How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
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 ch...
| Authors | Alex Anvi Eponon, Ildar Batyrshin, Christian E. Maldonado-Sifuentes et al. |
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| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 13 April 2026 |
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