Practise a capacity
Use a Synergy IQ node when you want repeated, adaptive practice on a defined cognitive operation.
See the training route →How it works / From training tasks to adaptive intelligence
Train a cognitive capacity.
Test how far it transfers.
Traditional brain training asks whether practice improves a cognitive task and whether that gain transfers. IQ Mindware keeps that evidence problem — but places it inside a larger one: how do people preserve enough headroom to notice change, build or compare workable alternatives, act in reality and learn what survives?
Near transfer first. Applied transfer next. Far transfer tested, never assumed.
From brain training to adaptive intelligence
Same scientific discipline.
A bigger adaptive question.
Traditional cognitive training remains important: train a defined operation and test whether improvement survives outside the exercise. The new paradigm adds the wider cognitive niche — social platforms, cloud tools, automation, AI and real-world action — and asks whether learning helps the person adapt, commit and keep learning as conditions change.
| Traditional brain-training paradigm | Adaptive-intelligence paradigm |
|---|---|
| Train a cognitive task. | Train interacting cognitive capacities. |
| Improve performance in the exercise. | Test whether structure survives changed tasks, contexts and delay. |
| The individual brain is the main locus of intervention. | Cognition is situated in a person interacting with tools, people and a digital/social niche. |
| Practice tends to occur in a relatively stable task environment. | Learning must cope with rapidly changing, possibility-rich environments. |
| More practice can produce greater task fluency. | Open possibilities, constrain them, commit, act, learn and reopen when evidence warrants it. |
| Transfer is checked after training. | Wrapper, layer, niche and delayed transfer are designed into the learning architecture. |
| Outcome is mainly a training or test score. | Keep task gain, AI-supported performance, retained human reasoning and capability, embedded action and state change separate. |
| Technology is mainly a delivery tool or distraction. | Search, social media, cloud platforms and AI actively reshape the cognitive niche. |
| Intelligence is sampled at a point in time. | Intelligence is also studied as repeated Gf → Gc → Gf adaptation over time. |
| Training ends at performance. | A full learning loop crosses the Reality Boundary: action → outcome → prediction error → banking. |
Traditional brain training
TRAIN A TASK↓REPEAT↓GET BETTER↓SCORE↓ASK WHETHER IT TRANSFERS
Adaptive intelligence
DEVELOP CAPACITIES↓OPEN + CONSTRAIN↓COMMIT↓CROSS THE REALITY BOUNDARY↓LEARN + BANK↓RE-ENTER A NEW PROBLEM
The new paradigm does not bypass the transfer problem. It makes the evidence burden larger: better game performance is only the first step. Broader claims require independent tests, real-world outcomes and delayed re-checks.
The shared learning rhythm
Regulate. Explore. Act.
Let reality answer.
Adaptive intelligence includes knowing what the situation needs now: recovery or external support when headroom is low; exploration when the path is too narrow; selection when several options are viable; and revision when feedback shows that the current course no longer fits. The G-loop is the application method; a dedicated software module is coming later.
OPEN→CONSTRAIN→TEST→COMMIT→ACT→LEARN→BANK
Digital tools can widen search, but they can also add demands to lives that are already crowded. The aim is not maximum possibility or maximum persistence. It is to regulate attention, search and action so that useful learning can occur under the conditions the person actually faces.
Choose the next useful loop
What are you trying to do?
The same difficulty does not always call for the same response. Choose the route that matches your current question; you can move between them later.
Profile adaptation and re-check
Use G Track for repeatable Adaptive G profiles — stability, learning, path control and reality coupling — alongside separate cognitive benchmarks such as reasoning, attention and working memory.
Explore G Track →Work on a real challenge
Use cognitive coaching when the problem involves your actual work, study, decision or AI workflow — including what to reason through yourself, what to do with AI and what can safely be delegated.
Explore cognitive coaching →Want the evidence first?Read the Science page for the transfer ladder, current evidence and the boundary between the programme's design aims and what has actually been demonstrated.Inspect the evidence →
01 / Focused construct training
One node is a complete training route.
You do not need the full network for a node to be worthwhile. A node begins with a specific cognitive operation, adds adaptive demand and variation, then checks whether the gain survives outside the exact exercise.
Target the construct
Practise a defined capacity such as attention control, working-memory updating, binding or relational reasoning.
Change the surface
Alter content, modality, timing and interference so progress is less likely to depend on one familiar display.
Use an untrained measure
Check a different task intended to sample the same capacity, then return after a delay.
Near / construct transferImprovement on an untrained task that draws on the same underlying capacity. This is a stronger and more credible claim than broad transfer, but it still has to be demonstrated rather than inferred from a rising training score.
02 / Capacity in real life
What could become easier?
These are practical problems the capacities may support. They are application targets, not guaranteed outcomes: workplace and study benefits require niche-transfer evidence beyond near transfer. The optional AI exercises below are in development.
Attention Control
Train: selecting relevant signals and resisting interference.
May help with: staying on the important thread in a noisy meeting, dashboard or document.
With AI: Find the signal. AI suggests what matters in a mixed information stream. Check its choices against your goal and spot what it missed.
Explore this node →Relational Memory
Train: holding and updating relationships.
May help with: tracking how assumptions, people or variables change across a complex project.
With AI: Keep the whole problem in view. Build an editable map with AI, check how the parts connect and update it when a constraint changes.
Explore this node →Binding Memory
Train: keeping features, roles and sources attached correctly.
May help with: avoiding source, owner or detail mix-ups when several cases are active.
With AI: Keep facts attached to their sources. Use AI to organise claims and supporting passages, then check that each source actually supports its claim.
Explore this node →Path Horizon
Train: representing sequences and possible next states.
May help with: planning dependencies and noticing where a decision path may lead.
With AI: Explore what happens next. Compare AI-proposed paths with your own prediction. Identify constraints and decide what would make you change course.
Explore this node →Knowledge Access
Train: retrieving the relevant concept, rule or precedent.
May help with: bringing the right knowledge to mind when time or complexity creates pressure.
With AI: Recall before reveal. Try recalling first, then use progressively stronger AI hints linked to approved learning material.
Explore this node →Generative Search
Train: producing and exploring alternatives.
May help with: escaping the first plausible answer and generating more options.
With AI: Go beyond the first answer. Offer your own possibilities before AI adds alternatives, analogies and counterexamples. Judge which are useful.
Explore this node →Reasoning
Train: combining relations and evidence to infer what follows.
May help with: testing conclusions, constraints and competing explanations.
With AI: Put your conclusion to the test. Ask AI to challenge assumptions and offer counterarguments. Check its evidence and decide what would change your mind.
Explore this node →03 / Applied transfer
Capacity → Strategy → Niche.
A trained operation is more useful when a person can recognise when it matters, recover a usable strategy, and deploy it in the real setting with its own cues and constraints.
- CAPACITYCan the underlying operation be performed under controlled demand?
- STRATEGYCan the learner name and deliberately recover a policy for using it?
- NICHECan that policy be used in study, work, decision-making or human–AI collaboration?
Trident-G theory
Learn a method. Meet a new challenge.
Fluid intelligence (Gf) concerns reasoning through unfamiliar problems; crystallised intelligence (Gc) concerns acquired knowledge and skills. General intelligence (g) describes what is shared across cognitive abilities. The seven nodes are training targets, not seven forms of Gc.
The Trident-G proposal asks whether useful learned methods support subsequent learning and problem solving. Mutualism suggests that capacities can influence one another’s development. Whether this training produces that effect, and whether some capacities contribute more strongly, are questions to test. AI-assisted performance is a separate outcome.
This is a theory-led intervention architecture. It does not establish that Synergy IQ raises Gf or general intelligence; that requires independent measurement and controlled longitudinal evidence.
04 / Test the distance
How far did the learning travel?
The further a claim moves from the trained task, the more independent the test and the stronger the study design must be.
- 01
Task learning
Better performance on the trained exercise.
- 02
Wrapper robustness
The operation survives changed content, form or modality.
- 03
Near / construct transfer
An untrained task sampling the same capacity improves.
- 04
Applied / niche transfer
The capacity and strategy are recovered in a real problem context.
- 05
Network / far transfer
Other capacities, fluid problem solving or broader adaptive outcomes change.
Choose your route
Start focused. Connect later.
Choose one node for the capacity that matters now. Add nodes if you want to test the network hypothesis over time. Keep measurement separate with G Track.
Application method · Dedicated software coming later
g loop — Cross the reality boundary.
The G-loop is the reality-anchoring method now: make a prediction, choose a proportionate action, observe what actually happens and update. A dedicated guided software space connecting the seven nodes is coming later.
Find the real sticking point
Start with your goal and conditions. Is the difficulty a skill, a strategy, the environment, the tool or a combination? Choose a manageable experiment.
Choose how to work
Make a prediction. Decide what to do yourself, what AI could help with and what needs checking. Use relevant node practice when it fits.
Keep what travels
Review the result, revise the method and try it on a new task. Check later what you understand and can do without assistance.
This connects the seven nodes; it is not an eighth cognitive capacity. Its structure draws on SPACE–PACE–HAGI. The software is in development; personal coaching is the current route for working through a real challenge together.
Track assisted output, retained understanding, independent performance and everyday results separately. Emotional task conditions may support experiments in handling interference; broad resilience and intelligence gains require their own evidence.