How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
Alex Anvi Eponon, Ildar Batyrshin, Christian E. Maldonado-Sifuentes, Grigori Sidorov

TL;DR
This paper argues that the limitations in AI's systematic reasoning stem from psychological learning theories' influence on architecture, proposing ReSynth as a new framework to embed systematicity structurally.
Contribution
It traces the influence of psychological learning paradigms on AI architecture and introduces ReSynth, a framework separating reasoning, identity, and memory to enhance systematicity.
Findings
Current AI struggles with systematic compositional reasoning.
Existing correction techniques address symptoms without fixing architecture.
ReSynth offers a structural solution by separating reasoning, identity, and memory.
Abstract
Current artificial intelligence systems struggle with systematic compositional reasoning: the capacity to recombine known components in novel configurations. This paper argues that the failure is architectural, not merely a matter of scale or training data, and that its origins lie in the psychological learning theories from which AI paradigms were derived. The argument proceeds in three stages. First, drawing on the systematicity debate in cognitive science and on the demonstration of Aizawa that neither connectionism nor classicism can make systematicity a structural consequence of the architecture, the paper establishes that the corrective techniques proliferating in modern AI, from chain-of-thought prompting to alignment through human feedback, function as auxiliary hypotheses that address symptoms without resolving the underlying architectural indifference to systematicity. Second,…
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