The F-35 Paradox
The F-35 Lightning II cockpit represents the pinnacle of aerospace engineering sophistication. A single large-format touchscreen replaces hundreds of switches and dials. Advanced sensor fusion integrates data from radar, infrared search and track, electronic warfare systems, and data links from other platforms. The distributed aperture system provides 360-degree spherical coverage. Helmet-mounted display projects symbology directly onto the pilot’s visor. The aircraft processes and presents more information than any previous fighter in aviation history.
And pilots are overwhelmed.
Not because they lack training—F-35 pilots undergo extensive preparation. Not because the systems fail technically—the sensor fusion works as designed. But because managing the volume of information these systems provide while executing tactical maneuvers, monitoring threats, coordinating with other aircraft, and making split-second decisions under high-G stress approaches and sometimes exceeds human cognitive capacity, creating situations where the most technically advanced fighter in the world produces performance degradation because its operators cannot effectively process what the aircraft is telling them fast enough to employ it optimally.
This is not equipment failure. This is incomplete engineering.
The Army Research Laboratory has identified cognitive workload as a critical factor affecting soldier performance and mission success, yet a comprehensive NATO study examining measurement approaches across member nations reached a stark conclusion—there is limited research measuring cognitive load in dynamic real-world settings with high mobility, precisely the environments where operators face maximum demand (Hollands et al., 2025; NATO STO, 2025).
This represents more than measurement deficiency—it reveals systematic blind spot in how defense technology is developed, acquired, and fielded. We measure every technical parameter with extraordinary precision. System latency to milliseconds. Algorithm accuracy to decimal points. Component reliability across operational lifetimes. Network resilience under degraded conditions. Entire engineering disciplines, procurement frameworks, and career trajectories have been built around optimizing these technical metrics because the principle is fundamental: you cannot improve what you do not measure.
But the cumulative cognitive cost imposed on operators managing these systems simultaneously? Largely unmeasured. The gap between human cognitive capacity and system information output continues widening, creating conditions where technically sophisticated systems exceed operator capacity to utilize them effectively under operational stress—not because operators lack competence, but because the aggregate demand from dense technology stacks compounds in ways that remain invisible to conventional acquisition processes.
In Tech Tides, I examined how power shifts through overlooked filters—variables that shape outcomes but remain invisible to conventional analysis, hidden mechanisms that provide context others miss. Cognitive performance in defense technology represents precisely such a filter, one that operates silently as systems become progressively more sophisticated while the operators using them face mounting cognitive demands that nobody systematically tracks until degradation manifests through errors that should not have occurred, decisions that should have been faster, or adaptation that should have been smoother.
This is not an argument against innovation. Technology advancement is essential for maintaining capability advantage. But incomplete engineering—optimizing technical performance while treating operator cognitive performance as unmeasured externality—creates strategic vulnerability disguised as technical progress. The question is not whether to innovate but whether innovation will include the complete system, technical and cognitive, in its optimization framework.
What Gets Measured Gets Optimized
Defense technology has achieved extraordinary sophistication in measuring and optimizing technical performance. Every procurement decision involves extensive specification of requirements. Performance benchmarks that systems must achieve. Interoperability standards ensuring integration. Reliability metrics defining acceptable failure rates. Entire testing regimes validating that technical parameters meet thresholds before systems enter service.
This rigor reflects institutional recognition that technical performance is measurable, that measurement enables optimization, and that optimization translates directly into operational capability. The approach has produced remarkable results—weapons systems of unprecedented accuracy, communications networks with extraordinary throughput, sensors capable of detecting signatures once invisible, computational systems processing vast data volumes in real-time.
But cognitive performance? The approach is fundamentally different. Assessment occurs primarily post-facto through error detection after problems manifest, or through binary qualification testing that determines whether operators meet minimum standards but provides no visibility into how close they are to those standards during actual operations, how their performance degrades under sustained demand, or how cumulative load from managing multiple systems simultaneously affects their decision quality over extended periods.
Multiple measurement tools exist, developed through decades of human factors research and validated across numerous studies. The NASA Task Load Index captures operators’ perceived workload through standardized questionnaires addressing mental demand, physical demand, temporal demand, performance, effort, and frustration. The Detection Response Task measures cognitive load through reaction time to stimuli, with degraded performance indicating increased cognitive demand. Physiological indicators including heart rate variability, pupil dilation, and electroencephalography patterns provide continuous assessment of cognitive state. Eye tracking reveals cognitive load through fixation patterns, saccade velocity, and blink rates, with longer fixations indicating increased processing difficulty and scattered scan patterns suggesting information search rather than monitoring (Hollands et al., 2019).
These tools work effectively in laboratory settings and controlled environments. Research examining message presentation rate effects on soldiers found that fast presentation reduced Detection Response Task accuracy and increased response times relative to slow presentation, demonstrating that cognitive load measurement can detect meaningful differences in how information presentation affects operator performance (Hollands et al., 2019). But translating these approaches to operational contexts faces significant constraints.
When operating indoors with moderate-to-low mobility, most cognitive load measures can be used effectively according to NATO’s comprehensive assessment. The challenge emerges in field conditions where mobility requirements increase and environmental factors compound—large-scale exercises with high mobility requirements present significant challenges for measuring cognitive load with existing options, creating a situation where measurement becomes most difficult precisely in the contexts where cognitive demand is highest and where performance degradation could have most serious consequences (NATO STO, 2025).
This operational gap becomes stark when examining specific defense contexts. Current technology allows extensive options for information display, graphical interfaces, and input devices, with designers having unprecedented flexibility in how they present information to operators. But as researchers examining tactical display systems observed more than two decades ago, the availability of these options has the potential to produce severe information overload for an infantry soldier who is hot or cold, tired, and stressed, creating conditions where technical capability to display information exceeds operator capacity to process it effectively (National Research Council, 1997).
That assessment was made in 1997 when the technology stack available to individual soldiers was orders of magnitude less complex than what they manage today. The intervening decades have seen exponential growth in data volume, system count, interface complexity, and information streams that operators must monitor, integrate, and act upon—yet the fundamental constraint identified then remains unaddressed, with cognitive performance still assessed primarily through post-facto error analysis rather than real-time visibility into operator state that would enable proactive intervention before performance degradation produces mission failure.
The IVAS Reality
The Integrated Visual Augmentation System represents this challenge in microcosm. Designed to provide soldiers with unprecedented situational awareness through augmented reality displays integrated into combat goggles, IVAS combines thermal imaging, night vision, mapping, target acquisition, and communication systems into a single interface projected directly into the soldier’s field of vision. The technical capability is extraordinary—soldiers can see through walls using thermal sensors, mark targets that appear as icons overlaid on their actual field of view, receive navigation waypoints displayed as virtual markers in physical space, and maintain visual connectivity with their squad’s positions and status.
But in field testing, soldiers reported cognitive saturation. Not equipment malfunction—the systems functioned as designed. But managing the volume of information IVAS presented while maintaining tactical awareness of their physical environment, monitoring threats, coordinating with their unit, and making decisions under combat stress created cognitive demands that, in the words of soldiers testing the system, felt like “drinking from a firehose.” The problem was not that soldiers lacked intelligence or training but that the aggregate cognitive load from processing augmented reality overlays, integrating multiple information streams, distinguishing between virtual and physical visual cues, and executing tactical tasks simultaneously approached or exceeded cognitive capacity for sustained periods.
Senior leadership recognized the challenge directly. Brigadier General Tony Potts, serving as Program Executive Officer for PEO Soldier, articulated the concern when discussing IVAS and related systems: “We worry about human cognition, we worry about sensory saturation in all these things. With the terabytes of data that are going to be available to them, how do we make sure that warfighters are optimally utilizing the data that is being made available to them?” The question reveals fundamental uncertainty about whether current approaches to system development adequately consider operator cognitive capacity as constraint on system utility rather than treating operators as having unlimited capacity to manage whatever systems procurement officials decide to acquire.
The IVAS challenge illustrates the broader pattern. Each system passes technical specifications individually through acquisition testing that validates functionality, reliability, and performance against defined benchmarks. But cumulative cognitive load imposed on operators managing IVAS alongside their rifle, radio, navigation system, tactical display, biometric sensors, and mission-specific equipment? This variable remains unmeasured through acquisition processes that focus on technical capability without systematically assessing cognitive cost.
Consider the operational reality that human factors researchers have identified through examination of soldier performance in network-centric warfare. A mounted soldier-operator monitors their formation of manned and unmanned ground vehicles, manages attached unmanned aerial vehicle assets providing overhead surveillance, and maintains tactical radio communications with multiple echelons. At the moment when they notice problems with one of the unmanned ground vehicles, they simultaneously lose contact with an aerial vehicle and receive preliminary indications of enemy activity on their flank—three distinct situations each requiring assessment, prioritization, and response occurring simultaneously rather than sequentially (National Research Council, 2009).
Each system individually represents manageable cognitive demand for which the operator has received training. Each scenario individually falls within expected operational parameters that doctrine addresses. But all three occurring simultaneously is likely to produce cognitive overload because human attention and working memory have finite capacity that training cannot expand indefinitely, creating situations where technically functional systems and qualified operators produce suboptimal outcomes not because of equipment failure or incompetence but because aggregate demand exceeds cognitive capacity—and this overload remains invisible to technical performance monitoring systems that track every parameter except the one that matters most in that moment, which is whether the operator can effectively process what the systems are providing.
We have sensors tracking every technical parameter across these systems. Real-time telemetry transmitted to maintenance systems. Automated alerts generated when thresholds are approached or violated. Predictive maintenance algorithms forecasting component failure before it occurs. System health constantly monitored, reported, analyzed.
We do not have equivalent real-time visibility into the cognitive state of the operator managing all these systems simultaneously under operational stress, trying to maintain situational awareness across multiple domains, integrate information from disparate sources, prioritize actions under time pressure, and make decisions where errors have tactical or strategic consequences—and this asymmetry in what gets measured creates systematic blind spot in how defense capability is understood, developed, and employed.
The Compounding Problem
Every technology upgrade adds cognitive demand to operators, introducing new interfaces that must be monitored, additional information streams that must be integrated, protocols that must be remembered, failure modes that must be anticipated, and alerts that must be triaged—with each addition individually justified by capability improvement it provides but collectively creating cumulative load that nobody systematically tracks as systems are added to the stack operators must manage.
The transformation of the U.S. military services into a highly networked force has markedly increased the need for rapid collection and dissemination of vast amounts of data, with the fusion and display of that data in formats that soldiers can readily comprehend and act upon becoming critical requirement for networked operations that link platforms, sensors, and decision-makers across unprecedented geographic scales and organizational echelons (National Research Council, 2009). Network systems now deliver such extensive data to soldiers that it can overpower the brain’s processing capacity, creating situations where information superiority paradoxically produces information overload that degrades rather than enhances decision quality.
Senior military leadership has articulated this concern directly. Brigadier General Tony Potts, serving as Program Executive Officer for PEO Soldier, stated the challenge explicitly when discussing future soldier systems: “We worry about human cognition, we worry about sensory saturation in all these things. With the terabytes of data that are going to be available to them, how do we make sure that warfighters are optimally utilizing the data that is being made available to them?” (Modern Military Training, 2019). The question is not rhetorical—it represents fundamental uncertainty about whether current approaches to system development adequately consider operator cognitive capacity as constraint on system utility.
Each system passes technical specifications individually through acquisition testing that validates functionality, reliability, and performance against defined benchmarks. Integration testing validates interoperability, confirming that systems can exchange data and function together without conflicts. But cumulative cognitive load imposed on operators managing the complete technology stack simultaneously under operational conditions? This variable remains unmeasured through acquisition processes that focus on technical capability without systematically assessing cognitive cost.
Understanding why this matters requires examining how cognitive systems respond to sustained demand over time. The concept of allostatic load, introduced by neuroendocrinologists Bruce McEwen and Eliot Stellar in 1993, describes the physiological wear and tear that accumulates as individuals are exposed to repeated or chronic stress, with the body’s regulatory systems maintaining stability through continuous adjustment that produces cumulative cost even when functioning effectively in the moment (McEwen & Stellar, 1993). This framework applies directly to cognitive systems, where allostatic load refers to prolonged dysregulation related to chronic stress that affects brain regions including the hippocampus, amygdala, and prefrontal cortex—areas critical for memory formation, emotional regulation, and executive function that enable operators to maintain situational awareness, assess threats, prioritize actions, and make decisions under pressure (Lenart-Bugla et al., 2022).
The mechanism is well-established through extensive neuroscience research. Moderate stress activates adaptive responses that enhance performance through increased arousal, focused attention, and mobilization of resources. But chronic exposure to elevated demand produces cumulative effects as stress hormones including cortisol and catecholamines become dysregulated, inducing an inter-connected cascade effect on inter-dependent biological systems that progressively degrade even as individual biomarkers remain within normal ranges—with the aggregate impact manifesting as decreased cognitive flexibility, impaired working memory, slowed processing speed, and reduced capacity to adapt to changing conditions (Juster et al., 2023).
For defense operators, this manifests as decreased situational awareness where peripheral information is missed or integrated slowly, delayed decision-making as cognitive processing slows under high load, and increased errors as working memory capacity becomes saturated and attention narrows. Combat systems designed by engineers often prioritize technical capability over human usability, creating interfaces that technically display all necessary information but practically overwhelm operators during critical moments when cognitive load is highest, producing situations where mission failure occurs not because information was unavailable but because operators could not effectively process the volume of information their systems provided under the operational stress they experienced (National Research Council, 1997).
The problem is not static but dynamic and accelerating. Systems continue evolving toward greater complexity as each upgrade adds capability by increasing sophistication of algorithms, expanding data sources being integrated, introducing new modes of operation, and creating additional failure conditions that operators must recognize and respond to. Soldiers will have access to ever-increasing amounts of data as networks expand, sensors proliferate, and processing systems become more capable—and they will need to adapt to new means for understanding those data as interfaces change, information presentation evolves, and cognitive demands shift in ways that training must address but cannot eliminate (Hollands et al., 2025).
Each upgrade compounds cognitive demand. Technical improvements get measured through specifications that validate performance gains. Cognitive costs imposed on operators remain unmeasured through acquisition processes that assess systems individually rather than evaluating cumulative load across the complete technology stack operators must manage simultaneously during sustained operations under conditions that maximize stress while minimizing recovery opportunity—and this systematic failure to measure the full cost of technical sophistication creates vulnerability disguised as capability enhancement.
High Performers Face Distinct Challenges
Defense operators are not random selections from general populations but are specifically chosen for high performance under stress through selection processes designed to identify individuals with superior cognitive abilities, trained extensively through programs that develop skills for managing complex systems, and evaluated continuously through qualification standards that maintain operational readiness. This creates a critical research gap because most cognitive load studies use convenience samples drawn from undergraduate student populations, general community volunteers, or other populations that differ systematically from military operators in baseline cognitive capacity, stress resilience, and performance under pressure—meaning that research findings may not translate directly to the elite performer populations that defense forces rely upon.
Elite performers respond differently to stress than general populations across multiple dimensions. Research examining how acute stress affects cognition demonstrates that stress is associated with both beneficial and detrimental effects on cognition in different individuals, with individual brain state moderating whether stress enhances or impairs performance—and critically, one putative biomarker of high-stress resilient individuals appears to be their ability to elevate Executive Control Network connectivity in response to stressors, suggesting that neural architecture differences may enable some individuals to maintain or even enhance performance under conditions that degrade performance in others (Vogel et al., 2017).
Studies of elite athletes, who represent populations analogous to military operators in facing high-performance demands under high-stress conditions, reveal patterns that illuminate dynamics relevant to defense contexts. In elite sports, competition outcomes are often primarily driven by psychological factors rather than pure physical capability, with superior technical skills becoming decisive advantage only when cognitive and emotional factors enable effective application of those skills under pressure—but elite athletes simultaneously face increasing physical, cognitive, and emotional stress that can either enhance or harm their wellbeing and mental health depending on intensity and whether they have access to adequate situational resources and coping strategies that enable them to manage demands without experiencing chronic overload (Gerber et al., 2025).
This suggests existence of threshold effects where performance relationship to demand is non-linear. Below certain thresholds, increasing demand enhances performance by increasing arousal, focusing attention, and mobilizing resources. But above those thresholds, additional demand begins degrading performance as cognitive systems become saturated, stress responses interfere with rather than support function, and cumulative load impairs the very capabilities that operators need most under pressure. Research on overtraining in athletes provides direct evidence for this pattern—excessive increases in training load negatively influence cognitive function even in elite athletes, with reaction time increasing in both simple reaction tasks and more complex Stroop colour-word tests while psychomotor speed decreases, indicating compromised performance across multiple cognitive domains when demand exceeds capacity for adaptation and recovery (Lee et al., 2023).
Researchers studying stress and performance have proposed that this relationship follows what they term a “hormetic” pattern, where low-to-moderate stress induces cognitive benefits that promote resilience through adaptive responses that strengthen rather than degrade function, but where this benefit reverses above certain thresholds as demand becomes excessive—with perceived stress showing hormetic U-shape association with performance outcomes where both too little and too much stress degrade performance relative to optimal moderate levels (Oshri et al., 2022). This framework suggests that defense operators may perform optimally under moderate cognitive load that activates without overwhelming their systems, but that excessive cumulative load from dense technology stacks could push them past optimal points into regions where performance begins degrading even though operators remain technically functional and continue producing output that meets minimum standards.
The invisibility problem compounds these dynamics because operators may not accurately self-assess their own cognitive degradation under high load conditions. Mental fatigue causes decreases in multiple aspects of performance even in elite performers, with coaches and leaders commonly undertaking cognitively demanding tasks that place them at risk for performance impairment they may not recognize in themselves until performance metrics reveal problems that subjective assessment missed (Browne et al., 2023). Subjective measures of cognitive load including questionnaires and self-reports suffer from recall bias where individuals misremember their experiences, social desirability effects where they report what they believe others want to hear rather than their actual state, and limited introspective access to cognitive processes that operate largely outside conscious awareness—meaning that operators who feel they are performing adequately may in fact be operating with degraded capacity they cannot perceive.
Research examining military-relevant contexts has demonstrated that the ability to measure physiological response to stressors and correlate that response to task performance could be used to identify resilient individuals who maintain effectiveness under pressure and to identify those at risk for stress-related performance decrements who would benefit from additional support or different task assignments—but this capability requires systematic measurement infrastructure that current operational environments largely lack (Saxby et al., 2015).
The implications are significant. Operators selected for high performance under stress may maintain adequate function even when experiencing substantial cognitive load because their baseline capacity exceeds minimum requirements by comfortable margins, creating situations where they remain operationally effective while operating closer to their performance ceilings than anyone realizes. Their output remains defensible by conventional metrics. Critical errors do not necessarily manifest immediately. Mission success may still be achieved. But they are operating with narrowed margins—reduced capacity to handle additional stressors if operational tempo increases, degraded decision quality that may not be apparent until compared against what optimal performance would have produced, and slower adaptation to changing conditions that creates vulnerability when adversaries introduce unexpected variables.
Still operational, in the language of defense qualification systems. But no longer optimal in terms of what the same operator could achieve if aggregate cognitive demand remained within ranges that enabled sustained high performance rather than forcing operation at capacity limits where small additional stressors could trigger performance collapse.
That gap between operational and optimal represents strategic vulnerability that current measurement approaches cannot detect, creating conditions where forces appear capable based on qualification standards while operating with degraded effectiveness that emerges only when operational stress exceeds the thresholds that testing environments never approach—and where adversaries who have optimized their operators’ cognitive performance gain decisive advantages not through superior equipment but through superior human-machine system integration that conventional technical assessments never measure.
From Measurement to Engineering Optimization
If cognitive performance is measurable through validated tools and frameworks that decades of research have established, then it becomes optimizable using the same engineering rigor that defense organizations apply to technical systems—requiring systematic measurement, analysis of factors affecting performance, interventions targeting specific deficiencies, validation of intervention effectiveness, and continuous improvement processes that treat cognitive performance as system variable requiring management rather than as fixed constraint that must be accepted.
The measurement tools exist and have been validated across diverse populations and contexts. Physiological indicators including heart rate variability provide continuous assessment of autonomic nervous system state that correlates with cognitive load, with decreased variability indicating increased load as regulatory capacity becomes taxed. Pupil dilation responds to cognitive demand with measurable changes occurring on timescales of milliseconds, enabling real-time tracking of moment-to-moment fluctuations in processing load. Electroencephalography patterns reveal neural activity associated with attention, working memory, and executive function, with specific frequency bands and event-related potentials providing signatures of cognitive state. Eye tracking captures fixation patterns, saccade velocity, and blink rates that reveal cognitive load through behavioral markers—longer fixations indicating increased processing difficulty as operators struggle to extract meaning from complex displays, scattered scan patterns suggesting information search behavior rather than efficient monitoring, and reduced peripheral vision utilization indicating cognitive tunneling where attention narrows to central focus at the expense of situation awareness (Hollands et al., 2025).
Behavioral measures complement physiological data by capturing performance outcomes that reflect cognitive state. The Detection Response Task, validated specifically for military applications, measures cognitive load through reaction time to simple stimuli presented during primary task performance, with degraded reaction time indicating that cognitive resources are consumed by primary task demands leaving less capacity available for responding to secondary stimuli. Research examining message presentation rate effects on soldiers using battle management systems found that fast message presentation reduced Detection Response Task accuracy and increased response times relative to slow presentation while also producing higher subjective workload ratings across multiple NASA Task Load Index subscales, demonstrating that cognitive load measurement can detect meaningful differences in how system design affects operator performance and that different measurement approaches produce converging evidence when load increases (Hollands et al., 2019).
These tools work. The limitation is not measurement validity but operational implementation—making the sensors sufficiently unobtrusive that operators can use them during actual missions without interference, making the data processing sufficiently robust that measurements remain reliable in field environments with movement artifacts and environmental noise, and making the interpretation sufficiently automated that real-time assessment can inform operational decisions rather than only enabling post-mission analysis that identifies problems after they have occurred.
Performance engineering for cognitive systems would require several integrated components operating across acquisition, training, and operational employment. First would be integrated system assessment that evaluates cognitive impact during the acquisition process before systems are fielded, treating cognitive load as procurement criterion alongside technical specifications. Measuring cognitive load of the soldier is key component to balancing workload between user and machine for secure, efficient, and effective human-machine interaction in a variety of different scenarios—but current processes assess systems individually for usability without evaluating cumulative load when new systems are integrated into existing stacks that operators already manage, creating situations where each system passes human factors testing in isolation while the aggregate effect on operators managing full technology stacks remains unmeasured (Hollands et al., 2025).
This would require procurement processes to explicitly assess how proposed systems interact with existing cognitive demands, what load they add to operators who are already managing multiple systems simultaneously, at what cumulative load performance begins degrading for operators at different skill levels, and how degradation will be measured during operational testing that replicates rather than simplifies the complexity of actual employment. These questions are not currently addressed systematically during acquisition, with decisions driven primarily by technical capability assessments that treat operators as having unlimited cognitive capacity to manage whatever systems procurement officials decide to acquire.
Second would be real-time monitoring during operations that provides commanders with visibility into cognitive state of operators under their command. Real-time measurement may provide improved understanding of automation effects on soldier cognitive load, enabling adaptive systems that adjust their behavior based on operator state—and monitoring cognitive load of operators and commanders could be used to adjust information flow so as not to exceed limits of human attention or working memory, with systems reducing update rates, filtering lower-priority information, or assuming greater autonomy temporarily when operators approach overload thresholds (Hollands et al., 2025).
This is not theoretical speculation but represents feasible engineering given current technology. Adaptive automation systems already implement similar principles for technical parameters, adjusting level of system autonomy based on operator workload and performance by maintaining operator control when cognitive load is low to preserve situation awareness, automating routine tasks as workload increases to free cognitive resources for higher-priority decisions, and temporarily assuming greater control under extreme load conditions while alerting operators that automation has increased its role—creating dynamic allocation that optimizes human-machine teaming across varying cognitive demands rather than forcing operators to manage all functions regardless of their current capacity (National Research Council, 2009).
The technology exists to measure operator state through physiological sensors that are becoming progressively less intrusive, to analyze that data through algorithms trained on validated datasets linking physiological signatures to performance outcomes, and to adapt system behavior through automation architectures that already adjust to technical parameters. Extending these capabilities to include cognitive load as measured variable is engineering implementation challenge rather than fundamental research problem, requiring integration of existing components into systems architecture that treats operator cognitive state as system parameter to be monitored and managed rather than as externality to be ignored.
Third would be training programs that build operator capacity to manage high cognitive load effectively through exposure to complex scenarios that approach operational demands, development of strategies for prioritizing and shedding tasks when capacity is approached, and deliberate practice at maintaining performance under conditions of information overload. Research examining cognitive training for military applications has identified specific approaches including working memory training that expands capacity to maintain and manipulate information, attention control training that enhances ability to sustain focus and resist distraction, and task-switching training that improves efficiency of transitioning between different cognitive operations—with meta-analyses suggesting that targeted training can produce meaningful improvements in cognitive performance that transfer to operational contexts (Brunyé et al., 2020).
Fourth would be cognitive impact assessment as standard component of procurement decisions parallel to technical specifications and interoperability requirements. Questions would include: What cognitive load does this system add to operators managing current technology stacks? How does load scale as operators move from managing one system to managing ten systems simultaneously? How does performance degrade as cumulative load increases, and at what thresholds does degradation become operationally significant? How will degradation be measured in operational contexts that cannot be replicated in laboratory testing? What recovery requirements must be provided to prevent cumulative load from producing chronic impairment?
These questions are not currently asked systematically during acquisition. Technical capability gets optimized through specifications, testing, and validation. Cognitive cost remains unmeasured externality that operators must absorb without systems or processes to track whether they have capacity to do so effectively—and this creates conditions where forces acquire technically sophisticated systems that exceed operator capacity to utilize them optimally, producing outcomes where investment in capability enhancement paradoxically degrades effectiveness by overwhelming the humans who must employ what technology provides.
This is not about making things easier for operators through reduction of mission complexity or operational demands. It is about making operators more effective by ensuring that technical capability matches and enhances rather than exceeds human capacity, that system design considers not only what technology can do but also what operators can manage while maintaining performance under operational stress, and that the complete human-machine system receives engineering attention rather than only the technical components being optimized while operator performance is treated as fixed input that must be accepted rather than variable output that can be improved through systematic intervention.
The Strategic Imperative and Research Agenda
The temporal mismatch that governs cognitive warfare applies with equal force to defense technology development. Technology evolves exponentially as computing power increases, algorithms become more sophisticated, sensors become more capable, and networks become more ubiquitous—while human adaptation proceeds linearly at best, with biological evolution operating on timescales measured in millennia, cultural adaptation operating on timescales measured in generations, and individual learning operating on timescales measured in years. This growing gap creates capability vulnerabilities that manifest not through equipment failure or technical inadequacy but through operators’ inability to fully utilize systems when cognitive demand exceeds capacity—and conventional assessment focused on technical metrics cannot detect this form of vulnerability until operational failures reveal problems that testing never anticipated.
Research provides direct evidence that more information does not guarantee better decisions beyond optimal thresholds where additional data begins degrading rather than enhancing performance. In decision-making experiments where subjects requested more information after exceeding optimal complexity levels, performance degraded through information overload as subjects became less accurate and less confident in their judgments while taking longer to make decisions—with the degradation occurring despite subjects having requested the information and believing it would improve their decisions, suggesting that even sophisticated decision-makers cannot reliably assess when additional information will help versus harm their performance (Thibodeau, 2020).
The military has recognized this explicitly through official statements acknowledging that investment in human factors engineering, user research, and iterative design may seem expensive compared to adding technical capabilities, but that cost of cognitive overload measured in mission failures and lost lives far exceeds these investments—making optimization of human-machine interface a strategic imperative rather than optional enhancement. Cost-benefit analysis favors systematic measurement and optimization of cognitive performance, with returns measured not in procurement cost savings but in operational effectiveness, mission success rates, and reduction of errors that occur when operators are overwhelmed by systems that provide more information than they can effectively process under operational conditions.
But there is competitive dimension beyond internal cost-benefit calculation. Near-peer adversaries conduct systematic research on operator performance optimization as component of military-civil fusion strategies that integrate academic research, commercial development, and military application in ways that Western separation of these domains makes difficult to replicate. Their literature addresses cognitive performance explicitly as variable to be measured and optimized rather than fixed constraint to be accepted, with particular emphasis on how system design can enhance rather than degrade operator effectiveness under high-stress conditions. If competitors systematically optimize their operators’ cognitive performance while Western forces focus primarily on technical capability of systems, the result is competitive disadvantage that manifests not in technical specifications that can be benchmarked but in human-machine system integration that determines which force can actually employ its technology effectively under operational stress.
Technical sophistication becomes irrelevant if operators cannot utilize it effectively when it matters most. The force with slightly less sophisticated technology but operators who can employ it optimally under stress defeats the force with technically superior systems that overwhelm their operators, creating conditions where investment in capability enhancement produces capability degradation because the optimization focused on wrong variable.
This represents research agenda requiring systematic investigation across multiple dimensions where current understanding remains inadequate. The first critical gap involves cumulative cognitive load across dense technology stacks. Current research measures cognitive load imposed by individual systems through controlled experiments that isolate specific variables. Some studies examine pairs of systems or simple combinations. But operators routinely manage ten, fifteen, or twenty integrated systems simultaneously during operations—and how cognitive load compounds across dense technology stacks remains largely unknown. Is the relationship additive where each system contributes independently to total load? Is it multiplicative where interactions between systems create load beyond simple sum? Are there threshold effects where adding one more system to an already-complex stack produces disproportionate degradation because it exceeds some fundamental capacity limit? These questions lack answers because the research examining full-stack complexity under operational conditions has not been conducted at scale, leaving procurement decisions and system design based on assumptions about scaling that may not reflect actual operator experience.
The second gap involves operational timeframe studies examining how sustained exposure to complex systems affects performance over realistic deployment cycles. Most cognitive load research uses laboratory settings that last hours or at most days, with field exercises extending to weeks in exceptional cases. But deployments last months with operators managing complex technology stacks continuously under varying operational tempo, sleep deprivation, environmental stress, and mission pressure—and how this sustained exposure affects cognitive performance over six-month or twelve-month operational cycles remains understudied. Do operators adapt to high complexity through skill development that makes what initially produced high load become more automatic? Does performance degrade progressively as cumulative load exhausts adaptive capacity? Are there recovery requirements analogous to physical training periodization where periods of high load must be followed by periods of lower demand to prevent chronic impairment? Current research cannot answer these questions because longitudinal studies tracking individual operators through complete deployment cycles while measuring cognitive performance at frequent intervals are rare, leaving commanders without evidence-based guidance for managing cognitive load across sustained operations.
The third gap involves high-performer specific research examining how military operators differ from general populations used in most cognitive studies. Selection processes screen for cognitive ability, stress resilience, and performance under pressure—creating populations that may respond to cognitive load differently than undergraduate students, community volunteers, or other convenience samples that dominate research literature. Research on elite athletes suggests that high performers demonstrate distinct stress response patterns compared to general populations, with some individuals showing enhanced rather than degraded performance under moderate stress through mechanisms including increased executive control network connectivity, more effective coping strategies, and greater cognitive flexibility—but whether these patterns extend to military operators and what this means for how cognitive load should be measured and managed in defense contexts remains uncertain because systematic research on military operator populations is limited by access constraints, operational security considerations, and difficulty conducting controlled experiments with participants who cannot be randomly assigned to experimental conditions (Gerber et al., 2025; Vogel et al., 2017).
The fourth gap involves technology stack interaction effects that emerge when systems are integrated into operational environments. Acquisition testing assesses systems individually for usability through human factors evaluations that confirm operators can learn to use the system, perform required functions, and maintain performance over relevant time periods. But what happens when that system is integrated into existing technology stack with twelve other systems operators must simultaneously manage? Do interaction effects emerge where combined load exceeds sum of individual loads because operators must switch attention between systems, maintain awareness across all systems even while focused on specific tasks, and manage conflicts when multiple systems demand attention simultaneously? Does the fifteenth system impose disproportionate cognitive load compared to the fifth because it represents one more element in attention space that has limited capacity? No systematic framework exists for assessing these interaction effects during acquisition, with integration testing focused on technical interoperability while cognitive interaction effects remain largely unexplored, creating risk that systems passing all acquisition testing produce unexpected performance degradation when employed in operational complexity.
The fifth gap involves real-time measurement in operational contexts where cognitive load is highest and where performance degradation has most serious consequences. NATO’s comprehensive review concluded that large-scale exercises with high mobility requirements present significant challenges for measuring cognitive load with existing options, identifying fundamental limitation that measurement becomes most difficult precisely in contexts where it matters most (NATO STO, 2025). The measurement technology exists for controlled environments where movement is limited, environmental conditions are stable, and experimental procedures can be implemented without operational constraints. Making measurement work reliably in high-mobility combat operations where operators experience physical stress, environmental extremes, and mission demands that cannot be interrupted for data collection represents unsolved engineering problem requiring sensor development, algorithm robustness, and integration approaches that current systems do not provide.
The sixth gap is less research challenge than process implementation gap—integrating cognitive performance as procurement criterion alongside technical specifications. Defense acquisition extensively specifies technical requirements through performance parameters, reliability standards, and interoperability protocols that systems must meet before acceptance. But no standardized cognitive impact assessment framework exists for procurement decisions, with cognitive considerations addressed unsystematically if at all during acquisition processes that focus primarily on technical capability. This gap represents opportunity rather than fundamental barrier because the knowledge exists regarding how to measure cognitive load, the tools exist for conducting assessments, and the frameworks exist for evaluating human-machine system performance—but systematic application across defense acquisition lags behind research capability, creating situation where better processes could produce immediate improvements without requiring new scientific breakthroughs.
These gaps represent strategic opportunity for forces that systematically address them through research investment, process reform, and operational implementation. The force that measures and optimizes cognitive performance gains advantage not through superior technology but through superior integration of human and technical capabilities that enables more effective employment of whatever technology is available. This advantage manifests in mission success rates, decision quality under stress, adaptation speed when situations change, and sustained performance over extended operations—outcomes that determine actual capability rather than the technical specifications that only establish potential capability.
Measuring What Matters
The Vietnamese peasant who heard the ghost tape attempting to manipulate his perception encountered it within collective frameworks that provided resilience stronger than the manipulation, with unit cohesion and revolutionary consciousness offering alternative interpretation of fear that prevented terror from determining behavior. Contemporary defense operators encounter technology stacks of unprecedented complexity without systematic frameworks for measuring and managing the cognitive demands these systems impose, creating vulnerability where operators remain technically qualified while operating with degraded performance that nobody systematically tracks until failures reveal problems that could have been detected and addressed if measurement infrastructure existed.
This represents more than operational inconvenience—it reveals fundamental incompleteness in how defense capability is understood and developed. We have achieved extraordinary sophistication in building systems but need equivalent sophistication in optimizing the operators using them, treating cognitive performance not as separate human factors concern but as integrated engineering challenge where technical and cognitive optimization occur simultaneously as components of unified system design process.
The measurement tools exist, validated through decades of research and proven effective in contexts ranging from aviation to medicine to industrial operations. The frameworks exist, developed through systematic investigation of how cognitive load affects performance and what interventions improve human-machine system effectiveness. The strategic imperative exists, created by widening gap between technical complexity and human capacity that produces vulnerability disguised as capability.
What is required now is systematic application—treating cognitive performance as engineering variable requiring measurement, analysis, optimization, and continuous improvement rather than as fixed constraint that must be accepted. This means procurement processes that assess cognitive impact as criterion alongside technical specifications. Training programs that build operator capacity to manage high cognitive load effectively through deliberate practice under realistic conditions. Real-time monitoring during operations that provides visibility into operator state enabling proactive intervention before performance degrades. System designs that optimize human-machine interaction rather than maximizing technical capability without considering operator constraints. Leadership commitment to measuring and managing cognitive performance with same rigor applied to technical performance.
The operators managing complex technology stacks under operational stress deserve the same engineering attention given to the systems they are operating—measurement systems tracking their cognitive state as precisely as technical parameters are monitored, design processes optimizing their interface with technology rather than only optimizing the technology itself, procurement frameworks evaluating whether systems enhance their effectiveness rather than only whether systems function technically, and operational procedures that treat their cognitive capacity as strategic resource to be managed rather than unlimited input that can absorb whatever demands technology and mission impose without consequence.
This is not about making operations easier by reducing complexity or lowering standards. It is about making operators more effective by ensuring that technical capability serves rather than exceeds human capacity, that innovation includes the complete system rather than only the technical components, and that forces gain capability through optimized integration of human and machine rather than through sophisticated technology deployed without systematic attention to whether operators can actually employ it effectively when it matters most.
The forces that recognize this gain advantage. Those that continue measuring only technical variables while treating cognitive performance as unmeasured externality build systems their operators cannot fully utilize under operational stress, creating vulnerability that adversaries who optimize their complete human-machine systems will exploit systematically.
The Question That Haunts Tomorrow
Here is what keeps me awake: What if our adversaries are already doing this?
China’s military-civil fusion strategy explicitly integrates academic research on human performance with military system development. Their literature addresses cognitive optimization as engineering challenge requiring systematic measurement and intervention. Russia’s research institutes study operator performance under stress as component of human-machine system effectiveness. Both invest in understanding not just what technology can do but what operators can accomplish when technology and human capacity are optimized together rather than technical sophistication being pursued without equivalent attention to cognitive integration.
We may be in the position of developing fifth-generation fighters while adversaries are developing fourth-generation fighters with third-generation cognitive load—producing situations where technically superior systems operated by cognitively saturated pilots face technically adequate systems operated by pilots whose cognitive capacity has been systematically optimized to employ what their aircraft provide effectively under operational stress. The technically superior system loses not because it lacks capability but because its operator cannot access that capability when it matters most.
This is not science fiction speculation. The research exists. The measurement tools exist. The frameworks exist. What differs is commitment to systematic application—treating cognitive performance as variable requiring engineering attention rather than fixed constraint to be accepted, measuring it with same rigor applied to technical parameters, and optimizing the complete human-machine system rather than only the technical components.
The next major conflict may not be decided by which force fields technically superior systems but by which force has optimized its operators to employ whatever systems they field more effectively. By which force has measured and managed the unmeasured variable while others continued optimizing only what conventional metrics captured. By which force recognized that the revolution in military affairs produced by information technology creates capability only when operators can employ it—and that employment capability depends on cognitive performance that nobody systematically tracks in the forces that built the most sophisticated technology in human history.
The choice is clear. The tools exist. The research agenda is defined. The strategic imperative is evident.
What is required now is recognition that we may already be behind—that while we have optimized technical sophistication to unprecedented levels, adversaries may have optimized the variable we left unmeasured, creating advantage that will manifest not in specifications that can be benchmarked but in effectiveness under operational stress where wars are decided and where the force that can actually employ its technology optimally defeats the force with superior technology that overwhelms its operators.
The unmeasured variable determines outcomes. And what remains unmeasured remains unoptimized regardless of how sophisticated the measured variables become.
This is the filter others miss—the overlooked variable that shapes capability without appearing in specifications, the hidden mechanism that determines whether technical sophistication translates into operational effectiveness, the strategic factor that has been systematically ignored while resources flow toward optimizing variables that are easier to measure but may matter less for actual performance under conditions where wars are fought and missions are executed.
The question is not whether to measure cognitive performance. The question is whether we will measure it before our adversaries’ investment in optimizing what we ignored produces the advantage that technical superiority cannot overcome.
That is the question that haunts tomorrow. And the answer will be written not through procurement decisions focused on technical specifications but through recognition that complete engineering requires measuring all the variables—including the one that may already determine who prevails when technically sophisticated systems meet operationally optimized integration of human and machine capabilities under the stress where outcomes are decided.
The unmeasured variable.
Measure it now, or discover too late that adversaries already have.
References
Browne, S. E., Isacco, L., Millet, G. Y., & Rossi, A. (2023). Thinking about elite performance: The experience and impact of mental fatigue in elite sport coaching. International Journal of Sports Science & Coaching, 18(3), 805-815. https://doi.org/10.1177/17479541231168580
Brunyé, T. T., Brou, R., Doty, T. J., Gregory, F. E., Hussey, E. K., Lieberman, H. R., … & Taylor, H. A. (2020). A review of US Army research contributing to cognitive enhancement in military contexts. Journal of Cognitive Enhancement, 4(1), 3-18. https://doi.org/10.1007/s41465-019-00155-8
Gerber, M., Ludyga, S., Mucke, M., Colledge, F., Pühse, U., & Lindwall, M. (2025). Stress and its impact on elite athletes’ wellbeing and mental health—A mini narrative review. Frontiers in Sports and Active Living, 7, 1630784. https://doi.org/10.3389/fspor.2025.1630784
Hollands, J. G., Spivak, T., & Kramkowski, E. W. (2019). Cognitive load and situation awareness for soldiers: Effects of message presentation rate and sensory modality. Human Factors, 61(5), 763-773. https://doi.org/10.1177/0018720819825803
Hollands, J., Binsch, O., Bourguignon, N., Cahill, L., Darses, F., Heaton, K., … & Witte, T. (2025). Measuring the cognitive load on the soldier (NATO STO Technical Report HFM-319). NATO Science and Technology Organization. https://www.sto.nato.int/publications/STO%20Technical%20Reports/STO-TR-HFM-319/
Juster, R. P., Bourdon, O., Trabold, N., Castellanos-Ryan, N., Séguin, J. R., & Lupien, S. J. (2023). Advancing the allostatic load model: From theory to therapy. Psychoneuroendocrinology, 155, 106321. https://doi.org/10.1016/j.psyneuen.2023.106321
Lee, H., Martin, D. T., Anson, J. M., Grundy, D., & Hahn, A. G. (2023). Impact of overtraining on cognitive function in endurance athletes: A systematic review. Sports Medicine – Open, 9(1), 62. https://doi.org/10.1186/s40798-023-00607-9
Lenart-Bugla, M., Szcześniak, D., Bugla, B., Kowalski, K., Niwa, S., & Rymaszewska, J. (2022). The association between allostatic load and brain: A systematic review. Psychoneuroendocrinology, 145, 105917. https://doi.org/10.1016/j.psyneuen.2022.105917
McEwen, B. S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093-2101. https://doi.org/10.1001/archinte.1993.00410180039004
Modern Military Training. (2019, November 19). How PEO Soldier is addressing cognitive overload. https://modernmilitarytraining.com/lvc-today/how-peo-soldier-is-addressing-cognitive-overload/
National Research Council. (1997). Tactical display for soldiers: Human factors considerations. The National Academies Press. https://doi.org/10.17226/5436
National Research Council. (2009). Opportunities in neuroscience for future army applications. The National Academies Press. https://doi.org/10.17226/12500
NATO Science and Technology Organization. (2025). Measuring the cognitive load on the soldier (Technical Report HFM-319). https://www.sto.nato.int/publications/STO%20Technical%20Reports/STO-TR-HFM-319/
Oshri, A., Kogan, S. M., Kwon, J. A., Wickrama, K. A. S., Vanderbroek, L., Palmer, A. A., & MacKillop, J. (2022). Is perceived stress linked to enhanced cognitive functioning and reduced risk for psychopathology? Testing the hormesis hypothesis. Psychiatry Research, 314, 114644. https://doi.org/10.1016/j.psychres.2022.114644
Saxby, D. J., Bryant, A. L., Modenese, L., Gerus, P., Killen, B., Fortin, K., … & Lloyd, D. G. (2015). Identification of resilient individuals and those at risk for performance deficits under stress. Frontiers in Neuroscience, 9, 328. https://doi.org/10.3389/fnins.2015.00328
Thibodeau, D. (2020). Information overload: A warfighter’s challenge (Canadian Forces College Joint Command and Staff Programme Paper). Canadian Forces College. https://www.cfc.forces.gc.ca/259/290/22/305/Thibodeau.pdf
Vogel, S., Kluen, L. M., Fernández, G., & Schwabe, L. (2017). Cognitive benefit and cost of acute stress is differentially modulated by individual brain state. Frontiers in Human Neuroscience, 11, 337. https://doi.org/10.3389/fnhum.2017.00337
