QUEST+: A general multidimensional Bayesian adaptive psychometric method.

QUEST+ is a Bayesian adaptive psychometric testing method that allows an arbitrary number of stimulus dimensions, psychometric function parameters, and trial outcomes. It is a generalization and extension of the original QUEST procedure and incorporates many subsequent developments in the area of parametric adaptive testing. With a single procedure, it is possible to implement a wide variety of experimental designs, including conventional threshold measurement; measurement of psychometric function parameters, such as slope and lapse; estimation of the contrast sensitivity function; measurement of increment threshold functions; measurement of noise-masking functions; Thurstone scale estimation using pair comparisons; and categorical ratings on linear and circular stimulus dimensions. QUEST+ provides a general method to accelerate data collection in many areas of cognitive and perceptual science.

Computing human optical point spread functions.

There is renewed interest in the role of optics in human vision. At the same time there have been advances that allow for routine standardized measurement of the wavefront aberrations of the human eye. Computational methods have been developed to convert these measurements to a description of the human visual optical point spread function (PSF), and to thereby calculate the retinal image. However, tools to implement these calculations for vision science are not widely available or widely understood. In this report we describe software to compute the human optical PSF, and we discuss constraints and limitations.

Letter identification and the neural image classifier.

Letter identification is an important visual task for both practical and theoretical reasons. To extend and test existing models, we have reviewed published data for contrast sensitivity for letter identification as a function of size and have also collected new data. Contrast sensitivity increases rapidly from the acuity limit but slows and asymptotes at a symbol size of about 1 degree. We recast these data in terms of contrast difference energy: the average of the squared distances between the letter images and the average letter image. In terms of sensitivity to contrast difference energy, and thus visual efficiency, there is a peak around » degree, followed by a marked decline at larger sizes. These results are explained by a Neural Image Classifier model that includes optical filtering and retinal neural filtering, sampling, and noise, followed by an optimal classifier. As letters are enlarged, sensitivity declines because of the increasing size and spacing of the midget retinal ganglion cell receptive fields in the periphery.

A formula for human retinal ganglion cell receptive field density as a function of visual field location.

In the human eye, all visual information must traverse the retinal ganglion cells. The most numerous subclass, the midget retinal ganglion cells, are believed to underlie spatial pattern vision. Thus the density of their receptive fields imposes a fundamental limit on the spatial resolution of human vision. This density varies across the retina, declining rapidly with distance from the fovea. Modeling spatial vision of extended or peripheral targets thus requires a quantitative description of midget cell density throughout the visual field. Through an analysis of published data on human retinal topography of cones and ganglion cells, as well as analysis of prior formulas, we have developed a new formula for midget retinal ganglion cell density as a function of position in the monocular or binocular visual field.

A formula for the mean human optical modulation transfer function as a function of pupil size.

We have constructed an analytic formula for the mean radial modulation transfer function of the best-corrected human eye as a function of pupil diameter, based on previously collected wave front aberrations from 200 eyes (Thibos, Hong, Bradley, & Cheng, 2002). This formula will be useful in modeling the early stages of human vision.

A unified formula for light-adapted pupil size.

The size of the pupil has a large effect on visual function, and pupil size depends mainly on the adapting luminance, modulated by other factors. Over the last century, a number of formulas have been proposed to describe this dependence. Here we review seven published formulas and develop a new unified formula that incorporates the effects of luminance, size of the adapting field, age of the observer, and whether one or both eyes are adapted. We provide interactive demonstrations and software implementations of the unified formula.

Modeling acuity for optotypes varying in complexity.

Watson and Ahumada (2008) described a template model of visual acuity based on an ideal-observer limited by optical filtering, neural filtering, and noise. They computed predictions for selected optotypes and optical aberrations. Here we compare this model's predictions to acuity data for six human observers, each viewing seven different optotype sets, consisting of one set of Sloan letters and six sets of Chinese characters, differing in complexity (Zhang, Zhang, Xue, Liu, & Yu, 2007). Since optical aberrations for the six observers were unknown, we constructed 200 model observers using aberrations collected from 200 normal human eyes (Thibos, Hong, Bradley, & Cheng, 2002). For each condition (observer, optotype set, model observer) we estimated the model noise required to match the data. Expressed as efficiency, performance for Chinese characters was 1.4 to 2.7 times lower than for Sloan letters. Efficiency was weakly and inversely related to perimetric complexity of optotype set. We also compared confusion matrices for human and model observers. Correlations for off-diagonal elements ranged from 0.5 to 0.8 for different sets, and the average correlation for the template model was superior to a geometrical moment model with a comparable number of parameters (Liu, Klein, Xue, Zhang, & Yu, 2009). The template model performed well overall. Estimated psychometric function slopes matched the data, and noise estimates agreed roughly with those obtained independently from contrast sensitivity to Gabor targets. For optotypes of low complexity, the model accurately predicted relative performance. This suggests the model may be used to compare acuities measured with different sets of simple optotypes.

Blur clarified: a review and synthesis of blur discrimination.

Blur is an important attribute of human spatial vision, and sensitivity to blur has been the subject of considerable experimental research and theoretical modeling. Often, these models have invoked specialized concepts or mechanisms, such as intrinsic blur, multiple spatial frequency channels, or blur estimation units. In this paper, we review the several experimental studies of blur discrimination and find that they are in broad empirical agreement. However, contrary to previous modeling efforts, we find that specialized mechanisms are not required and that the essential features of blur discrimination are fully accounted for by a visible contrast energy (ViCE) model, in which two spatial patterns are distinguished when the integrated difference between their masked local visible contrast energy responses reaches a threshold value. In the ViCE model, intrinsic blur is represented by the high-frequency limb of the contrast sensitivity function, but the low-frequency limb also contributes to the predictions for large reference blurs, and the model includes masking, which improves predictions for high-contrast stimuli.

Predicting visual acuity from wavefront aberrations.

It is now possible to routinely measure the aberrations of the human eye, but there is as yet no established metric that relates aberrations to visual acuity. A number of metrics have been proposed and evaluated, and some perform well on particular sets of evaluation data. But these metrics are not based on a plausible model of the letter acuity task and may not generalize to other sets of aberrations, other data sets, or to other acuity tasks. Here we provide a model of the acuity task that incorporates optical and neural filtering, neural noise, and an ideal decision rule. The model provides an excellent account of one large set of evaluation data. Several suboptimal rules perform almost as well. A simple metric derived from this model also provides a good account of the data set.

JPEG2000 encoding with perceptual distortion control.

In this paper, a new encoding approach is proposed to control the JPEG2000 encoding in order to reach a desired perceptual quality. The new method is based on a vision model that incorporates various masking effects of human visual perception and a perceptual distortion metric that takes spatial and spectral summation of individual quantization errors into account. Compared with the conventional rate-based distortion minimization JPEG2000 encoding, the new method provides a way to generate consistent quality images at a lower bit rate.

A standard model for foveal detection of spatial contrast.

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.

Impact of lossy compression on diagnostic accuracy of radiographs for periapical lesions.

OBJECTIVES: The purpose of this study was to evaluate the lossy Joint Photographic Experts Group compression for endodontic pretreatment digital radiographs. STUDY DESIGN: Fifty clinical charge-coupled device-based, digital radiographs depicting periapical areas were selected. Each image was compressed at 2, 4, 8, 16, 32, 48, and 64 compression ratios. One root per image was marked for examination. Images were randomized and viewed by four clinical observers under standardized viewing conditions. Each observer read the image set three times, with at least two weeks between each reading. Three pre-selected sites per image (mesial, distal, apical) were scored on a five-scale score confidence scale. A panel of three examiners scored the uncompressed images, with a consensus score for each site. The consensus score was used as the baseline for assessing the impact of lossy compression on the diagnostic values of images. The mean absolute error between consensus and observer scores was computed for each observer, site, and reading session. RESULTS: Balanced one-way analysis of variance for all observers indicated that for compression ratios 48 and 64, there was significant difference between mean absolute error of uncompressed and compressed images (P <.05). After converting the five-scale score to two-level diagnostic values, the diagnostic accuracy was strongly correlated (R (2) = 0.91) with the compression ratio. CONCLUSION: The results of this study suggest that high compression ratios can have a severe impact on the diagnostic quality of the digital radiographs for detection of periapical lesions.