Table 1.
Musical experience and listening skills.
Table 2.
Expertise differences in bebop and general model-fit.
Fig 1.
Scatterplots of entropy estimates from the bebop and general models with the final stimuli marked with + (low bebop entropy) and ⨉ (high bebop entropy). The entropy estimates plotted here resulted from the second model run which pertained to the candidate contexts and were computed over the full distribution of the 32 pitches occurring in the Charlie Parker corpus (see Fig B in S1 Text for further details).
Fig 2.
Mean correspondence between behavioural responses and computational model estimates (i.e. “model-fit”), plotted separately for non-musicians, classical musicians, and jazz musicians for two models trained on either bebop or general tonal music. Values positioned above the horizontal zero line designate good model correspondence whereas values below this line designate negative correspondence. For instance, jazz musicians perceive high levels of explicit uncertainty when entropy estimates of the bebop model are high whereas they perceive low uncertainty when the general model predicts high entropy. Error bars designate one standard error above and below the mean. Note that modest positive general model-fit for expectedness arises from high covariance of probability estimates from the two models. Similarly, artefactual negative general model-fit for explicit and inferred uncertainty results from actively ensuring a negative correlation between bebop and general entropy; importantly, this should not be ascribed to inverse following of the general model.
Table 3.
Non-parametric correlations of model-fit and musical expertise.
Fig 3.
Mean expectedness ratings for non-musicians, classical musicians, and jazz musicians in the conditions with low and high degrees of bebop entropy. Stimuli in the low-bebop-entropy condition were simultaneously high in general entropy while stimuli in the high-bebop-entropy condition were simultaneously low in general entropy. Whereas the three groups of participants did not differ when bebop entropy was high, jazz and classical musicians experienced melodic continuations as more unexpected on average in the low-bebop-entropy condition. Error bars designate one standard error above and below the mean.
Table 4.
Condition effects on mean expectedness, inferred, and explicit uncertainty.
Fig 4.
Mean inferred uncertainty for non-musicians, classical musicians, and jazz musicians in the conditions with low and high bebop entropy. Inferred uncertainty corresponds to the Shannon entropy of the distribution of expectedness ratings for each melodic context. Whereas non-musicians experienced similarly high degrees of uncertainty when exposed to melodies from either condition, jazz and classical musicians experienced lower degrees of uncertainty when entropy was estimated to be low according to the style-congruent bebop model. Error bars designate one standard error above and below the mean.
Fig 5.
Mean explicit uncertainty for non-musicians, classical musicians, and jazz musicians in the two experimental conditions. The results mirror those for inferred uncertainty (see Fig 4), with the exception that classical musicians did not have explicit access to the implicit information that they had acquired about music in the bebop style. Error bars designate one standard error below and above the mean.
Fig 6.
Mean explicit vs. inferred uncertainty.
Scatterplots of the relationship between mean explicit and inferred uncertainty separately for the groups of non-musicians, classical musicians, and jazz musicians. This relationship was statistically significant for jazz musicians only.