Case Study: A Psychophysiological Approach to Modeling Dual-Task Interference

A research roadmap case study on modeling dual-task interference using pupillometry to enhance performance in high-stakes environments.

🌟 Overview: The High Cost of Cognitive Overload

ā€œImagine an airline pilot mid-flight, gripping the yoke while processing a cascade of system alerts. Or an older surgeon holding robotic instruments while navigating fragile anatomy. In both cases, sustained physical load collides with high cognitive demands—and performance can slip.ā€

In high-stakes environments, understanding the precise point where a user becomes overwhelmed is critical for safety and efficiency. This case study outlines the research roadmap I designed to objectively measure and predict cognitive overload, moving beyond guesswork to create data-driven solutions for safer, more adaptive systems.

The framework combines controlled physical stressors (isometric handgrip), a battery of cognitive tasks, and real-time physiological biomarkers (pupillometry) to deconstruct how performance degrades under real-world pressure.


šŸš€ Applications: My Unique Value in Solving High-Stakes Problems

The true power of this research is its versatility. Below are concrete examples of how this methodology can solve critical R&D problems for industry leaders.

āœˆļø Aerospace & Defense

Problem: Pilots and drone operators must simultaneously manage fine motor control and process complex data streams, where cognitive overload can be catastrophic.

  • Lockheed Martin: Integrate continuous pupillometry into F-35 simulators to compute a live Cognitive Load Index, pinpointing maneuvers that trigger overload and allowing for targeted training to build resilience.

A mockup of a pilot’s heads-up display showing a real-time ā€˜Cognitive Load’ gauge, derived from pupillometry data.
  • General Atomics: Use grip-force calibration and cognitive load testing to redesign Predator drone controls, creating ergonomic interfaces that minimize cognitive interference and free up operator capacity for strategic decision-making.

A UI/UX wireframe of a drone operator’s console, designed with annotations highlighting ergonomic improvements.

šŸ„ High-Stakes Medicine

Problem: Surgeons using robotics platforms like the da Vinci system endure prolonged physical strain that can impair the complex decision-making required for patient safety.

  • Intuitive Surgical: Embed a Dual-Task Performance Dashboard into the surgical console, showing a real-time, pupil-based arousal graph alongside surgical metrics to give surgeons objective feedback on their cognitive state.

A mockup of a surgical console screen displaying a time-series plot of the surgeon’s pupil dilation, indicating cognitive load during a procedure.
  • CAE Healthcare: Enhance simulators to score both task precision and cognitive load, identifying fatigue ā€œhotspotsā€ where performance degrades. This allows for more effective training and debriefing.

A sample chart showing that as cognitive load (pupil dilation, red dashed line) spikes during high-load events, task accuracy (blue line) decreases.

šŸ­ Industry 4.0 & Human-Robot Collaboration

Problem: As factory workers collaborate more closely with robots, their safety depends on maintaining situational awareness while performing manual tasks.

  • Siemens / Rockwell Automation: Develop ā€œsmart toolsā€ that sense a worker’s grip force and use in-helmet eye-tracking to detect cognitive overload. When overload is detected, a nearby collaborative robot could automatically slow down, creating a safer, adaptive manufacturing environment.

A concept illustration showing a worker’s smart tool with embedded sensors providing a real-time data feedback loop to a nearby collaborative robot.

🧠 My Research Approach

My research was architected to be a robust and flexible platform for measuring human performance under pressure.

  • Dual-Task Interference: A paradigm where participants perform a sustained isometric handgrip (at 5% vs 40% of max strength) while simultaneously completing a cognitive task.
  • Multi-Domain Assessment: The methodology uses four distinct cognitive tasks to test the impact of physical load across different domains:
    1. Working Memory (Change Detection Task)
    2. Long-Term Memory (Mnemonic Similarity Task)
    3. Auditory Perception (Auditory Discrimination Task)
    4. Visual Perception (Visual Discrimination Task)
  • Pupillometry: I use pupil diameter as a real-time, objective biomarker of cognitive arousal and mental effort, linked to the brain’s locus coeruleus-norepinephrine system.
  • Cognitive Reserve (LEQ): This allows for a personalized analysis, moving beyond a one-size-fits-all model to understand why some individuals are more resilient to overload than others.

Experiment Pipeline
1. Calibrate MVC → 2. Baseline Pupil & LEQ → 3. Dual‑task Runs (4 tasks Ɨ 2Ɨ2 conditions) →
4. Data Cleaning (blink removal, MVC checks) → 5. LMM Analysis → 6. Cognitive Load Index

A flowchart illustrating the experimental protocol, from participant recruitment and baseline calibration to the dual-task sessions and final analysis.
  • Participants: 50 healthy older adults (60–90 yrs), representing a critical and growing user population.
  • Design: Within-subject 2Ɨ2 (low vs. high physical load) Ɨ (low vs. high cognitive load).
  • Metrics: Accuracy, Reaction Time; moderation by Cognitive Reserve (LEQ) and pupil dilation.
  • Analysis: Linear mixed-effects models (LMMs) were chosen to accurately model individual differences across repeated measurements, providing more statistical power and reliability than traditional ANOVA.
Milestone Date Deliverable
IRB Approval Sep 2025 Protocol & consent forms
Pilot (n=10 younger) Oct 2025 Technical validation of dual‑task setup
Main Data Collection Nov 2025–Feb 2026 Full dataset (50 older adults)
Analysis & Model Build Mar–Apr 2026 LMM scripts + Cognitive Load Index algorithms
Draft Manuscript May 2026 Preprint & conference abstract

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