Resolving the apparent paradox of multisensory expertise: coupling priors, common-cause priors, and how to distinguish among them

Perceptual expertise is widely assumed to refine multisensory perception, yet the empirical literature appears inconsistent. Trained musicians show narrower audiovisual temporal integration windows and quicker segregation, whereas experienced wine tasters seem more influenced by a wine's color in their aroma and flavor judgments than novices are. We argue that the inconsistency arises from a conflation of two formally distinct quantities: the prior probability that concurrent signals arise from a common source, and the coupling between the values of signals that share one. We present a generative model in which both are free parameters, along with channel precision and a decision criterion, that contains causal-inference and coupling-prior models as special cases. The model shows that the correlation between the values of two signals across an environment reflects only the product of the two priors, so that a correlation-tracking learner acquires the coupling when the signals always share a source, whereas separating the two priors requires representing causal structure; that binary common-source reports confound the common-cause prior with the decision criterion; that the coupling trades off against the measurement noise of the influencing channel unless its precision is measured independently; and that the two priors leave distinct signatures in how cross-modal influence varies with disparity. When the source is fixed, as for the color and odor of a wine, tighter coupling alone produces the conjunction of greater displacement by color and better discrimination between wines reported for experienced tasters, which no change in odor precision can produce. Simulations of a test battery combining continuous estimates, common-source reports and discrimination thresholds show that the parameters can be recovered, that a within-subject dissociation can be detected with conventional sample sizes under illustrative effect sizes, and that recovery fails in certain cases. The account yields a rule classifying environments by their base rate of common causation and their coupling strength, and the same ambiguity attends claims about altered multisensory processing in autism and aging. We also show that the most cited demonstration of expert susceptibility to color contains no expertise manipulation, and suggest that apparent expert biases may reflectwind calibration to real-world statistics.