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Method version v0.1

Our methods.

How we measure cognition, what we claim, what we don’t.

What we measure.

Seven daily drills across six cognitive domains. Each domain maps to a faculty the cognitive-science literature already names; we don’t invent new categories.

Fast Math
Numerical fluency
N-back
Working memory
Digit Span
Working memory
Stroop
Inhibition
Reaction Time
Processing speed
Sudoku
Fluid reasoning
Mental Rotation
Visuospatial

The six-domain map follows the Cattell-Horn-Carroll (CHC) framework - the standard taxonomy in modern cognitive assessment. Within domains that have multiple drills (Working memory: N-back + Digit Span), your domain skill is the higher of the two. Playing both gets you to the same domain ceiling without double-counting.

How Mind index is computed.

Mind Index is one number across the six domains. It’s unbounded - there’s no cap, just diminishing returns past three rated domains.

The formula

domain_skill[d]  =  max( active_skill_rating[g] )  for rated games g in domain d

sorted          =  sort_descending( domain_skill[*] for rated domains )
weights         =  [ 1.0,  0.8,  0.7,  0.5,  0.3,  0.3 ]

Mind Index      =  sum( sorted[i] × weights[i] )   for i = 0..5

Weights are applied by sort order, not by domain - your strongest rated domain gets 1.0, your second gets 0.8, and so on. Concave on purpose: ~70% of your Mind Index comes from your best three domains, so a balanced player still beats a specialist, but a specialist isn’t penalized for not playing every game. A game becomes rated after five sessions on rated configs; rated state, once earned, is permanent - only the inactivity fade below modulates contribution.

Worked example

A balanced 6-domain player at ~700 in each domain has Mind Index = 700 × (1.0 + 0.8 + 0.7 + 0.5 + 0.3 + 0.3) = 2520. A 1-domain specialist at 800 lands at 800; a 3-domain player at 700 / 600 / 500 lands at 700 + 480 + 350 = 1530.

Inactivity fade

If you stop playing a domain, its contribution fades gradually. 90-day grace, then a 1-year half-life. Returning even once snaps the fade factor back to 100%.

0 – 90 days idle
100% - full credit
~6 months
84%
~1 year past grace
59%
~2 years
30%
~5 years
4% - effectively gone, never strictly zero

What we don’t claim.

  • We will NOT claim mindlsn raises IQ or fluid intelligence.
  • We will NOT claim mindlsn delays Alzheimer’s, dementia, or age-related cognitive decline.
  • We will NOT claim mindlsn improves school, work, or athletic performance.
  • We will NOT claim mindlsn treats or reduces ADHD, PTSD, depression, or chemotherapy-related cognitive impairment.
  • We will NOT publish testimonials sourced through paid contests or incentives without clear disclosure.

What we do claim: you’ll get better at the games we offer, and we’ll measure that improvement honestly. That’s it.

The 2016 FTC settlement against Lumos Labs (Lumosity) targeted exactly the claim categories above - cost the company $2M plus a $50M suspended judgment. We list the categories here because you should know what kind of product mindlsn is, and what it explicitly is not.

Our sources.

Each citation underwrites a specific design decision in the methodology above. Updated as we add games or change the formula.

  • Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. Working-memory capacity baseline; underwrites the N-back / Digit Span score interpretation.
  • Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50, 1166–1186. Why even well-validated cognitive tasks have noisy single-session reliability (r=0.4–0.6); we surface this as the session-to-session noise band on N-back and Stroop.
  • Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428. Processing-speed framework; Stroop interference interpretation.
  • Brooker, H., et al. (2019). An online investigation of the relationship between the frequency of word puzzle use and cognitive function in a large sample of older adults. International Journal of Geriatric Psychiatry, 34(7), 921–931. University of Exeter / King’s College London. n=19,000+ puzzle players. Strongest public research backing for inclusion of Sudoku in the battery.

Method version v0.1 - this page captures the methodology as of May 2026. The version number is bumped and dated whenever anything material changes: the formula, the domain weights, the fade table, the anti-claim list, or the source citations. Older method versions stay readable in the changelog.