Measure state. Train control. Test transfer.
Train for transfer, not just practice.
The Trident G protocol is Synergy IQ's method for training attention, working memory and reasoning under changing demands. It tests far transfer rather than assuming that practice gains will generalise beyond one familiar task.
The simple idea is this:
Improvement matters most when it becomes reusable.
You can get better at one exercise because its timing, layout and rules become familiar. That may be useful, but it does not automatically mean the same thinking skill will carry into a new problem.
The Trident G Far Transfer Protocol™ is designed around a stronger question:
Can the same useful structure survive change?
That means training is not only about repetition. It is about learning, adapting, recovering and applying.
Change the surface. Keep the core challenge.
The task format changes while the underlying thinking demand remains similar.
A user may first learn a pattern, rule or relation in one format, then meet the same kind of challenge in a new surface: a different layout, wrapper, task type or problem form.
The question is:Can the same relation or strategy be recovered when the surface changes?
Move from control to reasoning.
The protocol also increases the depth of the challenge.
Training moves through a simple vertical route:
readiness → attention → working memory → reasoning → action
The aim is not just to make one task faster. The aim is to build the active mental workspace needed for clearer problem solving: holding relevant information, resisting distraction, tracking relations and deciding what follows.
The question is:Can lower-level control support higher-level thinking?
Check what still holds later.
A score can rise during practice and still fade when the format changes or the skill is revisited later.
That is why Synergy IQ separates daily training from progress checks. Re-checks ask whether progress still holds when familiar supports are reduced, conditions vary or the challenge is revisited after delay.
The question is:Does the gain survive change, delay or practical re-use?
Repeating a task can make its cues, timing and response pattern more familiar. That can improve performance, but it does not automatically show that the underlying thinking skill has become portable.
A score can rise inside one exercise while staying tied to its exact format.
Practice can make a task feel easier without requiring the learner to recover the same relation or strategy in a new form.
A gain may weaken when the format changes, the reasoning demand increases or the skill is revisited later.
The Trident G Far Transfer Protocol™ is a design response to this problem. It builds change, progression and re-checking into the training pathway.
The public route separates focused construct training from the broader network hypothesis.
Choose a defined capacity such as attention control, relational memory, binding or reasoning. Adaptive practice develops the target operation.
Change content, modality, interference and task surface so improvement is less likely to depend on one familiar routine.
Use an untrained task intended to sample the same construct, followed by delayed re-checks. This is the focused Gc route.
Recombine portable capacities under novel demands and test cross-node learning and fluid problem solving. This is the proposed Gf route.
In simple terms:
build portable cognitive skill first; then test what the connected network can do.
Each step asks a harder question and needs more independent evidence.
Focused construct training and connected network training play different roles.
So the aim is not to bank fluency with one game.
The aim is to bank portable structure.
That is the difference between getting better at one task, building a transferable construct-level skill, and testing whether a connected system improves fluid intelligence.
Boundary: the Gf effect is a hypothesis to test, not a promised outcome.
Read the Trident G theory preprint by Mark Ashton Smith (2025)
Daily training shows what was practised. Progress checks ask a different question: whether performance changes when familiar supports are removed, conditions vary or the skill is revisited later.
The evidence layer is kept separate on purpose. Stronger claims require more than engagement or rising in-app scores.
Read the proof and claims boundary →The page above gives the simple version. The full formal protocol explains the underlying theory, transfer logic, bottleneck model, delayed consolidation framework and validation boundary in more detail.
Read the formal protocol on GitHub →