Ziwei Zhang
@zz112.bsky.social
65 followers 88 following 10 posts
Grad student studying cognition and the brain 🧠 @ the University of Chicago Psychology Interested in how we pay attention and learn
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zz112.bsky.social
Out now in @naturehumbehav.bsky.social: We developed a generalizable brain network model predicting moment-to-moment surprise. This edge-fluctuation-based predictive model (EFPM) of surprise works across tasks, from adaptive learning to watching basketball games or cartoons! rdcu.be/d4y3g
Brain network dynamics predict moments of surprise across contexts
Nature Human Behaviour - Zhang and Rosenberg built a model that predicts surprise from brain network dynamics measured with fMRI revealing similarities across distinct contexts (task learning,...
rdcu.be
Reposted by Ziwei Zhang
jinke.bsky.social
New preprint! 🧠

Our mind wanders at rest. By periodically probing ongoing thoughts during resting-state fMRI, we show these thoughts are reflected in brain network dynamics and contribute to pervasive links between functional brain architecture and everyday behavior (1/10).
doi.org/10.1101/2025...
Ongoing thoughts at rest reflect functional brain organization and behavior
Resting-state functional connectivity (rsFC)-brain connectivity observed when people rest with no external tasks-predicts individual differences in behavior. Yet, rest is not idle; it involves streams...
www.biorxiv.org
zz112.bsky.social
Out now in @naturehumbehav.bsky.social: We developed a generalizable brain network model predicting moment-to-moment surprise. This edge-fluctuation-based predictive model (EFPM) of surprise works across tasks, from adaptive learning to watching basketball games or cartoons! rdcu.be/d4y3g
Brain network dynamics predict moments of surprise across contexts
Nature Human Behaviour - Zhang and Rosenberg built a model that predicts surprise from brain network dynamics measured with fMRI revealing similarities across distinct contexts (task learning,...
rdcu.be
zz112.bsky.social
We thank James Antony, Joseph McGuire, Chang-Hao Kao for sharing the data, Joshua Faskowitz & the brain networks & behavior lab ( www.brainnetworkslab.com ) for sharing the edge time series code. Thanks to @monicarosenb.bsky.social for the help and support on this project. 9/9
zz112.bsky.social
Nor did models built from related behavioral measures (e.g., participants’ prediction, reward). 8/9
zz112.bsky.social
Moreover, models built from BOLD activation alone failed to generalize across datasets to predict surprise. 7/9
zz112.bsky.social
The data-driven surprise EFPM outperformed models built from interactions between and/or within predefined functional brain networks. 6/9
zz112.bsky.social
The same model generalized to predict surprise when people watched NCAA basketball games (www.sciencedirect.com/science/arti...), even when controlling for other features in the games (e.g., video motion). 5/9
zz112.bsky.social
The model predicted surprise in the adaptive learning task (www.sciencedirect.com/science/arti...) in held-out individuals from their functional network dynamics. 4/9
zz112.bsky.social
Using insights from edge-centric neuroscience (www.nature.com/articles/s41...), we built an edge-fluctuation-based predictive model (EFPM) to identify functional interactions predicting moment-to-moment changes in surprise in an adaptive learning task. 3/9
zz112.bsky.social
This is difficult to assess with behavioral measures alone because in some paradigms surprise is measured explicitly whereas in others it is hidden. Characterizing brain dynamics allows us to discover commonalities between surprise in different contexts. 2/9
zz112.bsky.social
New preprint! www.biorxiv.org/content/10.1...
We’re surprised in many situations, like surprise parties, lab tasks, & suspenseful basketball games. Despite being in completely different situations, does our brain process unexpectedness similarly? 1/9
Brain network dynamics predict moments of surprise across contexts
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
www.biorxiv.org