Human discrimination and modeling of high-frequency complex tones shed light on the neural codes for pitch
Fig 8
Ideal-observer predictions for F0 discrimination.
Results of the F0 discrimination simulations. (A) Simulated F0DLs versus F0 of the ISO HCT stimulus in each auditory-nerve model. Simulations in this panel include no parameter roving. Points indicate the simulated F0DLs at a particular F0 while lines indicate a locally estimated scatterplot smoothing (LOESS) fit to the simulated F0DLs. (B) Simulated all-information F0DLs and vector strength (top row) and simulated rate-place F0DLs and Q10 (bottom row) versus frequency with a double y-axis. To choose the warping on the y-axis for vector strength and Q10, linear models were fit to predict log-transformed F0DLs as a function of log-transformed reciprocals of vector strength or Q10 for the all-information F0DLs or rate-place F0DLs, respectively. The fitted regression equations were then used to warp the y-axes. In other words, we warped the y-axes for vector strength and Q10 to maximize overlap with the model predictions (across all three models) in order to visually demonstrate the relationship between vector strength and Q10 and the simulated F0DLs (C) Ratio of simulated F0DLs at 1.4 kHz and 0.28 kHz in the non-roved simulation at 30 dB re: threshold for each model (left) and ratio of behavioral estimates of F0DLs at 1.4 kHz and 0.28 kHz from various studies (right). Simulated F0DLs were interpolated using LOESS while behavioral F0DLs were linearly interpolated on log-log coordinates.