# Event networks and the neural representations that support real-world memory

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $550,835

## Abstract

Project Summary
Memory systems evolved to inform the continual learning and decision making of organisms as they explore
and engage with an enormously complicated world. Humans in particular have a remarkable ability to recount
complex sequences of events: we can easily reconstruct a narrative about the past hour or day purely from
memory. In such real-world remembering, semantic and causal associations become exceedingly important,
defining a web of relational connections across time to guide recall. For example, your day might contain two
"hub" events: a dinner party that requires visiting several shops to pick up supplies, and a morning phone call
saying that your child has a fever and needs to go home; each spawns a multitude of events that make up your
day. Rich associations among these moments form an “event network” whose local and global properties
shape recall; your decisions guide how each event will unfold. While studies show that relations between
simple items are important for memory organization and its accompanying neural computations, no existing
models consider the higher-order structure of networks composed from inter-related naturalistic events. Even
among naturalistic studies, most use passively-viewed movies or stories; participants have no choices to make
or goals to pursue. This lack of attention to the higher-order network properties and volitional aspects of real-
world experiences has hindered efforts to identify the cortical dynamics that underlie ecologically meaningful
memory processes.
We seek to understand how memory encoding and retrieval of realistic events is implemented, in terms of
cortical representations and interactions between brain systems. Doing so requires paradigms with two critical
attributes. First, the stimuli must be sufficiently complex. Memory researchers have long focused on reductive
scenarios with isolated stimuli that intentionally destroy semantic and causal connections. In contrast, our
experiments use realistic events that are richly associated with each other and will naturally generate a
diversity of event network structures. Second, participants must take an active role in creating their memories.
Organisms in the real world can volitionally interact with their input stream: at a crowded party, you can choose
to explore the kitchen or the living room, talk to the biologist or the musician, leave early or stay until dawn. We
will test how participants' volitional behaviors, as they interact with and actively seek information about their
environment, shape event networks and neural representations of events. Altogether, these experiments will
provide novel frameworks and tools to examine how emergent higher-order structure in natural experiences
governs the neural mechanisms underlying encoding and recall. By advancing the level of ecological validity
and stimulus complexity in human memory research, we expect to help uncover brain-behavior relationships
not apparent in simpler paradigms, ...

## Key facts

- **NIH application ID:** 10877986
- **Project number:** 5R01MH133732-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** JANICE CHEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $550,835
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877986

## Citation

> US National Institutes of Health, RePORTER application 10877986, Event networks and the neural representations that support real-world memory (5R01MH133732-02). Retrieved via AI Analytics 2026-05-30 from https://api.ai-analytics.org/grant/nih/10877986. Licensed CC0.

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