaging is important for uncovering markers of cognition and disease. Here, the authors illustrate the importance of the reliability of...

nature.com