Scott Warren, University of North Texas
Janetta Boone, NASA
Brent Tincher, Lockheed Martin
Annette Fog, Globe Life
Stephanie L. Robinson, University of North Texas
127
Peer-Reviewed Studies
52 Years
1973–2025
Systematic examination of cognitive load applications in truck and forklift operator training
Identification of critical gaps between training environments and real-world demands
Robson & McCartan, 2016; Sweller, 2010
Inherent task complexity relative to learner expertise (e.g., steering, load manipulation, spatial awareness)
Unnecessary mental processing from poor design or distractions that divert working memory from learning
Productive mental effort dedicated to schema construction and pattern recognition (beneficial for learning)
Cierniak et al., 2008; Chu, 2014; Choi et al., 2014; Sweller, 2010
PsycINFO (cognitive psychology)
ERIC (educational research)
Web of Science (interdisciplinary)
IEEE Xplore & Google Scholar
Modified Cochrane Risk of Bias
78.7% low risk of bias
Timeframe: 1973-2025
Initial screening: 1,847 articles
Final corpus: 127 studies
Effect size analysis (Cohen's d)
Inter-rater reliability (κ = 0.82)
Thematic synthesis approach
Wells et al., 2000; Higgins et al., 2019; Effective Public Health Practice Project, 2009
8
Foundation
1973-1987
15
Theory Dev.
1988-1999
34
Application
2000-2009
42
Environment
2010-2019
28
Technology
2020-2025
70.1% of studies published after 2000; 33.9% focused on industrial/vocational training contexts
Baddeley & Hitch, 1974; Sweller, 1988; Choi et al., 2014; Romine et al., 2020
✓ Controlled conditions
✓ Minimal distractions
✓ Optimal lighting & climate
✓ Psychological safety
✓ Linear task progression
✗ Variable conditions
✗ Constant distractions
✗ Extreme temperatures/noise
✗ Real consequences/stress
✗ Task switching/interruptions
Luo et al., 2023; Choi et al., 2014; Malicka, 2020
Training fails to introduce realistic distractions, resulting in sudden overwhelming cognitive demands in real operations
Surface-level skill focus limits robust schema development necessary for adapting to novel situations
Linear complexity scaling fails to prepare operators for exponential increases when multiple factors combine
Sweller, 2010; Cierniak et al., 2008; Choi et al., 2021
Operators learn in quiet environments but must perform with constant distractions
Focus on individual tasks rather than simultaneous cognitive demands
Rare exposure to stress and complexity of actual emergencies
Emphasis on procedural compliance over deep understanding
Limited teaching of cognitive load self-monitoring
Training lacks contextual cues present in work settings
Chu, 2014; Smith & Vela, 2001; Choi et al., 2014
Affects arterial oxygen saturation and willingness to exert mental effort
Color temperature and luminance influence cognitive performance and mood
Blood glucose levels impact performance on tasks requiring mental effort
Impairs attention and diverts working memory capacity through intrusive thoughts
Training programs rarely address how these physiological and affective factors affect cognitive performance
Choi et al., 2014; Hygge & Knez, 2001; Scholey et al., 2001; Eysenck & Calvo, 1992
Cohen's d = 0.52 to 1.34 (M = 0.83) for matched vs. mismatched training environments
Cohen's d = 0.63 to 1.45 (M = 0.94) for complex tasks exceeding individual working memory capacity
Electrodermal activity correlated with cognitive load: r = 0.67 to 0.84 (M = 0.76); 87% classification accuracy
High-fidelity VR showed 34% better hazard recognition vs. traditional classroom training
Smith & Vela, 2001; Kirschner et al., 2018; Romine et al., 2020; Luo et al., 2023
1
Gradually introduce environmental variability (noise, lighting, temperature) to build adaptive schemas while managing cognitive load
2
Tailor training complexity to individual skill levels with real-time cognitive load assessment and adjustment
3
Use wearable sensors (EDA, heart rate) to provide objective feedback and enable personalized training adjustments
Luo et al., 2023; Romine et al., 2020; Paas & van Merriënboer, 1994
4
Leverage collective working memory effects to distribute cognitive load for complex tasks (effect size d = 0.94)
5
Provide instruction in multiple environmental contexts to build flexible schemas that transfer effectively to various work settings
6
Teach operators to recognize cognitive overload signs and implement strategies for managing mental resources effectively
Kirschner et al., 2018; Paas & van Merriënboer, 1994; Sweller, 2010
Intrinsic, extraneous, and germane load
Contextual processing and variability demands
Temperature, sensory, and autonomic regulation
Shared working memory and coordination
Environmental factors interact multiplicatively with traditional cognitive load types
Context-dependent memory critically affects knowledge transfer effectiveness
Collaborative approaches can manage complex tasks exceeding individual capacity
Choi et al., 2014; Sweller et al., 2011; Kirschner et al., 2018
Organizational resistance
Budget constraints
Expertise requirements
Safety considerations
Leadership commitment
Phased implementation
Data-driven adjustments
Clear ROI demonstration
Older workers may experience different cognitive load patterns requiring modified, personalized training approaches accounting for changes in working memory capacity and processing speed
Automation, robotics, and AI create new cognitive demands requiring different operator skill sets and training adaptations
Romine et al., 2020; Luo et al., 2023
✓ Training environments systematically fail to prepare operators for real-world cognitive demands
✓ Environmental factors interact multiplicatively with traditional cognitive load components
✓ Progressive complexity and real-time monitoring can effectively bridge training gaps
✓ Collaborative approaches leverage collective working memory (effect size d = 0.94)
✓ Implementation requires organizational commitment and specialized expertise
Restructuring training through CLT principles can significantly improve safety outcomes, operational efficiency, and operator preparation
Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In G. A. Bower (Ed.), The psychology of learning and motivation (Vol. 8, pp. 47-90). Academic Press.
Barrouillet, P., Bernardin, S., Portrat, S., Vergauwe, E., & Camos, V. (2007). Time and cognitive load in working memory. Journal of Experimental Psychology, 33(3), 570-585.
Choi, H.-H., van Merriënboer, J. J. G., & Paas, F. (2014). Effects of the physical environment on cognitive load and learning. Educational Psychology Review, 26(2), 225-244.
Choi, S., Kim, D., & Jung, J. (2021). The Effects of Task Selection Approaches on Cognitive Load. IAFOR Journal of Education, 9(4), 83-101.
Chu, H.-C. (2014). Potential Negative Effects of Mobile Learning. Educational Technology & Society, 17(1), 332-344.
Cierniak, G., Scheiter, K., & Gerjets, P. (2008). Explaining the split-attention effect. Computers in Human Behavior, 25(1), 315-324.
Di Lascio, E., Gashi, S., & Santini, S. (2018). Unobtrusive Assessment of Students' Emotional Engagement. Proceedings of the ACM, 2(4), 1-25.
Effective Public Health Practice Project. (2009). Quality Assessment Tool for Quantitative Studies.
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance. Cognition & Emotion, 6(6), 409-434.
Grant, H. M., et al. (1998). Context-dependent memory for meaningful material. Applied Cognitive Psychology, 12(6), 617-623.
Higgins, J. P. T., et al. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). John Wiley & Sons.
Hygge, S., & Knez, I. (2001). Effects of noise, heat and indoor lighting on cognitive performance. Journal of Environmental Psychology, 21(3), 291-299.
Kirschner, F., Paas, F., & Kirschner, P. A. (2009). A cognitive load approach to collaborative learning. Educational Psychology Review, 21(1), 31-42.
Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano R., J. (2018). From Cognitive Load Theory to Collaborative Cognitive Load Theory. International Journal of Computer-Supported Collaborative Learning, 13(2), 213-233.
Luo, Y., et al. (2023). Investigating the impact of scenario and interaction fidelity on training experience. Developments in the Built Environment, 16.
Malicka, A. (2020). The role of task sequencing in fluency, accuracy, and complexity. Language Teaching Research, 24(5), 642-665.
Mehta, R., Zhu, R. J., & Cheema, A. (2012). Is noise always bad? Exploring the effects of ambient noise on creative cognition. Journal of Consumer Research, 39(4), 784-799.
Paas, F., & van Merriënboer, J. J. G. (1994). Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review, 6(4), 351-371.
Robson, C., & McCartan, K. (2016). Real World Research (4th ed.). John Wiley & Sons.
Romine, W. L., et al. (2020). Using Machine Learning to Train a Wearable Cognitive Load Tracker. Sensors, 20(17), 4833.
Scholey, A. B., Harper, S., & Kennedy, D. O. (2001). Cognitive demand and blood glucose. Physiology & Behavior, 73(5), 585-592.
Smith, S. M., & Vela, E. (2001). Environmental context-dependent memory: A review and meta-analysis. Psychonomic Bulletin & Review, 8(2), 203-220.
Sweller, J. (1988). Cognitive load during problem solving. Cognitive Science, 12(2), 257-285.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123-138.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. Springer.
Wells, G. A., et al. (2000). The Newcastle-Ottawa Scale (NOS) for assessing quality of nonrandomised studies.