The illusion of internal models in biological movement.

Summary

Internal models—computational representations supposedly stored in the nervous system—dominate current theories of how animals control movement. This perspective argues that such representational approaches fundamentally mischaracterize biological systems for six reasons: they require an infinite regress of internal interpreters; decades of research have failed to find neural evidence for them; biological movement is nonlinear and multiscale, not reducible to conventional computations; they implicitly rely on Cartesian dualism by separating a controller from what is controlled; the framework is circular and unfalsifiable; and alternative ecological dynamics and self-organization explain adaptive behavior without representations. Sensorimotor control instead emerges from dynamic coupling between organism and environment across multiple scales.

Study at a glance

Abstract

The concept of internal models dominates contemporary theories of sensorimotor control, with researchers across neurosciences, specifically motor control, routinely explaining observed behaviors through computational representations that supposedly exist within the nervous system. In this perspective, I present a critical examination of internal model frameworks in sensorimotor control. I argue that representational approaches mischaracterize biological systems for several fundamental reasons: (1) Internal models require homuncular interpreters, creating infinite regress problems; (2) The purported neural implementations of internal models remain empirically elusive despite decades of research; (3) Biological movement systems exhibit multiscale, nonlinear, and non-Gaussian dynamics that fundamentally defy reduction to conventional computational representations; (4) Internal model frameworks implicitly depend on Cartesian dualism through their separation of the "controller" and "controlled;" (5) The framework is methodologically circular and largely unfalsifiable as virtually any behavior can be retroactively modeled as implementing some internal representation; and (6) Alternative frameworks based on ecological dynamics and self-organization can account for adaptive behavior without invoking representational assumptions. Instead of representational models, I propose that sensorimotor control emerges from the dynamic coupling between the organism and the environment across multiple spatial and temporal scales. By moving beyond the internal model paradigm, sensorimotor neuroscience can develop more powerful explanatory frameworks that better capture the emergent, context-sensitive properties of biological movement without invoking physiologically intractable computational metaphors.