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Meet the Affex Team

Educators, cognitive scientists, and product designers united by one goal: making effective learning accessible to everyone.

Affex Team

Learning Science & Product

The Affex content team combines expertise in cognitive psychology, educational technology, and software design. Our writers have hands-on experience with spaced repetition techniques, having used Anki, Noji, and other SRS tools in real study contexts — from medical school preparation to language learning and professional certification exams.

Our Expertise

Spaced Repetition Science

Deep knowledge of the SM-2 algorithm, Ebbinghaus Forgetting Curve, and cognitive load theory.

Educational Technology

Experience with Anki, Mnemosyne, SuperMemo, and modern learning apps across iOS and Android.

Cognitive Psychology

Applied research on active recall, interleaving, elaboration, and retrieval practice.

Language Learning

Practical experience using SRS for Japanese, Spanish, French, and Portuguese vocabulary acquisition.

Our Editorial Standards

All content published on the Affex blog is grounded in peer-reviewed research. We cite primary sources — not secondary summaries — and link directly to studies in journals such as Psychological Science, Journal of Experimental Psychology, and Psychological Bulletin.

Techniques we recommend are ones our team has personally tested in real study contexts. We do not publish speculative claims about memory or learning without scientific backing.

Key References We Build On

  • Ebbinghaus, H. (1885). Über das Gedächtnis — foundational work on the Forgetting Curve.
  • Cepeda, N. J. et al. (2006). Distributed practice in verbal recall tasks. Psychological Bulletin, 132(3), 354–380. PubMed →
  • Roediger, H. L. & Karpicke, J. D. (2006). Test-enhanced learning. Psychological Science, 17(3), 249–255. PubMed →
  • Dunlosky, J. et al. (2013). Improving students' learning with effective techniques. Psychological Science in the Public Interest. PubMed →
  • Wozniak, P. A. (1990). Optimization of Learning — original SuperMemo SM-2 algorithm. supermemo.com →