I investigate an impact of gamified Working Memory training on transfer of learning and other higher order cognitive constructs. 

• Research interest: 

  • Cognitive rehabilitation of people with affected brain function due to:

    – brain injury: traumatic brain injury, stroke

    – neurodegenerative disorders: multiple sclerosis, dementia,

    – neurodevelopmental conditions: ADHD, schizophrenia

    – psychological trauma

  • Psychiatric and neurodegenerative disorders in neuroimaging research

  • Assessment of cognition, emotion and behaviour.

  • Understanding biopsychosocial factors impacting an individual’s everyday life cognitive abilities.

Core Architecture

NeuxScience® platform combining:

• Frontend: React/TypeScript with Pixie 2D engine for declarative experiment construction
• Backend: Azure Cloud with Kubernetes orchestration
• Storage: Neo4j graph database

Key Features

Data Capture & Analysis

• Knowledge graph stores all experiment entities and interactions
• Comprehensive tracking of actions, durations, and movements
• Flexible data analysis through graph relationships

The NeuxScience® platform represents an approach to cognitive testing that leverages modern cloud-native architecture and advanced data modeling. At its core, the platform utilizes React with TypeScript for the frontend, enhanced by the Pixie 2D engine that enables declarative experiment construction through an intuitive interface. The backend infrastructure is built on Azure Cloud, with Kubernetes handling container orchestration to ensure scalability and resilient service delivery. A distinguishing feature of the architecture is its use of Neo4j as the primary database, implementing a knowledge graph model that captures complex relationships between experimental entities, participant interactions, and temporal data. This graph-based approach enables comprehensive tracking of participant actions, response durations, and movement patterns, while providing flexible querying capabilities through graph traversal. The platform’s architecture is specifically designed to support rigorous cognitive research by maintaining high-fidelity data capture while offering sophisticated analysis capabilities through its interconnected data model.

Core Architecture

Cloud Infrastructure
The Azure Cloud foundation provides robust scaling capabilities and global availability, with Kubernetes orchestrating containerized services for optimal resource utilization and service reliability.

Frontend Framework
The React/TypeScript combination delivers a type-safe, component-based interface, while the Pixie 2D engine enables sophisticated visual experiment design through declarative programming patterns.

Data Architecture
The Neo4j graph database serves as the cornerstone of the platform’s data architecture, storing and connecting all experimental components, participant interactions, and behavioral metrics in a flexible, relationship-driven model that facilitates complex data analysis and pattern discovery.

PhD in Computational Cognitive Neuroscience and Psychology, Birkbeck University of London

Machine Learning in Science (Level 7), University of Nottingham

Computational Cognitive Psychology (Level 7), University of Nottingham

Neural Computation (Level 7), University of Nottingham

Practical Biomedical Modelling (Level 7), University of Nottingham

BSc Psychology and Neuroscience (with Honours), Birkbeck University of London

Cognitive Modelling (Level 6), Birkbeck University of London

R for Reproducible Psychological Research (Level 6), Birkbeck, University of London

Certifications: 52 CPD, CME and CE courses in Clinical Psychology, Neuroscience and Research Methods organised by: American Psychological Association (APA), Oxford University, The Stanford University School of Medicine, Yale University, King’s College London, among others.

Styk, D., & Davelaar, E. J. (2024, June 25-27). New approach in Working Memory Training [Poster presentation]. The 4th International Conference of Working Memory, Leeds, England, United Kingdom. DOI: 10.13140/RG.2.2.19565.60644

Styk D., Przygody małej Ronki, Wydawnictwo Rona, Kraków, 2011. (Good Reads, Jagiellońska Biblioteka Cyfrowa)

Associate lecturer in Psychology L4 – L7, Anglia Ruskin University, (Cambridge, England)

L4 Self and Society 2021/22, 2022/23
L4 Clinical and Health Psychology 2021/22, 2022/23
L5 Social Psychology, Development and Individual Differences 2021/22, 2022/23
L6 Forensic Psychology 2022/23
L7 Topics in Clinical Psychology 2021/22; 2022/23
L7 Issues in Scientific Research 2022/23
L7 Research in Action: Qualitative Methods and Psychology in Practice 2022/23

Researcher in Cognitive Neuropsychology at Anglia Ruskin University and member of the Cognition Group which forms part of the ARU Centre for Mind and Behaviour (Cambridge, England)

Research Associate in Dynamic Memory and Cognition Laboratory at Birkbeck, University of London, Neurofeedback project.

British Psychological Society (since 2014) The Division of Academics, Researchers and Teachers in Psychology (DARTP), The Cognitive Psychology section (since 2024)

British Neuroscience Association (since 2024)

American Psychological Association (since 2015)

Canadian Psychological Association (since 2017)

Experimental Psychology Society (since 2025)

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