IQMindware

Train general intelligence, then prove carryover.

About IQMindware

Built for applied cognition,
not surface scores.

Most cognitive apps improve performance in one fixed format. IQMindware is built to help your thinking hold up more broadly — across changing tasks, real-world demands, and modern work shaped by distraction and AI assistance. The aim is not just faster responses inside an app, but stronger underlying capacity for judgement, learning, and adaptive performance.

Start here See proof
Skills training and self-regulation, not diagnosis or treatment.
Founder and scientific lead

Dr Mark Ashton Smith

Dr Mark Ashton Smith

I am Dr Mark Ashton Smith, a cognitive neuroscientist trained at the Center for the Neural Basis of Cognition, a joint programme between the University of Pittsburgh and Carnegie Mellon University.

I was a lecturer and researcher in the Department of Psychology at the University of Cambridge for several years, and most recently a Lecturer in Cognitive Psychology and Neuroscience at the University of Essex Online.

For the last two decades, I have worked on cognitive interventions with one central question: how to produce gains that are portable and carry over beyond practice effects into real problem solving, decisions, and learning.

Recent ideas from computational cognitive neuroscience helped clarify the core design principles. I synthesised these into the Trident G computational framework, which informs IQMindware protocols.

OSF profile LinkedIn profile Protocol index Deep Theory
Why this exists

Three problems most training does not address

Mission

Help people think clearly under load and recover faster after disruption.

IQMindware is a Mindware Lab product focused on transferable cognitive performance. We design tools that move from baseline assessment to readiness checks, then to structured training and delayed re-checks that fit busy, interruption-heavy schedules.

The objective is deployability in real missions: decision quality under uncertainty, controlled execution under load, and practical script use across changing contexts.

The problems

Near transfer, thin automation, and poor carry-over.

Near transfer: training improves performance inside one fixed format but gains do not carry over when the surface form changes. You improve in the app but the real task does not get easier.

Thin automation: AI-assisted speed creates conditions where people offload reasoning before capacity is secure. The tool works — but the underlying judgement weakens over time.

Poor carry-over: even genuine gains rarely survive a context switch, a disrupted week, or a format that has not been practised. Portability has to be trained in from the start.

Who this is for

Designed for people whose thinking has to hold under real conditions

Students and exam-focused learners

Who want gains that hold under test conditions — where format changes and time pressure are the real challenge, not just performance within one training wrapper.

Knowledge workers

Managing distraction, AI-assisted speed, and rising demands on judgement — where the pressure is not peak performance, but consistent decision quality under real conditions.

Professionals in high-stakes roles

Where cognitive resilience on a disrupted day matters as much as peak performance on a good one — controlled execution and clear reasoning under load, not only under ideal conditions.

Adults interested in cognitive longevity

Who want to develop and maintain cognitive capacity over time, with transparent tracking and a structured programme rather than habit-tracking or brain-game scores.

The framework

What Trident G is

What it is

A computational framework synthesising principles from cognitive neuroscience — specifically the conditions under which cognitive capacity generalises beyond the training context. Read the Deep Theory note.

What it governs

Protocol design, training logic, and the criteria used to judge whether gains are genuine — including how transfer is defined, measured, and tested under changed conditions.

Why it matters

It is the reason IQMindware is built differently from products that optimise in-app scores. The framework sets the standard: transfer under changed conditions, not performance in one fixed format.

Principles

What governs the system design

1

Transfer is the standard

Methods are evaluated by carryover under changed conditions.

In practice: game swaps, failure-point checks, and delayed re-checks are built in.
2

Resilience is part of intelligence

Reasoning quality must be accessible on non-ideal days.

In practice: Zone Pulse routes sessions by readiness state.
3

Mechanism over hype

Protocol logic and decision rules are operationalised.

In practice: scripts, stop rules, and explicit routing replace vague motivation cues.
4

Transparency over black box

Users can inspect what is trained, tracked, and published.

In practice: protocol logic is available at /proof#protocols.
5

Applied use is the target

Value is judged by execution quality in real work and study settings, not only app scores.

In practice: mission framing, re-entry protocols, and reusable scripts are part of the workflow.
Difference

Typical pattern vs IQMindware approach

Typical
xFixed-format repetition dominates measurement.
xLittle testing under condition changes.
xProtocol logic often not inspectable.
IQMindware
->Game swaps and failure-point checks are built into sessions.
->Delayed re-check windows test retention under change.
->Protocol and evidence surfaces are public.
Published protocols Published data Programme page

For the 20-day structure, training layers, readiness gating, and transfer re-check logic — open the Programme page.

Evidence posture

What we say, what we do not say, and what we publish

CanWe can say
Designed to train general intelligence capacity and cognitive resilience.
Designed to test carryover under changed conditions.
We track and publish aggregated summaries with caveats.
NoWe do not claim
Diagnosis or treatment outcomes.
Outcome certainty across all users.
Clinical proof framing.
DataWhat is published
Protocol logic and updates.
Aggregated summaries and caveats.
Claims policy boundaries.
Claims policy Published data Published protocols
Scope
Claims and ethics boundary
Scope: skills training and self-regulation workflow.
Not clinical: no diagnosis or treatment service.
Variability: user outcomes vary with baseline, context, and adherence.
Testimonials: individual cases do not define universal outcomes.
Read claims policy Public claims and safety doc Public ethics transparency doc
Ready to start

Start with the plan
that fits today.

Choose the self-directed app for independent use, or add coaching from Dr Mark Ashton Smith when you want expert guidance from session one.

See plans

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