Evidence-Based

The Research Behind MindFrame

Every training mode, scoring metric, and feedback mechanism in MindFrame is grounded in peer-reviewed research on metacognition, calibration, and cognitive skill development.

Effect Size Evidence Table

What is g? g (Hedges' g), d, and ES are all standardised effect sizes — a universal ruler that lets researchers compare results across different studies and populations. Think of it as a percentile gap: g = 0.63 means a person at the 50th percentile in the trained group would outscore 73% of untrained people.

Scale: 0.20 = small · 0.50 = medium · 0.80 = large · 1.0+ = very large. Values above 0.40 are considered educationally significant (Hattie, 2009).

Intervention	Domain	Effect	Source	Evidence
Metacognitive Training	Academic Performance	g = 0.63Trained people outscore 73% of untrained — beats homework, class-size cuts, and most tutoring	de Boer, Donker & van der Werf (2018)	high
Metacognitive Therapy (MCT)	Anxiety & Depression vs Cognitive Behavioral Therapy (CBT)	g = 0.69In clinical trials, metacognitive therapy showed a larger effect than CBT for anxiety — therapy research, not a claim about MindFrame	Normann & Morina (2021)	high
Calibration Training	Forecasting Accuracy	6–11%Under an hour of probability training consistently improved Brier-score accuracy over a control group	Good Judgment Project (Mellers et al., 2014)	high
Working Memory Training	Cognitive Transfer	g ≈ 0.28Small-to-medium — modest generalisation beyond trained tasks	Basak et al. (2008)	medium
Spaced Repetition	Long-term Retention	d = 0.47–0.71Medium-to-large — strong durable memory across 254 studies	Cepeda et al. (2006)	high
Error Monitoring Training	Decision Accuracy	SignificantSynthesis of rational-thinking research; effect sizes vary by study	Stanovich et al. (2016)	medium

Training Principles

The five research-backed mechanisms MindFrame is built on. Whether they work for you is something your own data will show.

Calibrated confidence, not just accuracy

Getting an answer right is not enough. Knowing when you're right versus guessing — and assigning the correct probability — is what distinguishes expert decision-makers from lucky ones. Every MindFrame challenge requires you to state your confidence alongside your answer.

Brier Score + Calibration Error

Immediate, precise feedback

Vague feedback ("good job") produces no improvement. Improvement requires specific information about where you deviated from ideal performance. MindFrame gives you percentile rankings, calibration error breakdown, and mode-level analytics after every session.

Composite Score, Mode Breakdown, Percentile Rank

Spaced repetition scheduling

The forgetting curve is real. A single exposure to a concept produces brief retention. Reviewing at increasing intervals forces retrieval practice, which produces durable memory and skill. MindFrame uses SM-2 scheduling to surface the right challenge at the right time.

SM-2 adaptive scheduling

Reasoning quality evaluation

Correct answers reached through faulty reasoning don't transfer to novel situations. MindFrame's AI coach evaluates the logical structure and quality of your reasoning, not just whether your final answer was right.

AI Reasoning Score

Reflective consolidation

Research on learning shows that post-session reflection significantly increases knowledge transfer. After every session, MindFrame prompts you to identify your biggest error and the strategy that would have prevented it.

Session Journal