Jie Sun
@jie-sun.bsky.social
PhD Candidate at University of Melbourne. Computational neuroscience, memory, EEG, evidence accumulation models of decision making.
Pinned
Jie Sun
@jie-sun.bsky.social
· Jul 25
A Parietal Memory Strength Signal Linked to Evidence Accumulation in Recognition Decisions https://www.biorxiv.org/content/10.1101/2025.07.20.665783v1
🧠 New preprint alert!
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
Never truly understood what people meant by 'stretched too thin' until I was stretched too thin.
October 23, 2025 at 5:47 AM
Never truly understood what people meant by 'stretched too thin' until I was stretched too thin.
Reposted by Jie Sun
Using EEG 🧠⚡ and representational similarity analysis, we mapped how the neural representations of food attributes (e.g., taste & health) unfold over time when viewing foods🍎🍰🥗 @danfeuerriegel.bsky.social @tgro.bsky.social
theconversation.com/our-brains-e...
doi.org/10.1016/j.ap...
theconversation.com/our-brains-e...
doi.org/10.1016/j.ap...
Redirecting
doi.org
October 22, 2025 at 1:26 AM
Using EEG 🧠⚡ and representational similarity analysis, we mapped how the neural representations of food attributes (e.g., taste & health) unfold over time when viewing foods🍎🍰🥗 @danfeuerriegel.bsky.social @tgro.bsky.social
theconversation.com/our-brains-e...
doi.org/10.1016/j.ap...
theconversation.com/our-brains-e...
doi.org/10.1016/j.ap...
Reposted by Jie Sun
Honey, we fixed Signal Detection Theory (SDT)! In this preprint, Constantin Meyer-Grant, David Kellen, Sam Harding, and I critically evaluate the (unequal-variance) Gaussian SDT model in recognition memory and pursue the Gumbel-min model as a principled alternative: doi.org/10.31234/osf...
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Extreme-Value Signal Detection Theory for RecognitionMemory: The Parametric Road Not Taken
Signal Detection Theory has long served as a cornerstone of psychological research, particularly in recognition memory. Yet its conventional application hinges almost exclusively on the Gaussian…
doi.org
April 27, 2025 at 2:46 PM
Honey, we fixed Signal Detection Theory (SDT)! In this preprint, Constantin Meyer-Grant, David Kellen, Sam Harding, and I critically evaluate the (unequal-variance) Gaussian SDT model in recognition memory and pursue the Gumbel-min model as a principled alternative: doi.org/10.31234/osf...
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Reposted by Jie Sun
What if forgetting was not the result of time erasing memories but interference from new, similar experiences?
It was what Dr Raina Zhang from the Complex Human Data Hub calls a “mind-blowing” theory that set her on the path to study the mechanisms behind memory.
Learn more: go.unimelb.edu.au/ut7p
It was what Dr Raina Zhang from the Complex Human Data Hub calls a “mind-blowing” theory that set her on the path to study the mechanisms behind memory.
Learn more: go.unimelb.edu.au/ut7p
Meet Dr Raina Zhang: the researcher redefining how we understand memory and forgetting
Meet Dr Raina Zhang: the researcher redefining how we understand memory and forgetting
go.unimelb.edu.au
July 25, 2025 at 12:50 AM
What if forgetting was not the result of time erasing memories but interference from new, similar experiences?
It was what Dr Raina Zhang from the Complex Human Data Hub calls a “mind-blowing” theory that set her on the path to study the mechanisms behind memory.
Learn more: go.unimelb.edu.au/ut7p
It was what Dr Raina Zhang from the Complex Human Data Hub calls a “mind-blowing” theory that set her on the path to study the mechanisms behind memory.
Learn more: go.unimelb.edu.au/ut7p
🧠 New preprint alert!
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
A Parietal Memory Strength Signal Linked to Evidence Accumulation in Recognition Decisions https://www.biorxiv.org/content/10.1101/2025.07.20.665783v1
July 25, 2025 at 1:12 AM
🧠 New preprint alert!
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
In this study, using a joint modelling method with the Diffusion Decision Model, we offer a mechanistic reinterpretation of the Late Positive event-related potential Component (LPC) as a neural signature of mnemonic strength during evidence accumulation in recognition memory.
Reposted by Jie Sun
We recently updated our preprint that explains how to deal with unidentifiability constraints when measuring participants' decision-making cognition as well as introducing new methods to measure participants' decision-making cognition from brain+behavioral data
osf.io/preprints/ps...
osf.io/preprints/ps...
OSF
osf.io
January 17, 2025 at 3:47 PM
We recently updated our preprint that explains how to deal with unidentifiability constraints when measuring participants' decision-making cognition as well as introducing new methods to measure participants' decision-making cognition from brain+behavioral data
osf.io/preprints/ps...
osf.io/preprints/ps...
🚨New Pre-print is out!
What causes the drift rate to vary across trials? How much does the drift rate variability estimate in the Diffusion Decision Model reflect the true variability? Here, we critically examined this by including trial-level regressors of drift rate.
osf.io/preprints/ps...
What causes the drift rate to vary across trials? How much does the drift rate variability estimate in the Diffusion Decision Model reflect the true variability? Here, we critically examined this by including trial-level regressors of drift rate.
osf.io/preprints/ps...
OSF
osf.io
January 25, 2025 at 2:00 AM
🚨New Pre-print is out!
What causes the drift rate to vary across trials? How much does the drift rate variability estimate in the Diffusion Decision Model reflect the true variability? Here, we critically examined this by including trial-level regressors of drift rate.
osf.io/preprints/ps...
What causes the drift rate to vary across trials? How much does the drift rate variability estimate in the Diffusion Decision Model reflect the true variability? Here, we critically examined this by including trial-level regressors of drift rate.
osf.io/preprints/ps...