## 4 Results
### Correlation Between Perception and Simulation
So the familiar sounds are perceived after intense coincidence detection, while the non-familiar sounds are perceived before coincidence detection, or only using a small part of it. For pitch detection, only a single spike is necessary for each period of f0. Timbre, on the contrary, needs more pitches to differentiate the sound. Therefore, a small number of spikes in the ISI point to pitch-based perception while many peaks allow for an elaborated timbre space. Also, the main peaks of the ISI histogram are associated with pitch in a harmonic sound as the main peaks  
Figure 9: Standard deviation for roughness perception. The guitar tones are among those sounds with the lowest standard deviation. Three _hulusi_ and one strings sound have considerably larger SDs. correspond to a common periodicity, so to f0. Smaller amplitudes more strongly represent timbre, the elaboration of the sound. So, we can conclude that the perception strategy of familiar sounds uses pitch information and that of unfamiliar sounds uses timbre. Second, separateness is correlated oppositely between familiar (negative correlation) and unfamiliar (positive correlation) sounds with low coincidence detection (lower right plot corners), while roughness is correlated negatively in both cases. This corresponds to the findings above, that separateness is perceived considerably more strongly with guitar tones than with non-guitar sounds, while roughness does not show this clear distinction. So, although for both it holds the pitch/timbre difference, separateness uses opposite strategies of perception while roughness uses the same correlation direction. Third, judgements based on timbre are much more inconsistent than those based on pitch, which appears in the correlation between the standard deviation of the perceptual parameters and the entropy S of the ISI histograms. These findings are based on the entropy calculated from the post-processed ISI histogram. Figure 11 shows the same correlations only taking the number N of surviving amplitudes into consideration. The results are similar in some respect, but show differences too. With the roughness guitar plot the ridge is again there, with a negative correlation in the right and left corners as before. Still, now the unfamiliar roughness case has a strong negative correlation when few amplitudes remain in the upper right corner and strong positive correlations in the many amplitude case in the lower left corner. Again, the perception is opposite, but much clearer than in the entropy case. Figure 10: Perceptual standard deviation of the separateness sounds. Here, the guitar tones are among those sounds with the highest SD. The gongs and one _hulusi_ have considerably lower values. For separateness the cases reverse too, the opposite correlation in the lower left corner disappears, and in the familiar and unfamiliar cases both regions correlate positively. Here, the ridge region changes correlation direction. These findings contradict the findings from the perception test, namely, the clear split of familiar and unfamiliar sounds for separateness, pointing to an opposite perception strategy. Therefore, we conclude that using only amplitude counting is not perfectly in  
Figure 11: Same as Figure 7 but correlation over number of ISI histogram amplitudes N. For roughness the non-guitar sounds on the right have a negative correlation in the upper right part and positive in the lower left, while the guitar sounds on the left have a positive correlation on the upper right ridge, again pointing to an opposite perception between familiar and unfamiliar sounds. For separateness, the correlation between the non-guitar sounds on the right is negative in the upper right part and positive in the lower left, while for the guitar sounds it is positive on the upper right ridge and slightly negative in the lower left part.