Fig. 3

Dynamic Admixture Point Process (DAPP) model: rationale and results. a The DAPP model fits smoothly time-varying weights (α and (1−α)) capturing the relative contribution of A- and B-like response distributions to each AB dual-sound trial (point1). The dynamic tendencies of the α curves were then used to generate projected new α curves for hypothetical future draws from this distribution. The waviness and central tendencies were quantified by computing the max swing size and trial-wise mean for an individual trial drawn from the distribution (point 2). Low max swing sizes indicate flat curves and higher values indicate wavy ones (point 3, right panel). Similarly, the distribution of trial-wise means could be bimodal (Extreme) or unimodal (Central) (point 3, left panel). b–d Fit alphas for three example triplets (triplets in b–d are the same as in Fig. 2f, g, h, respectively) and the distributions of trial-wise means and max swing sizes for future draws from the alpha curve generator. e The pattern of DAPP results extended the whole-trial analysis results. Triplets categorized as Mixtures with a win probability > 0.95 tended to be tagged as Flat-Extreme (as example in b). Triplets categorized as Intermediates fell in two different main groups, Wavy-Central (as example in c) and Flat-Central (as example in d). Information about the Skewed vs. Symmetric tag is not shown. See Supplementary Table 1 and Supplementary Figures 6 and 7 for a complete listing of all the tag combinations and additional analyses