## 4 Results
### Correlation Between Perception and Simulation
Therefore, we can conclude that when subjects were concentrating on strong coincidence detection, the lower left corner and negatively with a low entropy in the upper right corner are negatively correlated with a low entropy in the upper right corner. Figure 7: Correlation between perceived roughness (upper plots) as well as separateness (lower plots) for the guitar tones (left plots) and all other non-guitar sounds (right plots) and the entropy S of the accumulated ISI spectrum, while post-processing the spectrum with Gaussian blurring with standard deviation \(\sigma\) and a peak sharpness s. All plots show a consistency or ridge from right/bottom to left/top as expected (see text for more detail). For roughness, the non-guitar sounds on the right have a negative correlation throughout, while the guitar sounds on the left have a positive correlation, pointing to an opposite perception between familiar and unfamiliar sounds. For separateness, the correlation between the guitar sounds on the left is positive at the right upper ridge and negative on the lower left side, while the non-guitar sounds on the left have a positive correlation on this lower left side and negative correlation at the upper right ridge, again showing opposite behaviour. their judgements were much more consistent intersubjectively compared to the cases where subjects put their attention to low coincidence. The presence of only 3-5 amplitudes after strong coincidence detection points to subjects performing pitch perception, while low coincidence detection can be associated with timbre. Therefore, high correlations of perception with much coincidence, the ridges in the right upper corners of the plots, mean that subjects have put their attention to pitch. By contrast, a high correlation of perception with low coincidence detection, the left lower part of the plots, indicates subjects paying attention to timbre. This is expected as pitch is a much more consistent perception than timbre. It is also interesting to have a look at the standard deviations for the perception of roughness and separateness, shown in Figure 9 and Figure 10, respectively. This perceptual  
Figure 8: Correlation of the standard deviation of the perceptual separateness and roughness with the ISI histogram entropy S. The correlation shows slight similarity with the mean value correlations of the perceptual dimensions. standard deviation between subjects is expected to be low with familiar sounds, so subjects agree on their perception. It is expected to be higher with unfamiliar sounds, where subjects are not trained to these sounds and judge more individually. Indeed, for roughness the guitar sounds have among the lowest values. Especially, the unfamiliar hulusi sounds are perceived considerably differently between subjects. Nevertheless, especially for the smaller tone intervals up to a minor third, the differences between familiar and unfamiliar sounds are less prominent. For separateness, the standard deviations are highest for the guitar sounds, and subjects judged nearly all unfamiliar sounds more similarly. The two gongs are among the lowest values, indeed these sounds are special as one can decide to hear only one sound or many anharmonic partials. Clearly, in terms of the judgements' standard deviations a difference between roughness and separateness is present. This is also seen when examining the correlation between the mean and standard deviation of roughness perception, which is 0.62, pointing to increased uncertainty of judgements with higher values. While the correlation between mean and standard deviation for separateness is only 0.27, and so, seems to be more independent. So, we conclude three points. First, the familiar guitar sounds are perceived using only a small number of ISI histogram amplitudes, here 3-5, while the unfamiliar non-guitar tones are perceived using the broad range of many amplitudes in the ISI histogram.