Author

A. Jakovác

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#artificial intelligence Preprint Oct 2026

A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space

Learning and problem solving depend critically on the structure of internal representations. While many modern data-driven artificial systems achieve strong predictive performance, their learned representations often lack explicit structure for expressing abstraction, invariance, and task-relevant regularities. We prop...

A. Jakovác, András Telcs · 1 citation

#artificial intelligence Preprint Oct 2026

Cognitive Schemas, Laws and Tasks

This paper asks how explicit representations can support reusable cognitive schemas in knowledge-based problem solving. We develop a structural framework in which schemas are organized by the information and relations required for their use, rather than introduced as unrelated primitives. The framework also distinguish...

A. Jakovác, András Telcs · 0 citations

Open access Aug 2026

Causality beyond the Reichenbach principle: the degrees-of-freedom method

Causal relationships underpin scientific reasoning across disciplines, yet reliable inference from observational data remains challenging. In many fields, from climate science to neuroscience, direct interventions are infeasible, and distinguishing genuine causality from correlations induced by hidden drivers is noto...

A. Jakovác, Dániel Fabó, L. Erőss et al. · Scientific Reports · 0 citations