Fig. 11.

Improved neural discrimination in the behaving compared with the passive condition using vector strength (VS). A–D: example of ML SU that improved phase-locking in the behaving condition. A and B: raster plots of SU response to 15-Hz AM in the passive (A) and behaving (B) conditions. C: phase-projected vector strength (VS_pp) plotted as a function of modulation depth for the passive and behaving conditions. D: ROCa based on VS_pp plotted as a function of modulation depth. Note in D that the behaving point (white circle) is covering the ability to see the passive point (black square) at 0% depth (both behaving and passive are 0.5 for ROCa by definition). E and F: ML population mean ROCas based on VS_pp during the entire stimulus duration (80–800 ms, the onset response is excluded) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (E) and SUs (F). G and H: population mean ROCas based on VS_pp during 1st half of the stimulus (80–400 ms, the onset response is excluded) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (G) and SUs (H). I and J: population mean ROCas based on VS_pp during 2nd half of the stimulus (400–800 ms) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (I) and SUs (J). In all panels, P values are denoted showing Wilcoxon signed-rank test comparing collapsed-depth ROC areas between behaving and passive conditions. *Individual modulation depths where a Wilcoxon signed-rank test yielded a value of P < 0.05.