Binding Phoneme Identity to Position in Neural Models and the Human Brain

A fundamental challenge in cognitive science and neural computation is the binding problem: how neural systems associate separately represented contents with their structural roles. Compositional generalization can be achieved by factorizing representations into roles, the structural slots of a representation (e.g., serial position or syntactic function), and fillers, the contents that occupy them (e.g., a phoneme or a word). Because roles and fillers can be recombined freely, a system that represents them separately can represent novel combinations. How neural systems implement role–filler binding, however, remains largely unknown. One of its simplest linguistic cases is phoneme sequencing, where phoneme identity (the filler) must be bound to position (the role) so that /gum/ and /mug/ are represented differently. While previous work identified the neural encoding of single phonemes in human auditory cortex, it remains unclear how these encodings are combined into sequences. A recent proposal from natural language processing, the “onion hypothesis” (Csordás et al., 2024), offers a concrete account: the direction of the neural population state encodes token identity, while its magnitude encodes token position. However, the onion hypothesis was formulated for random token sequences; here we extend it to natural language, where successive tokens are strongly constrained, and test it in both neural models and the human brain. We first evaluated the onion hypothesis in an encoder–decoder trained to repeat English words, combining representational geometry analyses with causal interventions. Magnitude alone turns out to be incomplete: position is encoded by magnitude together with a systematic rotation of the state direction, a scheme we formalize as the “spiral hypothesis.” We then analyzed intracranial recordings from the auditory cortex of 20 participants listening to short spoken sentences. Across superior temporal gyrus electrodes, the change in population response between consecutive phonemes of a word decreases in magnitude as position advances, while the direction of that change is positively aligned within consonants and within vowels and close to orthogonal across the two classes. This supports the magnitude coding shared by both the onion and spiral accounts. Together, these results provide a geometric account of how neural systems bind phoneme identity to position, and show how hypotheses derived from neural models can yield testable predictions about human neural computation.