Power spectrum model of visual masking: simulations and empirical data

In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.

Accommodative changes produced in response to overnight orthokeratology

Background To evaluate short-term (3 months) and long-term (3 years) accommodative changes produced by overnight orthokeratology (OK). Methods A prospective, longitudinal study on young adult subjects with low to moderate myopia was carried out. A total of 93 patients took part in the study. Out of these, 72 were enrolled into the short-term follow-up: 21 were on a control group, 26 on a Paragon CRT contact lenses group, and 25 on a Seefree contact lenses group. The other 21 patients were old CRT wearers on long-term follow-up. Accommodative function was assessed by means of negative and positive relative accommodation (NRA / PRA), monocular accommodative amplitude (MAA), accommodative lag, and monocular accommodative facility (MAF). These values were compared among the three short-term groups at the follow-up visit. The long- and short-term follow-up data was compared among the CRT groups. Results Subjective accommodative results did not suffer any statistically significant changes in any of the accommodative tests for any of the short-term groups when compared to baseline. There were no statistically significant differences between the three short-term groups at the follow-up visit. When comparing the short- and long-term groups, only the NRA showed a significant difference (p = 0.0006) among all the accommodation tests. Conclusions OK does not induce changes in the ocular accommodative function for either short-term or long-term periods.