Why do we remember some events but forget others? In our recent work published at @NatureComms, @AudreyDuarte15 and I used machine learning to simultaneously quantify multiple cognitive components of episodic memory. https://t.co/PtsYBPy0Lf 1/11

This work was a true team effort, and I’d like to thank Wanjia Guo, Dominik Grätz, Eric Wang, Dr. Ulrich Mayr, and Dr. Brice Kuhl, @KuhlLab, for their help throughout the process. I’m especially grateful to Brice for his guidance and support that extended far beyond this paper.

These findings provide insight into how the hippocampus resolves memory interference 'one dimension at a time', demonstrating highly dynamic and adaptive processes that dramatically increase the representational distance between memories that are most at risk for interference.

Finally, dimension-specific input-output functions in CA3/DG strikingly mirrored the sequential pattern observed in behavior: CA3/DG inverted each similarity dimension when it contributed to memory interference but preserved the dimension when it didn't contribute to interference

Among the 10 dimensions, the first 2 dimensions of similarity strongly predicted memory interference errors. However, their influence on behavior sharply changed with experience. Whereas one dimension drove interference earlier in learning, the other drove interference later.

First, we generated a set of natural scene images from two visual categories and rigorously characterized similarity using a wide array of methods. We then applied PCA to these similarity matrices to identify orthogonal components (dimensions) of similarity across the 10 metrics.

🧠🚨 How does the hippocampus transform the visual similarity space to resolve memory interference? In this preprint, we found that the hippocampus sequentially inverts the behaviorally relevant dimensions of similarity 🧵 https://t.co/BB6UQmIqNh

Glad to see our recent work has been featured in @thedailytexan!!

If you're going to #CNS2025, please stop by my poster (C36) on Sunday evening to find out more about this paper and episodic memory's multidimensional scrutiny! 🤓 @CogNeuroNews

This paper is finally out! An amazing effort by @SoroushMirjali2 who used a novel ML approach to covertly asses the multidimensional nature of episodic memory.

How can machine learning help us understand why we remember some events but forget others by analyzing the multiple cognitive components involved in episodic memory?@NatureComms @UTAustin "Using machine learning to simultaneously quantify multiple cognitive components of

I'd like to sincerely thank @jalewpea, @furranko, and Tzyy-Ping Jung for the helpful discussion and Chuu Nyan and Sahana Ram for assisting with the EEG data collection. Lastly, a huge shoutout to my advisor, @AudreyDuarte15 for her endless guidance and support :) 11/11

Overall, not only does this study shed light on the multidimensionality of memory, which is important for basic science, but it also opens avenues for future implications in terms of real-world interventions to improve memory. 10/11

Crucially, the idea of teasing apart a cognitive process into its cognitive components is not restricted to the episodic memory domain and the approach of this study also offers avenues for neuroscientists who are interested in other cognitive domains. 9/11

We also found that the neural evidence of high levels of perception, sustained attention, and selective attention were higher for events preceded by a hit than events preceded by a miss. Similar results were found when comparing the events based on the next event's success. 8/11

We investigated whether the sources' involvement fluctuated depending on how long the participant had been performing the encoding task. We found that as the time-on-task increased, the level of perception, sustained, and selective attention significantly decreased. 7/11

To better define brain states associated with memory success, it is also important to know how long a participant has been encoding events (i.e., the “time-on-task” effect) and whether the previously presented event was successfully encoded (i.e., the encoding “history”). 6/11

By leveraging the sources in a stepwise manner, we could quantify the extent to which each source improved the memory classification performance. While not independent from each other, each three sources explained a unique additional variance of encoding-related activity. 5/11

We found that this multidimensional treatment of memory decoding improved prediction performance compared to traditional, unidimensional, methods. 4/11

Using a machine learning algorithm known as “transfer learning”, we leveraged visual perception, sustained attention, and selective attention brain states (i.e., the "sources") to predict memory performance from trial-to-trial encoding electroencephalography (EEG) activity. 3/11

Previous studies attempting to decode successful vs. unsuccessful encoding brain states have met with limited success, potentially due, in part, to assessing episodic memory as a unidimensional process, despite evidence that multiple domains contribute to episodic encoding. 2/11