Applying Cognitive Load Theory to Learning
Use cognitive load theory to design better learning experiences, study methods, and instructional materials that respect working memory limits.
Step-by-step guides and clear explanations of complex topics. Break down difficult concepts into understandable pieces with practical examples.
Some ideas are worth understanding deeply. Explainers break down complex topics from cognitive biases to feedback loops, from the Dunning-Kruger effect to confirmation bias into clear, accessible explanations that build genuine understanding, not just surface familiarity.
This collection focuses on psychological phenomena, cognitive science, thinking frameworks, and decision-making processes. Each explainer answers the fundamental questions: How does this work? Why does it matter? When do you encounter it? How can you apply it?
What you'll find: Research-backed explanations, concrete examples from real life, practical applications, connections to related concepts, and insights that change how you see everyday thinking patterns.
Step-by-step introductions for complete beginners
22 articlesReal-world examples and detailed case analyses
18 articlesReady-to-use resources and quick reference guides
25 articlesSide-by-side comparisons of tools, approaches, and concepts
16 articlesClear explanations of systems, processes, and mechanisms
20 articlesCommon errors, misconceptions, and lessons from failure
14 articlesDetailed procedural guides and tutorials
28 articlesClear definitions and explanations of terminology
30 articlesPattern recognition and analytical perspectives
15 articlesDiagnostic guides and problem-solving approaches
12 articles
Use cognitive load theory to design better learning experiences, study methods, and instructional materials that respect working memory limits.
Explore the technical architecture of Git and distributed version control, from commits and branching to merging and conflict resolution.
Learn how to systematically identify underlying causes of problems using techniques like Five Whys, fishbone diagrams, and causal mapping.
Trace the evolution of systems thinking from ancient philosophy through cybernetics to modern complexity science and organizational theory.
Understand incentive mechanics—how rewards and punishments shape behavior, create alignment, and why incentive design matters.
Understand feedback loops—how outputs become inputs, creating reinforcing or balancing cycles that shape system behavior over time.
Compare automated and manual processes—understand when automation worth the investment, when manual better, and tradeoffs in each approach.
Practical decision checklist—systematic steps for making better decisions under uncertainty, reducing bias and improving outcomes.
Analyze incentive failures—real cases where reward systems created perverse behavior, gaming, and outcomes opposite to intent.
Learn systems thinking fundamentals—understanding interconnections, feedback loops, and why problems require holistic approaches beyond linear thinking.
The Dunning-Kruger effect is a cognitive bias where people with limited knowledge or competence in a domain overestimate their own ability. The less you know, the harder it is to recognize what you're missing. This creates a paradox: incompetence hides itself. As people gain expertise, they become more aware of the gaps in their knowledge.
Confirmation bias is the tendency to search for, interpret, and remember information that confirms pre-existing beliefs while ignoring contradictory evidence. It affects decision-making by creating blind spots you see what you expect to see rather than what's actually there. This bias is one of the most pervasive obstacles to clear thinking and rational judgment.
Feedback loops are mechanisms where outputs of a system circle back as inputs, creating self-reinforcing (positive feedback) or self-correcting (negative feedback) cycles. Positive loops amplify changes and drive exponential growth or collapse. Negative loops stabilize systems and maintain equilibrium. Understanding feedback loops is essential for systems thinking and recognizing patterns in complex environments.
Cognitive biases are systematic patterns of deviation from rationality in judgment and decision-making. They're mental shortcuts (heuristics) that help us process information quickly but can lead to errors. They matter because they're predictable, pervasive, and often invisible to the person experiencing them. Awareness of cognitive biases is the first step toward clearer thinking.
Availability bias (or availability heuristic) is the tendency to overestimate the likelihood of events that are easy to recall or imagine. If you can quickly think of examples, you assume they're common. This leads to distorted risk assessment plane crashes feel more likely than car accidents because they're more memorable and widely covered in media, even though statistically car accidents are far more frequent.
Anchoring bias occurs when people rely too heavily on the first piece of information they encounter (the 'anchor') when making decisions. Even irrelevant numbers can influence subsequent judgments. In negotiations, the first offer sets an anchor that affects all following counteroffers. Awareness of anchoring helps you recognize when initial information is disproportionately influencing your thinking.
The sunk cost fallacy is the tendency to continue investing in something because you've already invested time, money, or effort even when continuing no longer makes sense. Past costs are irrelevant to future decisions, but psychologically, we feel compelled to justify previous investments. Recognizing sunk costs helps you make decisions based on future value rather than past commitment.