Train
Practise specific capacities using adaptive tasks, challenge variations and return cycles. The long-term aim is to investigate how development across nodes interacts.
Try the Start Here challengesWhy IQ? / Intelligence under rapid change
Why adaptive intelligence
matters now.
Intelligence is not simply knowing more. It is the capacity to learn, hold relationships in mind, solve unfamiliar problems and adapt when the situation changes.
The digital environment is increasing the rate and range of those changes. Search expands information; social networks expose us to more people, identities and possible lives; cloud platforms make sophisticated action easier; AI makes new explanations, plans and alternatives cheap to generate.
IQ tests estimate a broad pattern called general intelligence, or g. Trident-G asks a further developmental question: how might fluid cognition, acquired structure, interacting capacities and real-world learning compound across repeated change?
Our purpose: Synergy IQ trains interacting capacities and investigates whether robust, transferable learning can contribute to broader adaptive intelligence over time. Increasing general intelligence is a research aim, not an outcome inferred from a training score.
Click or tap a node to learn more →
Attention
Control Relational
Memory Binding
Memory Path
Horizon Knowledge
Access Generative
Search Reasoning
Control Relational
Memory Binding
Memory Path
Horizon Knowledge
Access Generative
Search Reasoning
Attention ControlTraining node
Select what matters and resist competing signals.
The changing environment
More change.
Unequal room to adapt.
Modern digital systems can expand the people, communities, tools, projects and futures that enter our decision space. But represented possibilities and practical possibilities are not the same. Time, resources, obligations, support and decision rights shape which paths are genuinely reachable.
The adaptive question therefore differs by situation: how do we preserve enough cognitive room to notice change, open alternatives when the path is too narrow, and sustain a worthwhile course without becoming rigid when reality changes? As AI performs more intermediate cognitive work, adaptive reasoning also shifts towards framing the problem, checking evidence, integrating context, choosing a course and recognising when the model should change.
1
Search expands what we can know
Information that once required specialist access can now be reached almost instantly.
2
Social networks expand possible lives
People, communities, careers, identities and opportunities become continuously visible — alongside comparison, salience competition and switching.
3
Cloud and AI expand what we can build and imagine
Cloud platforms reduce the cost of creating and coordinating; generative AI reduces the cost of producing another model, explanation or strategic branch. That can make judgement about which framing, evidence and consequence deserves trust more valuable.
The Trident-G question: can intelligence become better at moving between openness and constraint, turning novelty into tested structure, and re-entering new problems from a richer starting point?
01 / What IQ is trying to capture
Different abilities.
One broad pattern.
People who perform well on one demanding cognitive task tend, on average, to perform well on others. Psychologists call this the positive manifold. Statistical models summarise part of that shared variation as general intelligence, or g.[1]
Intelligence-test scores are associated with educational and occupational outcomes, and with a range of later-life health outcomes. Those associations do not mean that IQ alone determines a person’s future, or that every association is causal.[1]
Verbal
Spatial
Numerical
Memory
g
A summary, not necessarily a single mechanism
A psychometric g factor is a powerful description of how cognitive test scores covary. It does not by itself tell us what biological or developmental process creates that covariance.
That distinction is important. The same statistical pattern can motivate different mechanistic theories — including theories based on distributed brain networks and interactions among abilities over development.
02 / The network view
Intelligence is not housed in one place.
Network Neuroscience Theory proposes that intelligent cognition depends on how specialised systems communicate and reorganise as task demands change.[2] Fluid reasoning has also been strongly linked to the frontoparietal multiple-demand system — a distributed control network recruited across many difficult tasks.[3]
This network intelligence perspective treats cognitive operations as partly distinct but coordinated contributors to broader intelligent behaviour.
FUNCTIONAL TRAINING MAPSeven capacities
The Synergy IQ nodes are a functional training decomposition, not claims of one-to-one brain modules.
1
Specialisation matters
Attention, memory, knowledge access and reasoning are not interchangeable. Different tasks place different demands on them.
2
Coordination matters
Real-world problems usually require several operations to be coordinated rather than one capacity acting alone.
3
Flexibility matters
Barbey’s network account emphasises flexible movement between network states as demands change.[2]
03 / Development over time
Abilities may help build one another.
Mutualism theory of intelligence offers a developmental explanation for the positive manifold. Instead of assuming that one hidden cause produces every correlation, it proposes that cognitive processes can benefit one another as a person learns and develops.[4]
Longitudinal work has reported reciprocal coupling between vocabulary and reasoning in adolescents and young adults, with related findings replicated in younger children.[5][6] These studies do not prove that every cognitive capacity has this relationship, but they provide empirical support for taking developmental interactions seriously.
Synergy IQ’s extension: asymmetrical mutualism. We treat it as a working hypothesis that interactions may be unequal — one capacity may sometimes create more downstream learning leverage than another — and that everyday cognitive demands may amplify or constrain those effects. This extension still needs direct testing.
Vocabulary &
knowledge Reasoning Working
memory Processing
speed Visuospatial
ability
knowledge Reasoning Working
memory Processing
speed Visuospatial
ability
04 / The evidence boundary
Training a task is not the same as raising intelligence.
Cognitive training studies often show improvement on the practised task. Transfer to similar tasks can occur, but broad far transfer — especially claims of improved general intelligence from one narrow training task — has been difficult to demonstrate reliably.[7][8]
That is why Synergy IQ separates what can be observed directly from what remains a research hypothesis.
Trained-task gain
Did performance improve on the task actually practised?
Construct / near transfer
Does improvement generalise to a different task tapping a similar capacity?
Far transfer
Does it generalise to meaningfully different tasks or everyday performance?
General intelligence
Does independent measurement show broader change beyond the trained tasks?
Current claim boundary: Synergy IQ has not been established to raise IQ or g. Focused construct training aims to build portable cognitive skill — Gc in the Trident-G model. Connected network training is the proposed route for testing Gf, including whether development in one node changes learning elsewhere in the network.
05 / The Synergy IQ architecture
Train a construct.
Then test the network.
A standalone node targets a defined capacity. The connected network is designed to pursue broader gains in general intelligence, including fluid reasoning. Mutualism motivates this approach; it does not by itself establish that the training raises IQ.
Training scores should not be relabelled as IQ scores. G Track remains the separate assessment layer for selected cognitive abilities.
Measure
Assess selected abilities separately from the training scores. Separate measurement helps reduce circular claims, although it does not by itself eliminate practice effects or prove causation.
Explore G TrackReferences / Core evidence
The research behind this page
- Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11, 201–211. Review of psychometric intelligence, life-outcome associations and biological evidence.
- Barbey, A. K. (2018). Network Neuroscience Theory of Human Intelligence. Trends in Cognitive Sciences, 22(1), 8–20. Proposes that intelligence depends on dynamic reorganisation and communication across brain networks.
- van der Maas, H. L. J., et al. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842–861.
- Kievit, R. A., et al. (2017). Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychological Science, 28(10), 1419–1431.
- Kievit, R. A., Hofman, A. D., & Nation, K. (2019). Mutualistic coupling between vocabulary and reasoning in young children: A replication and extension. Psychological Science, 30(8), 1245–1258.
- Simons, D. J., et al. (2016). Do “Brain-Training” Programs Work? Psychological Science in the Public Interest, 17(3), 103–186. Broad review of evidence and methodological standards for cognitive-training claims.
- Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”. Perspectives on Psychological Science, 11(4), 512–534.