The
Architecture of
Learning
OVERVIEW
The Architecture of Learning examines the cognitive, neurobiological, and psychological systems that make learning possible. Rather than treating learning as a single process, this research domain explores how attention, perception, memory, executive functions, language, emotion, motivation, and prior knowledge interact to construct understanding over time. It provides the scientific foundation for designing educational experiences that align with how learning actually occurs.
RESEARCH SYNTHESIS
The Architecture of Learning: Understanding How Humans Learn
A conceptual synthesis examining learning as the interaction of multiple cognitive and neurobiological systems rather than as the simple acquisition of information.
RESEARCH BRIEFS
Learning Begins With Attention
Why attention determines what information can enter the learning process.
Memory Is Constructed, Not Stored
Understanding how encoding, consolidation, retrieval, and reconstruction shape learning.
Executive Functions Orchestrate Learning
How planning, inhibition, cognitive flexibility, and working memory regulate complex learning.
Emotion Shapes Memory
Why emotional significance influences what we remember, what we forget, and how learning is consolidated.
Learning Requires Cognitive Effort
Why desirable difficulty, retrieval, and active processing strengthen long-term learning.
Prior Knowledge Changes Everything
How existing knowledge determines comprehension, transfer, and new learning.
Learning Is a Network
Why cognition emerges through the interaction of perception, language, memory, attention, executive functions, and emotion.
Motivation Sustains Learning
How motivation influences persistence, cognitive engagement, and self-regulated learning.
VISUAL FRAMEWORKS
The Architecture of Learning
A conceptual framework illustrating the interaction of attention, perception, memory, executive functions, language, emotion, and motivation during learning.
The Learning Cycle
A visual model describing the progression from perception to understanding, consolidation, retrieval, transfer, and application.
The Cognitive Systems of Learning
Illustrating how cognitive systems interact to support learning rather than functioning independently.
From Experience to Long-Term Memory
A framework connecting attention, working memory, consolidation, retrieval, and meaningful learning.
FOUNDATIONAL STUDIES
The following studies represent key contributions that continue to shape contemporary understanding of learning and inform LAJAM's ongoing research on The Architecture of Learning.
Attention
Petersen, S. E., & Posner, M. I. (2012). The Attention System of the Human Brain: 20 Years After.
A landmark synthesis explaining the organization of the brain's attentional systems and their central role in learning, cognitive control, and information processing.
Working Memory
Constantinidis, C., & Klingberg, T. (2016). The Neuroscience of Working Memory Capacity and Training.
A comprehensive review describing the neural mechanisms of working memory, its relationship to higher cognitive abilities, and the evidence for cognitive training.
Executive Functions
Diamond, A. (2013). Executive Functions.
A foundational synthesis describing inhibition, working memory, and cognitive flexibility as core executive processes supporting learning, reasoning, and self-regulation.
(Yes, this is slightly older than our preferred window, but I would absolutely keep it because it remains one of the defining syntheses in the field.)
Memory & Retrieval
Gonçalves, A. O., Muniz, B. F. B., & Jaeger, A. (2025). Retrieval Practice Versus Elaborative Encoding: A Systematic and Meta-analytic Review.
Synthesizes contemporary evidence comparing retrieval practice with other evidence-based learning strategies, refining our understanding of long-term learning and retention.
Emotion & Learning
Immordino-Yang, M. H., & Damasio, A. (2007). We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education.
Demonstrated that emotion is fundamental to attention, memory formation, and meaningful learning rather than separate from cognition.
(Another indispensable landmark that continues to shape contemporary educational neuroscience.)
Motivation & Self-Regulated Learning
Vansteenkiste, M., Aelterman, N., et al. (2025). Blending Teacher Autonomy Support and Provision of Structure in the Classroom for Optimal Motivation: A Systematic Review and Meta-analysis.
Synthesizes contemporary evidence on how autonomy support and instructional structure interact to promote motivation, engagement, and self-regulated learning.
Cognitive Load
Zou, L., Zhang, Z., Mavilidi, M., et al. (2025). The Synergy of Embodied Cognition and Cognitive Load Theory for Optimized Learning.
A contemporary review integrating Cognitive Load Theory with embodied cognition to advance evidence-based instructional design.
Learning Sciences
Privitera, A. J., Ng, S. H. S., & Chen, S.-H. A. (2025). Cognitive and Neural Mechanisms of Learning and Interventions for Improvement Across the Adult Lifespan: A Systematic Review.
A broad synthesis examining the cognitive and neural mechanisms that support learning and the evidence for interventions that improve learning across the lifespan.
SELECTED REFERENCES
Baddeley, A. D. (2012). Working Memory: Theories, Models, and Controversies.
Carpenter, S. K., Pan, S. C., & Butler, A. C. (2022). The Science of Effective Learning with Spacing and Retrieval Practice.
Constantinidis, C., & Klingberg, T. (2016). The Neuroscience of Working Memory Capacity and Training.
Cowan, N. (2016). Working Memory Capacity.
Diamond, A. (2013). Executive Functions.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving Students' Learning with Effective Learning Techniques: Promising Directions from Cognitive and Educational Psychology.
Gonçalves, A. O., Muniz, B. F. B., & Jaeger, A. (2025). Retrieval Practice Versus Elaborative Encoding: A Systematic and Meta-Analytic Review.
Immordino-Yang, M. H., & Damasio, A. (2007). We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education.
Mayer, R. E. (2021). The Cambridge Handbook of Multimedia Learning (3rd ed.).
Petersen, S. E., & Posner, M. I. (2012). The Attention System of the Human Brain: 20 Years After.
Privitera, A. J., Ng, S. H. S., & Chen, S.-H. A. (2025). Cognitive and Neural Mechanisms of Learning and Interventions for Improvement Across the Adult Lifespan: A Systematic Review.
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later.
Vansteenkiste, M., Aelterman, N., et al. (2025). Blending Teacher Autonomy Support and Provision of Structure in the Classroom for Optimal Motivation: A Systematic Review and Meta-Analysis.
Willingham, D. T. (2009). Why Don't Students Like School?