Understanding the cognitive architecture of mathematical learning and its disruption represents a fundamental challenge in developmental science. Mathematical Learning Difficulties (MLD), with a ~10% prevalence, impose severe individual and societal costs, impacting economic growth. Rising standards could dramatically enhance economic performance in a 0.74% increase in real GDP growth. The developmental trajectory of core number-sense remains poorly mapped at the cognitive level. This project addresses this gap through an ambitious, methodologically pioneering program. We will:

1) deconstruct the dynamic cognitive network functions underpinning early numeracy using high-precision cognitive assays, and 

2) design, implement, and validate a novel, theory-driven early intervention (ages 3–8) based on the transformative "Open Algorithm Based on Numbers" (ABN) method. 

ABN instigates a profound conceptual shift by grounding arithmetic in quantity manipulation rather than symbolic cipher procedures. We hypothesize that MLD risk stems from a maladaptive configuration of domain-general executive functions, which the ABN approach can functionally rewire to scaffold a robust cognitive architecture for number sense. By integrating advanced cognitive science with rigorous educational design, this research will unveil the specific and general cognitive mechanisms constraining math acquisition. COGNIMATH will link cognitive mechanisms to classroom practice by combining fine-grained assessment with a theory-driven ABN intervention and a longitudinal evaluation. The project will deliver a mechanistic model of early numeracy and a scalable, classroom-ready toolset to reduce the proportion of children who remain at risk of MLD.

If you are interested in applying for MSCA-PF, please submit by email to jose.navarro@uca.esthe following documents:

1. CV (including complete contact details and list of publications, projects, etc., and describing academic achievements and research expertise, focusing on those aspects relevant in the context of the project).

2. Motivation letter (including information concerning your research and career interests and goals, project idea aligned with our research lines and how the candidate will contribute with his/her expertise in the project, and your interests in this call).