The Nervous System in Pedro Ortiz Cabanillas’s Sociobiological Informational Theory: An Epistemological Alternative to Classical Neuroanatomy
Hans Contreras-Pulache, Diego Carrera-Hidalgo, Ana Astorga-Cota, Andrea Mija-Reyna, Eduardo Espinoza-Lecca, Nelly Lam-Figueroa
Human Arenas May 17, 2026 DOI: 10.1007/s42087-026-00610-5 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Qualitative Peer reviewed |
|---|---|
| Keywords | Sociobiology Counterpoint Context archaeology Neuroanatomy Consciousness Embodied cognition Determinism Epistemology Cognitive science Psychosocial Mainstream |
| Key points | Argues that Pedro Ortiz Cabanillas's model integrates emotional, productive, and volitional components across five hierarchical levels, portraying the nervous system as a dynamic unit where biological and psychosocial processes converge. |
Abstract
This article examines Pedro Ortiz Cabanillas’s original model of nervous system organization, as presented in the second edition of Introducción a una Psicobiología del Hombre (2010). The framework integrates three core components —emotional, productive, and volitional— across five hierarchical levels (cellular, tissue, neural, paleocortical, and neocortical), which in turn span biological, psychological, symbolic, and social dimensions. Employing a qualitative theoretical-bibliographic methodology, the analysis draws on primary sources and unpublished materials from the Pedro Ortiz Cabanillas Research Documentation Center. By reconstructing these conceptual foundations, the study highlights the model’s portrayal of the nervous system as a dynamic and integrative unit in which biological and psychosocial processes converge. Developed within Peru’s peripheral intellectual context under challenging socio-political conditions, Ortiz’s sociobiological informational theory offers a relevant counterpoint to mainstream neuroscience and contributes to the diversification of epistemological perspectives on human complexity.