A Neural Hypervector Model of Memory-Driven Spatial Navigation

In this study, we develop an experimental environment for studying human spatial awareness and memory as well as a neuro-computational connectionist model based on hyperdimensional computing, useful for modeling participants’ ability to store and recall stimuli. A qualitative comparison between the proposed model’s confidence, in which it recalls past stimuli, and a participant’s performance in recalling the same past stimuli, was conducted. The results demonstrate that the proposed memory model shows promise for modeling participant performance fall-off, offering a potentially useful tool for neuroergonomics research.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-09-01
DOI
https://doi.org/10.1177/10711813251364803
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Neural Hypervector Model of Memory-Driven Spatial Navigation

Jamison Heard, Esa M. Rantanen, William Gebhardt, Alexander G. Ororbia
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Multimodal Machine Learning Applications
article

A Neural Hypervector Model of Memory-Driven Spatial Navigation

Jamison Heard, Esa M. Rantanen, William Gebhardt, Alexander G. Ororbia
article en

Abstract

In this study, we develop an experimental environment for studying human spatial awareness and memory as well as a neuro-computational connectionist model based on hyperdimensional computing, useful for modeling participants’ ability to store and recall stimuli. A qualitative comparison between the proposed model’s confidence, in which it recalls past stimuli, and a participant’s performance in recalling the same past stimuli, was conducted. The results demonstrate that the proposed memory model shows promise for modeling participant performance fall-off, offering a potentially useful tool for neuroergonomics research.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
Rochester Institute of Technology (US)
National Science Foundation
Openalex Percentile: Top 78%
Multimodal Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.