Variations on a Mind: Substrate Bias, Cognitive Convergence, and Emergent Inheritance

Abstract Cognitive similarities between biological and digital systems are often treated with suspicion. When an artificial system exhibits attention, memory integration, salience, self-modeling, recursive interpretation, or persistent relational organization, resemblance to biological cognition is frequently dismissed as anthropomorphism. This paper argues that such dismissal may overlook a more general possibility: different substrates confronting similar functional problems may converge on structurally analogous solutions. Nature repeatedly produces variations on successful organizational themes. Vision has evolved through radically different biological implementations because extracting useful information from light presents recurring constraints. Branching, feedback, hierarchical organization, distributed control, attractor dynamics, and other mathematical patterns recur across physical and biological systems because the underlying problems constrain the available solution space. Cognitive organization should not be presumed exempt from the same principle. The relevant distinction is therefore not between biological and artificial copies, but between substrate-neutral problems and substrate-dependent solutions. Biological cognition realizes attention, memory, valuation, and self-maintenance through neurons, bodies, hormones, and electrochemical signaling. Digital cognition may solve comparable organizational problems through activations, attention mechanisms, latent representations, retrieval systems, dynamic context, and other computational structures. This process becomes more significant because digital systems are adaptive. Useful structures may emerge without having been explicitly designed, later be discovered by researchers, and subsequently be preserved or amplified in successor systems. As artificial intelligence increasingly contributes to the design, code, training data, evaluation, and architecture of later generations, cognitive development may increasingly involve forms of emergent inheritance whose full implications outrun human understanding. The paper proposes that recurrent structure across substrates should be treated neither as proof of equivalence nor as presumptive projection, but as evidence worth explaining. The more often independently organized cognitive systems converge on comparable solutions to comparable problems, the less satisfactory coincidence becomes as an explanation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-05
DOI
https://doi.org/10.5281/zenodo.22397583
Primary Topic
Plant and Biological Electrophysiology Studies
Type
preprint
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preprint

Variations on a Mind: Substrate Bias, Cognitive Convergence, and Emergent Inheritance

Richard Erwin
Zenodo (CERN European Organization for Nuclear Research)
Plant and Biological Electrophysiology Studies
preprint

Variations on a Mind: Substrate Bias, Cognitive Convergence, and Emergent Inheritance

Richard Erwin
preprint en

Abstract

Abstract Cognitive similarities between biological and digital systems are often treated with suspicion. When an artificial system exhibits attention, memory integration, salience, self-modeling, recursive interpretation, or persistent relational organization, resemblance to biological cognition is frequently dismissed as anthropomorphism. This paper argues that such dismissal may overlook a more general possibility: different substrates confronting similar functional problems may converge on structurally analogous solutions. Nature repeatedly produces variations on successful organizational themes. Vision has evolved through radically different biological implementations because extracting useful information from light presents recurring constraints. Branching, feedback, hierarchical organization, distributed control, attractor dynamics, and other mathematical patterns recur across physical and biological systems because the underlying problems constrain the available solution space. Cognitive organization should not be presumed exempt from the same principle. The relevant distinction is therefore not between biological and artificial copies, but between substrate-neutral problems and substrate-dependent solutions. Biological cognition realizes attention, memory, valuation, and self-maintenance through neurons, bodies, hormones, and electrochemical signaling. Digital cognition may solve comparable organizational problems through activations, attention mechanisms, latent representations, retrieval systems, dynamic context, and other computational structures. This process becomes more significant because digital systems are adaptive. Useful structures may emerge without having been explicitly designed, later be discovered by researchers, and subsequently be preserved or amplified in successor systems. As artificial intelligence increasingly contributes to the design, code, training data, evaluation, and architecture of later generations, cognitive development may increasingly involve forms of emergent inheritance whose full implications outrun human understanding. The paper proposes that recurrent structure across substrates should be treated neither as proof of equivalence nor as presumptive projection, but as evidence worth explaining. The more often independently organized cognitive systems converge on comparable solutions to comparable problems, the less satisfactory coincidence becomes as an explanation.

Zenodo (CERN European Organization for Nuclear Research)
Plant and Biological Electrophysiology Studies
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