The Seligman Proof: A First Principle for AI-Mediated Appraisal

The Seligman Proof: A First Principle for AI-Mediated Appraisal

Sean Paul Abrahams¹*

¹ Centre for African Positive Psychology (CAP), Cape Town, South Africa

* Corresponding author:

Sean Paul Abrahams

Email: Sean.Abrahams@uct.ac.za

Abstract

Positive psychology has generated a rich and empirically grounded body of theory addressing strengths, meaning, agency, and flourishing. Yet despite this progress, the field has lacked a clearly articulated first principle capable of unifying its diverse mechanisms at the level of cognitive process. This absence has become increasingly consequential as human appraisal is no longer exclusively human-directed but is now routinely co-constructed with artificial intelligence. Large language models increasingly participate in narrative reconstruction, meaning-making, and future-oriented reasoning, raising urgent questions about how flourishing-aligned appraisal should be identified, evaluated, and safeguarded in hybrid human–AI cognition.

This paper proposes the Seligman Proof as a candidate first principle for AI-mediated appraisal. Drawing on an observation articulated by Martin Seligman, the Proof formalises a minimal, process-level criterion: flourishing-aligned appraisal is characterised by movement toward increasingly reality-aligned, evidence-consistent, and future-viable interpretation. Rather than redefining flourishing or privileging any single wellbeing model, the Seligman Proof functions as a directional constraint on appraisal itself, specifying the cognitive condition under which diverse positive psychology interventions exert their effects.

The paper situates this principle within established criteria for foundational theory-building in psychological science, outlines criteria for evaluating first principles in psychological contexts, and demonstrates how the Seligman Proof unifies established findings across explanatory style, hope, prospection, meaning-making, and self-regulation. It further examines the implications of AI-mediated appraisal, introducing the Distortion-Augmented Reality Knowing (DARK) hypothesis as a predictable failure-mode when generative systems preserve coherence with distorted interpretive frames. By contrast, the Proof provides a foundation for Flourishing-Augmented Reasoning (FAR), specifying the conditions under which AI-supported appraisal can broaden interpretation rather than entrench constraint.

Together, these arguments position the Seligman Proof as a parsimonious, culturally neutral, and empirically tractable first principle capable of anchoring positive psychology in an era of hybrid human–AI cognition.