Per B. Sederberg

Approximate Whereabouts

I am currently a postdoctoral fellow in Ken Norman's Computational Memory Lab, which is part of the Department of Psychology and the Center for the Study of Brain, Mind, and Behavior (CSBMB) at Princeton University.

Recent Academic History

I graduated from the neuroscience PhD program at the University of Pennsylvania (having moved from Brandeis University with my advisor Mike Kahana). My work in Professor Kahana's Computational Memory Lab focused on the electrophysiological correlates of episodic memory formation and retrieval, as well as neural network models of memory.

I received my undergradutate degree in cognitve science from the University of Virginia, where I worked with Chip Levy on computational models of hippocampal trace conditioning and sequence compression.

Research Interests

The overarching goal of my research is to develop a comprehensive theory of memory formation and retrieval that links our rich cognitive behavior to its underlying neural mechanisms.

Many simple behavioral tasks, such as free recall, have been studied by psychologists for over 100 years and reveal the complex dynamics of human memory processes. Much of my work has focused on uncovering the neural correlates and developing computational models of these processes.

Neural correlates of memory

Gamma oscillations distinguish true from false memories.

I believe in the "start simple" approach to research. As such, I began by trying to understand the most basic memory behavior I could think of: are you going to remember it or not. By analyzing EEG signals recorded from electrodes both on the cortical surface of and deep into the brain of epileptic patients in order to locate the epileptic foci, my colleagues and I discovered that a distinct pattern of high-speed gamma oscillations (fluctuations in the EEG that occur 50-100 times per second) emerges while you are studying an item if you are going to recall that item later. During the subsequent test, that same pattern of gamma oscillations reemerges prior to a response to distinguish correct from incorrect recalls.

In other work, I have attempted to uncover the neural correlates of the primacy effect (that participants tend to recall items from the first few list positions better than items in the middle of the list.) In one scalp EEG study we demonstrated that gamma oscillations are larger for early list items, indicating that the primacy effect is not simply due to rehearsal throughout the list, but also arises from a different brain state at the start of the list.

Recently, I have been applying pattern classification techniques to neural data (both fMRI and scalp EEG) to unravel the interplay between semantic and episodic information during memory retrieval.

Computational modeling

Most of my current modeling centers around expanding the framework of the temporal context model (TCM), originally proposed by Howard and Kahana (2002). The basic idea behind TCM is that episodic memories are formed when items are bound to the temporal context prevailing when that item is presented. Later, your internal state of context is then used as the cue to drive retrieval of that item. One key aspect of the model is that retrieval of an item also retrieves the temporal context present when that item was studied, which explains why after recalling an item, participants then tend to recall items they studied around the same time as the first item.

My colleagues and I recently demonstrated that an expanded version of TCM, with a new retrieval rule based on the Usher and McClelland (2001) competitive accumulator model of decision-making, is able to account for recency and contiguity effects seen in both immediate and continual-distractor free recall.

My current goal is to parameterize an implementation of TCM based on the output from a neural classifier predicting a participant's cue state (episodic vs. semantic) and then use the model to predict their next response. Just your basic mind-reading trick...

Selected Publications

Recent Posters

Make Contact

Feel free to send me an email! My address is persed at princeton dot edu (You know what to do.)

If you're more inclined to send me a letter of the snail-mail variety, here's my University address:

    Per B. Sederberg
    Green Hall
    Princeton University
    Princeton, NJ 08540