Bicultural populations offer a valuable natural experiment that could illuminate how constitution operates. Children of non-Western immigrants raised in Western contexts experience developmental exposure to both temporal systems. If constitution is correct, these individuals might show hybrid implicit signatures: neural responses that resemble neither of the two purely but may reflect the specific mixture of their developmental input. Alternatively, one system might dominate depending on timing or intensity of exposure, which would reveal how competing temporal environments interact during the sensitive period. Such populations sidestep the ethical and practical challenges of experimental manipulation while providing insight into how multiple temporal frameworks can coexist, or compete, within a single cognitive system.
Computational modeling could formalize the constitution/modulation distinction in ways that generate more precise predictions. Specifically, models of statistical learning should be tested to determine whether they produce categorical representations (discrete attractor states that resist modification once established) or continuous representations (probability distributions that shift smoothly with new input). Current connectionist and Bayesian models make different predictions here. If statistical learning over rhythmic input produces categorical boundaries analogous to phonemic categories, with sharp transitions and resistance to later restructuring, this would provide computational support for constitution. If instead the models predict smooth, continuous updating regardless of when exposure occurs, this would favor modulation. Formalizing the distinction computationally would also clarify what "architecture" means: whether we are discussing the structure of representations, the learning algorithms that produce them, or the neural substrates that implement them.