Recent attempts to merge predictive processing with phenomenology underscore the embodied basis of inference but leave unexplained how inherited meanings shape priors that guide thought and action. This article introduces Predictive Hermeneutics (PH), a framework that treats Bayesian priors as interpretive schemas layered through foundational constraints (arkhai), narrative identity, hermeneutic horizons, and metaphorical instantiations. Bias, on this account, is not peripheral error but the str…
Read moreRecent attempts to merge predictive processing with phenomenology underscore the embodied basis of inference but leave unexplained how inherited meanings shape priors that guide thought and action. This article introduces Predictive Hermeneutics (PH), a framework that treats Bayesian priors as interpretive schemas layered through foundational constraints (arkhai), narrative identity, hermeneutic horizons, and metaphorical instantiations. Bias, on this account, is not peripheral error but the structural mark of these schemas at work. To critically evaluate them, PH develops a Comparative Hermeneutic Audit that subjects interpretive commitments to the criteria of coherence, flexibility, and commensurability. Drawing on evidence from cross-cultural psychology, metaphor studies, and cognitive neuroscience, PH demonstrates how culturally embedded priors organize salience and inference in both human cognition and artificial systems. Applied to large language models, this approach demonstrates why statistical tuning alone cannot resolve bias or hallucination without addressing the figurative and narrative scaffolds that shape predictive horizons. By surfacing these deep interpretive structures, Predictive Hermeneutics reframes bias as the constitutive trace of predictive minds and provides a principled method for analyzing how deep interpretive structures shape reasoning in humans and machines.
