Note: There is no advantage to be nominated by a current member vs. self-nominating. The election process will not indicate how candidates were nominated.
The deadline is Wednesday April 15th.
Feel free to DM me with any questions.
The Memory Disorders Research Society (MDRS) is seeking nominations for new members. Self-nominations are welcome! The society is broadly focused on memory research; not just disorders.
Here is the nomination form, which includes info about eligibility criteria: forms.gle/Qn7mchoPpaqL...
Happy to share some of the work done in our lab in this mega-thread of nine (!) papers/preprints (+1 sneak peek) from the last six months. Here goes (in no particular order)! **Please repost** and let me know if you need access to any of the PDFs! #sleeppeeps #sleep #neuroscience 1/12
More broadly, these findings show that statistical learning can remain flexible even when the same item supports different predictions across changing contexts.
For more info, please check out this exciting work led by my talented grad student Fleming Peck:
www.biorxiv.org/content/10.6...
Why do some models succeed?
🗝️The key is HOW THEY REPRESENT CONTEXT.
Successful models form more distributed representations: the same input produces different hidden states depending on context.
This supports flexible switching and prevents interference.
Only models with moderate weight initialization variance learned both contexts.
Too little variance → stuck in the most recent context
Too much variance → noisy, unstable representations
An intermediate regime enables successful context-dependent learning, and mimicked human performance.
We then trained recurrent neural networks (GRUs) on the same task.
These models learn to predict the next item in a sequence — allowing us to test how context-dependent structure might emerge from experience alone.
No explicit context signal was provided.
Reaction times also revealed online learning.
Participants became progressively faster to the predictable second item in each pair over time, consistent with growing anticipation.
For context-dependent pairs, early slowing may reflect interference from recent context switches.
Participants showed above-chance learning of BOTH associative structures!
This happened even when context was never signaled (fully latent).
Adding an explicit visual context cue (border color) didn’t help, suggesting learners inferred context directly from sequence statistics.
Participants viewed a continuous stream of unfamiliar objects and made a simple perceptual decision on each one.
🥨The twist:
- the same objects predicted different next objects depending on which of two contexts was active
No learning instructions. No feedback. Just exposure.
🚨NEW PREPRINT (w/ Fleming Peck & Hongjing Lu)
"Spontaneous emergence of context-dependent statistical learning in humans and neural networks"
We are constantly predicting what will happen next. Yet the same cue can lead to different predictions depending on context.
How might this work...? 🧵1/8
My favorite conference is the Memory Disorders Research Society meeting. It's a delightful community: top-notch research & wonderful people who have been so supportive in my career.
Want to join? Nominations for membership (including self-nominations) are open until April 9! Form at the top👇🏼
The Memory Disorders Research Society (MDRS) has extended its deadline in seeking nominations for new members to next Wednesday (4/9)! Self-nominations welcome. Feel free to DM me if you have questions about the society or the membership process. Application link: docs.google.com/forms/d/e/1F...
Our results suggest that face perception and memory might be less intertwined than we might think, with implications for prosopagnosia and memory related disorders.
Work led by the fantastic Jan Kadlec.
We test this with an additional face inversion experiment. We find greater inversion effects for easier perceptual levels --> more holistic processing at easy levels --> more interaction at easy levels as a result of face-specific interference which disrupts holistic processing.
Instead, we hypothesize that the face-related distractor in the face-specific interference task disrupts holistic face processing, pushing participants to switch to a feature-based strategy instead.
This pattern of interaction does not fit with theories of increased cognitive load, or overlapping shared resources. Also, just increasing cognitive load through an orthogonal, non-face related interference condition (math), has negligible effect on performance on the face task.
An equally robust finding though, is that under more complex conditions (when an emotional face interference task was inserted into the delay period), we do find interactions. Surprisingly, these go in an unexpected direction - more interaction for easy rather than hard perceptual conditions. Why?
Across four large, independent datasets, with over 800 participants in total, we find clear evidence that face perception and face working memory are fundamentally independent, at least when task demands are low.
We parametrically modulate difficulty in each domain separately. Shared resources would imply interactions between perceptual and memory difficulty, with increasing interaction as difficulty increases. If they are independent however, we would expect difficulty to be additive.
We designed the Face Memory and Perception (FMP) task to systematically test whether perceptual and memory components of face processing rely on shared cognitive resources, or function independently.
Excited to share this new preprint, along with an explainer thread written by my talented collaborator Michal Ramot.
Are face perception and face memory fundamentally independent processes, or are they overlapping and impossible to disentangle? 🧵 www.biorxiv.org/content/10.1...
Still one month left to apply for the tenure-track faculty position in "Measurement Issues in Complex Data Structures" in the UCLA Dept of Psychology.
The scope is broad, but includes methodologists working with brain data (fMRI, EEG, cellular neurophysiology/imaging).
recruit.apo.ucla.edu/JPF09885
Would love to be added if I’m not already on there. Thanks!
🧠👁️ Our MindEye2 preprint is out!
We reconstruct seen images from fMRI activity using only 1 hour of training data.
This is possible by first pretraining a shared-subject model using other people's data, and then fine-tuning on a held-out subject with only 1 hr of data.
arxiv.org/abs/2403.11207
Maybe this one:
Schleim, S. (2022). Why mental disorders are brain disorders. And why they are not: ADHD and the challenges of heterogeneity and reification. Frontiers in Psychiatry, 13, 943049.
www.frontiersin.org/journals/psy...
In case you’re interested in trying out these methods, all of @crewalsh.bsky.social’s code is available at osf.io/5q6th/
Future studies will be needed to better characterize whether these subtle learning-induced semantic distortions are short-lived or whether they can endure for weeks or months.
Despite the general stability of semantic knowledge over the course of one’s lifetime, our results demonstrate that even a brief session of episodic learning can subtly yet systematically re-sculpt semantic space, and this representational change impacts subsequent recall.
Testing, on the other hand, facilitates elaborative connections linking paired words – when these connections don’t already exist (like in semantically related pairs), new connections are created, which subtly change the representation content of both cue and target.