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Working Memory

Working Memory

Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...

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Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

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Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task.

Hrishikesh M Rao¹, Christopher J Smalt¹, Aaron Rodriguez¹

¹Human Health and Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, United States.

Frontiers in Human Neuroscience

|July 29, 2020

Summary

This summary is machine-generated.

Researchers developed a virtual reality marksmanship task to monitor cognitive load in service members. Gait and speech patterns accurately identified high cognitive load, aiding mission readiness.

Keywords:

cognitive load marksmanship multimodal physiological features predicting performance virtual environment

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Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

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Published on: November 30, 2022

Area of Science:

Human Factors Engineering
Cognitive Science
Virtual Reality Applications

Background:

Operational environments demand significant cognitive resources, increasing error risk.
Monitoring cognitive burden is crucial for enhancing mission readiness.
Existing methods for assessing cognitive load in real-time are limited.

Purpose of the Study:

To develop and validate a virtual reality (VR) system for assessing cognitive load during operational tasks.
To identify physiological and behavioral indicators of cognitive burden.
To predict performance in marksmanship and working memory tasks.

Main Methods:

A VR marksmanship scenario with an embedded working memory task (3- vs. 6-digit recall) was created.
Physiological (heart rate, breathing) and behavioral (speech, body movement, gait) data were collected.
A random forest classifier analyzed features to distinguish between low and high cognitive load conditions.

Main Results:

The system significantly discriminated between low and high cognitive load (AUC = 0.94).
Gait and speech features were the most effective indicators of cognitive load.
Performance prediction was achieved for digit recall (AUC = 0.71) and marksmanship (AUC = 0.58).

Conclusions:

The developed VR framework effectively quantifies cognitive load in operational simulations.
Behavioral biometrics show promise for real-time cognitive state monitoring.
This framework can be expanded to study other stressors and their impact on performance.