HRP Lab
The research and development company behind the scientific programme, validation work and intellectual property.
About IQ Mindware
Cognitive support built for minds working under demand.
IQ Mindware turns cognitive science into protocol-led support for demanding work, study and AI-assisted workflows—with progress checked beyond the exercise.
Founder and scientific lead
Dr Mark Ashton Smith
Cognitive neuroscientist, researcher, educator and cognitive coach.
Mark trained at the Center for the Neural Basis of Cognition, a joint programme between the University of Pittsburgh and Carnegie Mellon University.
He was a lecturer and researcher in the Department of Psychology at the University of Cambridge and later a Lecturer in Cognitive Psychology and Neuroscience at the University of Essex Online.
For two decades, his work has focused on a central question: can cognitive training produce gains that extend beyond practice on one familiar task?
How the pieces fit
Research, products and one question: does progress survive change?
IQ Mindware
The customer-facing apps, workflow tools, coaching and focused organisational pilots.
Trident-G
The theory and transfer-testing approach that asks whether a skill survives changes in task, level, context and time.
Scientific principles
Scientific discipline without inflated claims.
- Measure progress separately from training
- Change task formats instead of relying on repetition alone
- Check again after a delay
- Keep current products separate from the roadmap
- Test real-world outcomes rather than assuming them
- Maintain clear non-clinical and privacy boundaries
SCIENTIFIC PROGRAMMEExplore APC, H-AGI and the HRP Transfer Lab
Adaptive Predictive Control (APC) is a developing framework for how cognition selects information, updates from evidence and decides when to act. H-AGI explores human-owned, AI-supported reasoning. The HRP Transfer Lab is the planned AI-augmented research capability for developing and testing interventions.
These frameworks guide research and development. They should not be read as established proof for the current commercial apps.
Explore research and methods →Start with the products available now.
Find what can be measured or trained now—and how it connects to the task that matters.