Grimmer, Daniel (2026) Evolutionary Meta-Learning in Neural Networks as a Neutral Testing Ground for Nativism and Empiricism. [Preprint]

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Abstract

The recent engineering success of neural networks is often seen as favoring Empiricism over Nativism. Indeed, Empiricist accounts of epistemology (i.e., tabula rasa initialization followed by general-purpose learning from massive amounts of sensory data) align closely with the standard way of training neural networks. Crucially, however, this training regime imposes a broadly Empiricist epistemology by engineering fiat, rather than fairly testing it against a Nativist alternative. We argue that this issue can be remedied with a change of training regime; our Evolutionary Meta-Learning framework allows one to use neural networks to efficiently simulate the evolutionary pressures that shaped the human mind. We present a first-principles derivation showing that the dynamics of a Darwinian Lineage Simulation (DLS) are formally equivalent to a noisy Stochastic Gradient Ascent (SGA) up a log-fitness landscape. Furthermore, we demonstrate that the Baldwin Effect—modeled here as an evolutionary pressure towards rapid learning so as to minimize childhood mortality—is well captured by the MAML++ meta-objective function. This framework offers a neutral testing ground for Nativist and Empiricist adaptation strategies while also aligning both approaches with Sutton's Bitter Lesson. Without hand-coding or brute stipulation, adaptations are chosen by the evolutionary process itself as it navigates its way up an ever-changing fitness landscape.

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Item Type:	Preprint
Creators:	Creators Email ORCID Grimmer, Daniel daniel.grimmer@yale.edu 0000-0002-8449-3775
Keywords:	Nativism vs Empiricism; Evolutionary Epistemology; Artificial Neural Networks; Philosophy of Cognitive Science; Philosophy of Artificial Intelligence;
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Cognitive Science Specific Sciences > Artificial Intelligence General Issues > Computer Simulation
Depositing User:	Dr. Daniel Grimmer
Date Deposited:	27 Feb 2026 21:21
Last Modified:	27 Feb 2026 21:21
Item ID:	28373
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Cognitive Science Specific Sciences > Artificial Intelligence General Issues > Computer Simulation
Date:	26 February 2026
URI:	https://philsci-archive.pitt.edu/id/eprint/28373

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