An adaptive psychometric procedure that places each trial at the current most probable Bayesian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The procedure is simple, fast, and efficient, and may be easily implemented on any computer. The QUEST procedure was developed at the Kenneth Craik Laboratory of Cambridge University. A.B.W. was supported by an NIH postdoctoral fellowship, F32 EYO'219. D.G.P. was supported by a British Ministry of Defence grant, "Spatial noise spectra and target detection/recognition," to F. W. Campbell. We thank, Stanley Klein, Daniel Kersten, and Joseph Hall for helpful comments.A.B.W.'5 mailing addressis:

We propose a model of how humans sense the velocity of moving images. The model exploits constraints provided by human psychophysics, notably that motion-sensing elements appear tuned for two-dimensional spatial frequency, and by the frequency spectrum of a moving image, namely, that its support lies in the plane in which the temporal frequency equals the dot product of the spatial frequency and the image velocity. The first stage of the model is a set of spatial-frequency-tuned, direction-selective linear sensors. The temporal frequency of the response of each sensor is shown to encode the component of the image velocity in the sensor direction. At the second stage, these components are resolved in order to measure the velocity of image motion at each of a number of spatial locations and spatial frequencies. The model has been applied to several illustrative examples, including apparent motion, coherent gratings, and natural image sequences. The model agrees qualitatively with human perception.

We have implemented a model of contrast gain control in human vision which incorporates a number of key features, including a contrast sensitivity function, multiple oriented band-pass

The ModelFest data set was created to provide a public source of data to test and calibrate models of foveal spatial contrast detection. It consists of contrast thresholds for 43 foveal achromatic contrast stimuli collected from each of 16 observers. We have fit these data with a variety of simple models that include one of several contrast sensitivity functions, an oblique effect, a spatial sensitivity aperture, spatial frequency channels, and nonlinear Minkowski summation. While we are able to identify one model, with particular parameters, as providing the lowest overall residual error, we also note that the differences among several good-fitting models are small. We find a strong reciprocity between the size of the spatial aperture and the value of the summation exponent: both are effective means of limiting the extent of spatial summation. The results demonstrate the power of simple models to account for the visibility of a wide variety of spatial stimuli and suggest that special mechanisms to deal with special classes of stimuli are not needed. But the results also illustrate the limited power of even this large data set to distinguish among similar competing models. We identify one model as a possible standard, suitable for simple theoretical and applied predictions.