Carly A. Lasagna
@carlylasagna.bsky.social
Clinical Science PhD at UMich | Intern at Harvard Medical School/McLean Hospital | NSF GRFP | Social Perception & Decision-Making in Psychosis via Computational Modeling & 🧠
I’m recruiting two new PhD students to start in 2027, working with me on my program of research in quantitative psychopathology. I’ve put some information for interested students on my website: miriamkforbes.academic.ws Please share with anyone you know who might be interested!
I'm excited to announce that I will be reviewing graduate student applications for Fall 2027 admissions! I review applications in both the Personality, Development, and Health and the Clinical Science (Weinberg) programs.
Research | Personality Across Development Lab
Current Research Topics Leadership Emergence and Development Tackett, J. L., Reardon, K. W., Fast, N. J., Johnson, L., Kang, S. K., Lang, J....
sites.northwestern.edu
I'm thrilled to announce that we are recruiting a new faculty colleague in our Northwestern University Clinical Science program this fall! Please share widely with your colleagues, students, and mentees! psychology.northwestern.edu/people/facul...
Job Opportunities: Department of Psychology - Northwestern University
psychology.northwestern.edu
Sensing your body (‘Interoception’) is everywhere in theories of mental health. But when we tested how well hundreds of people sensed their heart and lungs, the link to symptoms was almost nowhere to be found. 💣 🚨New @natmentalhealth.nature.com paper 🚨🎉 asks why 🧵 www.nature.com/articles/s44...
Yale Psychology is hiring an Assistant Professor in Clinical Psychology! Review of applications will begin on September 2, 2026. Link: apply.interfolio.com/188806 Please share widely & apply!
Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio
apply.interfolio.com
Our paper "Neural timescales from a computational perspective" is finally out in @natneuro.nature.com: www.nature.com/articles/s41... We discuss how computational models and methods can distill empirical observations on neural timescales into quantitative, testable theories of brain computation.
Neural timescales from a computational perspective - Nature Neuroscience
This review integrates computational approaches to provide a unified view on how data analysis methods, biophysical mechanistic models and machine learning approaches can help to uncover the origins a...
nature.com
Predicting continuous outcomes: Some new tests of associative approaches to contingency learning journals.plos.org/ploscompbiol...
Predicting continuous outcomes: Some new tests of associative approaches to contingency learning
Author summary When we learn about cause and effect in everyday life—such as whether a medicine helps recovery from illness—we experience outcomes that vary in degree rather than simply happening or n...
journals.plos.org
1/ We have a PhD position open on 'Mental Disorders as Harmful Stable States'. If you know students interested in the intersection of mental health & statistical modeling (EMA & time series), please encourage them to apply. www.academictransfer.com/nl/jobs/3621...
PhD Position on "Mental disorders as harmful stable states"
PhD Position on “Mental disorders as harmful stable states” The departments Clinical Psychology and Methodology & Statistics, at the Institute of Psychology, Faculty of Social Sciences, Leiden Univers...
academictransfer.com
Self-association enhances early attentional selection through automatic prioritization of socially salient signals
Self-association enhances early attentional selection through automatic prioritization of socially salient signals
Self-related information automatically modulates early attentional selection into awareness through mechanisms distinct from physical salience, revealing an obligatory, individualized self-prioritization...
buff.ly
Acetylcholine: a candidate substrate for hippocampal predictive learning? A Perspective by William de Cothi, Sarah Shipley & Caswell Barry @shipleysj.bsky.social @caswell.bsky.social #neuroscience #neuroskyence www.nature.com/articles/s41...
Acetylcholine: a candidate substrate for hippocampal predictive learning? - Nature Reviews Neuroscience
Predictive models have emerged as a normative lens for understanding neural function. In this Perspective, de Cothi, Shipley and Barry plausibly unify the diverse roles of hippocampal acetylcholine in...
nature.com
Some new work on AI & science: A reporting checklist for LLMs in behavioral science Thanks to @sfeuerriegel.bsky.social for spearheading. As LLMs become more common in research, transparency is essential. We developed a reporting checklist for reporting how LLMs were used - please share! 1/4
A reporting checklist for large language models in behavioural science - Nature Human Behaviour
Large language models offer new opportunities for behavioural science, but their rapid evolution poses challenges for research rigour. We introduce a consensus-based reporting checklist to improve tra...
nature.com
Representativeness and response validity across nine opt-in online samples
Representativeness and response validity across nine opt-in online samples
Nature Human Behaviour, Published online: 22 June 2026; doi:10.1038/s41562-026-02438-zStagnaro et al. compare nine opt-in participant samples on measures of response validity, representativeness and professionalism. They find substantial variation across samples and offer guidance for choosing samples based on the research question.
dlvr.it
Potential mechanisms and functional significance of aperiodic neural activity
Potential mechanisms and functional significance of aperiodic neural activity
Nature Human Behaviour, Published online: 22 June 2026; doi:10.1038/s41562-026-02503-7In this Review, Preston, Smith and Voytek examine aperiodic neural activity in the brain and how measuring aperiodic activity can shed light on brain function and disease.
dlvr.it
Check out @jonathannicholas.bsky.social 's new preprint measuring eye movements to infer individual memory retrievals during decision making, plus a cool RNN modelof that process. Can't express how excited I am about this new technique!
Jonathan Nicholas@jonathannicholas.bsky.social · 2mo agoWe make flexible choices in new situations by knitting together information from separate relevant memories. But what governs which memories are retrieved and when? In a new preprint, we captured how people build decision variables from different memories by tracking their gaze on a blank screen.
Tip for junior authors: "Filling a gap" is not a theoretical motivation. Before submitting a paper, ask which theory or model predicts a different pattern from yours. If no rival prediction is at stake, you're likely testing an effect, not a theory. #cogpsyc #AcademicSky🧪
This one👇🏻 because: 1) the cognitive and brain sciences and AI/ML have typically focused on problem solving and there’s not nearly as much discussion of how we find important problems or what makes a problem worthy of attention; 2) the paper could use more citations. link.springer.com/article/10.1...
From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences - Computational Brain & Behavior
Meta-theoretical perspectives on the research problems and activities of (cognitive) scientists often emphasize empirical problems and problem-solving as the main aspects that account for scientific p...
link.springer.com
Dan Quintana@dsquintana.bsky.social · 2mo agoWhat do you think is your most under-recognised paper and why should people be paying more attention? Go on, take the chance to toot your own horn 🎺
Super excited by this manuscript led by the amazing @brissend.bsky.social By combining fMRI, TMS, and modeling, we finally have causal evidence that the cerebellum is contributing to brain-wide working memory representations and recall performance! 1/n
bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 3mo agoCerebellar perturbation impairs human working memory and degrades spatial tuning throughout cortex https://www.biorxiv.org/content/10.64898/2026.05.14.724968v1
Enjoying the Barcelona APS and seeing all the @um-src.bsky.social data being presented @psychscience.bsky.social
Thinking about how this might go Listener: My chains aren't converging Andrew: Your model is bad Aki: Your parameterization is bad Richard: The Buddha teaches us that numerical integration is suffering
Pierre-Simon Laplace@learnbayesstats.bsky.social · 3mo ago🚨 @rmcelreath.bsky.social @statmodeling.bsky.social & @avehtari.bsky.social are coming on the show mid-June to discuss their new book, Bayesian Workflow! ONE listener gets to bring a real Bayesian problem onto the recording and have the three of them work through it live. Here's how to enter 🧵
A study in @nature.com shows that while complex traits are polygenic in population at large, they have distinct architecture at tails, where rare variants of large effect act as key drivers. Not a surprising finding but neat to see it demonstrated in large UK Biobank cohort across traits & methods.🧪
Distinct genetic architecture in the tails of complex traits - Nature
Genome-wide analyses in multiancestry and European cohorts show that in complex traits, rare alleles have disproportionately large effects at the tails of the phenotypic spectrum compared with common ...
nature.com
direct.mit.edu/imag/article...
Building Bridges Between Brain and Behavior: An Open-Source Toolbox for Joint Modeling with fMRI
Abstract. Understanding how neural activity relates to behavior remains a central challenge in cognitive neuroscience. Joint modeling offers a principled method by simultaneously fitting behavioral an...
direct.mit.edu
It is my mission in life to spend 15 minutes writing footnotes that maybe 3 people will read.
![Translating the DAG in Figure 2B to structural equations, we have Y(t) = aZ, Y(t+1) = bZ + cY(t), ΔY = Y(t+1) – Y(t). Set all variances to 1 for simplicity. Then the total covariance between Z and ΔY is cov(Z,ΔY) = cov(Z,Y(t+1)) – cov(Z,Y(t)) = [b+ac] – [a] = b + a(c–1). Only when c=1 is the true direct effect b recovered without bias. If c<1, then the analysis will underestimate the influence of Z on Y(t+1).](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ta2whs6wjtpljvpqgs5leu36/bafkreihhxkemzp2iukrwm3azsl3pycut2bjs7644fkpzgjv6ame3htuyvy)
1/ Another @sadcatlab.bsky.social paper 👀 Cognitive change methods and cognitive change in CBTs: are they important? What do we know? What is anything? Here we draw a link between the literature on cognitive reappraisal and cognitive restructuring in CBTs. psycnet.apa.org/record/2027-...
100% worth your 63 seconds if you have my reading speed. sinceyouarrived.world/taken
taken.
A web page that tells you what your browser gave away the moment you arrived. No login, no form, no permission. Most pages do this. None of them tell you.
sinceyouarrived.world
some big news! hBayesDM has now been fully ported to cmdstan/cmdstanr/cmdstanpy check out the PR below, or the changelog for an overview: PR: github.com/CCS-Lab/hBay... Changelog: ccs-lab.github.io/hBayesDM/new... stay tuned for some additional features coming soon, including covariate support 🤓
feature: hBayesDM version 2.0 by Nathaniel-Haines · Pull Request #182 · CCS-Lab/hBayesDM
hBayesDM 2.0 — modernize stack & toolchain Top-to-bottom refactor of both the R and Python packages onto current Stan tooling, plus the supporting work to keep behavior, docs, and CI in sync. H...
github.com
📣 New preprint 📣 "Neural Dynamics of Belief and Value Computations Guiding Strategic Social Decisions" We use EEG and computational modeling (combining learning + DDM) to explain participants' choices, RT, and neural signals in a strategic game. www.biorxiv.org/content/10.6...
Neural Dynamics of Belief and Value Computations Guiding Strategic Social Decisions
Successful strategic behavior must be grounded in beliefs about the opponent and her intentions. While many potential models have been proposed to explain choices in such situations, the neural mechanisms that govern learning and choice in complex strategic contexts remain poorly understood. Here, we use a computational model that combines dynamic learning and choice mechanisms to explain both choices and response times of human participants engaged in a competitive strategic task. Using electroencephalography (EEG), we identify temporally structured stages of neural processing that support an evolving value-based decision process, corresponding to first- and second-order belief updates and evidence accumulation related to the comparison of action values. Gamma-band phase coupling between central and parietal EEG signals varied with individual winning rate, suggesting that strategic behavior involves coordinated information transfer across spatially remote areas. Together, our data characterize the temporally evolving neural dynamics of belief and valuation processes that underlie strategic choice and provide neural validation for assumptions embedded in computational models of this behavior. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, 725355
biorxiv.org
Online Now: Rhythmic sampling of decision alternatives through attention
Rhythmic sampling of decision alternatives through attention
Recent work by Siems et al. shows that the brain rhythmically samples competing alternatives through covert spatial attention. This challenges continuous models of decision-making and suggests that evaluation is temporally structured by oscillatory dynamics, with attention determining when alternatives are accessed rather than reflecting changes in their representations.
dlvr.it
