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Virtual reality (VR) displays are being used in an increasingly wide range of applications. However, previous work shows that viewers often perceive scene properties very differently in real and virtual environments and so realistic perception of virtual stimuli should always be a carefully tested conclusion, not an assumption. One important property for realistic scene perception is surface color. To evaluate how well virtual platforms support realistic perception of achromatic surface color, we assessed lightness constancy in a physical apparatus with real lights and surfaces, in a commercial VR headset, and on a traditional flat-panel display. We found that lightness constancy was good in all three environments, though significantly better in the real environment than on the flat-panel display. We also found that variability across observers was significantly greater in VR and on the flat-panel display than in the physical environment. We conclude that these discrepancies should be taken into account in applications where realistic perception is critical but also that in many cases VR can be used as a flexible alternative to flat-panel displays and a reasonable proxy for real environments., (© 2024. The Psychonomic Society, Inc.)

Virtual reality (VR) displays are an increasingly popular medium for experiments on visual perception. This presents the challenge of showing precisely controlled stimuli on devices that were not primarily designed for research. Here we describe methods for controlling stimulus luminance in VR experiments created in Unity using the Built-in Render Pipeline. We discuss the Gamma/Linear setting, measuring luminance in a VR headset, and using color grading in Unity's Post-Processing Stack to make stimulus luminance proportional to achromatic RGB value. We provide MATLAB code that uses luminance measurements from a VR headset to generate the lookup table that Unity requires for linearizing luminance. We emphasize that when creating experiments in this complex environment, it is important to experiment with the rendering process to confirm that stimuli are displayed as expected. We show results of several such tests and provide code as a starting point for readers who wish to run further tests related to their own research.

Lightness perception is the perception of achromatic surface colors: black, white, and shades of grey. Lightness has long been a central research topic in experimental psychology, as perceiving surface color is an important visual task but also a difficult one due to the deep ambiguity of retinal images. In this article, I review psychophysical work on lightness perception in complex scenes over the past 20 years, with an emphasis on work that supports the development of computational models. I discuss Bayesian models, equivalent illumination models, multidimensional scaling, anchoring theory, spatial filtering models, natural scene statistics, and related work in computer vision. I review open topics in lightness perception that seem ready for progress, including the relationship between lightness and brightness, and developing more sophisticated computational models of lightness in complex scenes.

Rapidly judging the number of objects in a scene is an important perceptual ability. Recent debates have centered on whether number perception is accomplished by dedicated mechanisms and, in particular, on whether number-adaptation aftereffects reflect adaptation of number per se or adaptation of related stimulus properties, such as density. Here, we report an adaptation experiment ( N = 8) for which the predictions of number and density theories are diametrically opposed. We found that when a reference stimulus has higher density than an adaptation stimulus but contains fewer elements, adaptation reduces the perceived number of elements in the reference stimulus. This is consistent with number adaptation and inconsistent with density adaptation. Thus, number-adaptation aftereffects are more than a by-product of density adaptation: When density and number are dissociated, adaptation effects are in the direction predicted by adaptation to number, not density.

Lightness perception is the ability to perceive black, white, and gray surface colors in a wide range of lighting conditions and contexts. This ability is fundamental for any biological or artificial visual system, but it poses a difficult computational problem, and how the human visual system computes lightness is not well understood. Here I show that several key phenomena in lightness perception can be explained by a probabilistic graphical model that makes a few simple assumptions about local patterns of lighting and reflectance, and infers globally optimal interpretations of stimulus images. Like human observers, the model exhibits partial lightness constancy, codetermination, contrast, glow, and articulation effects. It also arrives at human-like interpretations of strong lightness illusions that have challenged previous models. The model's assumptions are reasonable and generic, including, for example, that lighting intensity spans a much wider range than surface reflectance and that shadow boundaries tend to be straighter than reflectance edges. Thus, a probabilistic model based on simple assumptions about lighting and reflectance gives a good computational account of lightness perception over a wide range of conditions. This work also shows how graphical models can be extended to develop more powerful models of constancy that incorporate features such color and depth.

People are able to perceive the 3D shape of illuminated surfaces using image shading cues. Theories about how we accomplish this often assume that the human visual system estimates a single lighting direction and interprets shading cues in accord with that estimate. In natural scenes, however, lighting can be much more complex than this, with multiple nearby light sources. Here we show that the human visual system can successfully judge 3D surface shape even when the lighting direction varies from place to place over a surface, provided the scale at which these lighting changes occur is similar to, or larger than, the size of the shape features being judged. Furthermore, we show that despite being able to accommodate rapid changes in lighting direction when judging shape, observers are generally unable to detect these changes. We conclude that, rather than relying on a single estimated illumination direction, the human visual system can accommodate illumination that varies substantially and rapidly across a surface.

Lightness constancy is the ability to perceive black and white surface colors under a wide range of lighting conditions. This fundamental visual ability is not well understood, and current theories differ greatly on what image features are important for lightness perception. Here we measured classification images for human observers and four models of lightness perception to determine which image regions influenced lightness judgments. The models were a high-pass-filter model, an oriented difference-of-Gaussians model, an anchoring model, and an atmospheric-link-function model. Human and model observers viewed three variants of the argyle illusion (Adelson, 1993) and judged which of two test patches appeared lighter. Classification images showed that human lightness judgments were based on local, anisotropic stimulus regions that were bounded by regions of uniform lighting. The atmospheric-link-function and anchoring models predicted the lightness illusion perceived by human observers, but the high-pass-filter and oriented-difference-of-Gaussians models did not. Furthermore, all four models produced classification images that were qualitatively different from those of human observers, meaning that the model lightness judgments were guided by different image regions than human lightness judgments. These experiments provide a new test of models of lightness perception, and show that human observers' lightness computations can be highly local, as in low-level models, and nevertheless depend strongly on lighting boundaries, as suggested by midlevel models.

Lightness constancy is the ability to perceive surface reflectance correctly despite substantial changes in lighting intensity. A classic view is that lightness constancy is the result of a "discounting" of lighting intensity, and this continues to be a prominent view today. Logvinenko and Maloney (2006) have proposed an alternative approach to understanding lightness constancy, in which observers do not make explicit estimates of reflectance, and lightness constancy is instead based on a perceptual similarity metric that depends on both the reflectance and the illuminance of surfaces viewed under different lighting conditions. Here we compare these two views using a novel, free-adjustment reflectance-matching task. We test whether observers can match reflectance in a task where they are free to adjust both the illuminance and the reflectance of the match stimulus over a wide range. We find that observers can match reflectance under these conditions, which supports the view that observers make explicit estimates of reflectance. We also compare performance in this free adjustment task using physical objects and computer-rendered images as stimuli. We find that lightness constancy is good in both cases, but with some evidence of a glow-related artifact with computer-rendered stimuli.

The shape of the illusory surface in stereoscopic Kanizsa figures is determined by the interpolation of depth from the luminance edges of adjacent inducing elements. Despite ambiguity in the position of illusory boundaries, observers reliably perceive a coherent three-dimensional (3-D) surface. However, this ambiguity may contribute additional uncertainty to the depth percept beyond what is expected from measurement noise alone. We evaluated the intrinsic ambiguity of illusory boundaries by using a cue-combination paradigm to measure the reliability of depth percepts elicited by stereoscopic illusory surfaces. We assessed the accuracy and precision of depth percepts using 3-D Kanizsa figures relative to luminance-defined surfaces. The location of the surface peak was defined by illusory boundaries, luminance-defined edges, or both. Accuracy and precision were assessed using a depth-discrimination paradigm. A maximum likelihood linear cue combination model was used to evaluate the relative contribution of illusory and luminance-defined signals to the perceived depth of the combined surface. Our analysis showed that the standard deviation of depth estimates was consistent with an optimal cue combination model, but the points of subjective equality indicated that observers consistently underweighted the contribution of illusory boundaries. This systematic underweighting may reflect a combination rule that attributes additional intrinsic ambiguity to the location of the illusory boundary. Although previous studies show that illusory and luminance-defined contours share many perceptual similarities, our model suggests that ambiguity plays a larger role in the perceptual representation of illusory contours than of luminance-defined contours.

Most of the theory supporting our understanding of classification images relies on standard signal detection models and the use of normally distributed stimulus noise. Here I show that the most common methods of calculating classification images by averaging stimulus noise samples within stimulus-response classes of trials are much more general than has previously been demonstrated, and that they give unbiased estimates of an observer's template for a wide range of decision rules and non-Gaussian stimulus noise distributions. These results are similar to findings on reverse correlation and related methods in the neurophysiology literature, but here I formulate them in terms that are tailored to signal detection analyses of visual tasks, in order to make them more accessible and useful to visual psychophysicists. I examine 2AFC and yes-no designs. These findings make it possible to use and interpret classification images in tasks where observers' decision strategies may not conform to classic signal detection models such as the difference rule, and in tasks where the stimulus noise is non-Gaussian., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models' performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools for testing for such a strategy.

Despite decades of research, there is still uncertainty about how people make simple decisions about perceptual stimuli. Most theories assume that perceptual decisions are based on decision variables, which are internal variables that encode task-relevant information. However, decision variables are usually considered to be theoretical constructs that cannot be measured directly, and this often makes it difficult to test theories of perceptual decision making. Here we show how to measure decision variables on individual trials, and we use these measurements to test theories of perceptual decision making more directly than has previously been possible. We measure classification images, which are estimates of templates that observers use to extract information from stimuli. We then calculate the dot product of these classification images with the stimuli to estimate observers' decision variables. Finally, we reconstruct each observer's "decision space," a map that shows the probability of the observer's responses for all values of the decision variables. We use this method to examine decision strategies in two-alternative forced choice (2AFC) tasks, for which there are several competing models. In one experiment, the resulting decision spaces support the difference model, a classic theory of 2AFC decisions. In a second experiment, we find unexpected decision spaces that are not predicted by standard models of 2AFC decisions, and that suggest intrinsic uncertainty or soft thresholding. These experiments give new evidence regarding observers' strategies in 2AFC tasks, and they show how measuring decision variables can answer long-standing questions about perceptual decision making.

All images are highly ambiguous, and to perceive 3-D scenes, the human visual system relies on assumptions about what lighting conditions are most probable. Here we show that human observers' assumptions about lighting diffuseness are well matched to the diffuseness of lighting in real-world scenes. We use a novel multidirectional photometer to measure lighting in hundreds of environments, and we find that the diffuseness of natural lighting falls in the same range as previous psychophysical estimates of the visual system's assumptions about diffuseness. We also find that natural lighting is typically directional enough to override human observers' assumption that light comes from above. Furthermore, we find that, although human performance on some tasks is worse in diffuse light, this can be largely accounted for by intrinsic task difficulty. These findings suggest that human vision is attuned to the diffuseness levels of natural lighting conditions., (© 2014 ARVO.)

Classification images and bubbles images are psychophysical tools that use stimulus noise to investigate what features people use to make perceptual decisions. Previous work has shown that classification images can be estimated using the generalized linear model (GLM), and here I show that this is true for bubbles images as well. Expressing the two approaches in terms of a single statistical model clarifies their relationship to one another, makes it possible to measure classification images and bubbles images simultaneously, and allows improvements developed for one method to be used with the other.

Every biological or artificial visual system faces the problem that images are highly ambiguous, in the sense that every image depicts an infinite number of possible 3D arrangements of shapes, surface colors, and light sources. When estimating 3D shape from shading, the human visual system partly resolves this ambiguity by relying on the light-from-above prior, an assumption that light comes from overhead. However, light comes from overhead only on average, and most images contain visual information that contradicts the light-from-above prior, such as shadows indicating oblique lighting. How does the human visual system perceive 3D shape when there are contradictions between what it assumes and what it sees? Here we show that the visual system combines the light-from-above prior with visual lighting cues using an efficient statistical strategy that assigns a weight to the prior and to the cues and finds a maximum-likelihood lighting direction estimate that is a compromise between the two. The prior receives surprisingly little weight and can be overridden by lighting cues that are barely perceptible. Thus, the light-from-above prior plays a much more limited role in shape perception than previously thought, and instead human vision relies heavily on lighting cues to recover 3D shape. These findings also support the notion that the visual system efficiently integrates priors with cues to solve the difficult problem of recovering 3D shape from 2D images.

Classification images have recently become a widely used tool in visual psychophysics. Here, I review the development of classification image methods over the past fifteen years. I provide some historical background, describing how classification images and related methods grew out of established statistical and mathematical frameworks and became common tools for studying biological systems. I describe key developments in classification image methods: use of optimal weighted sums based on the linear observer model, formulation of classification images in terms of the generalized linear model, development of statistical tests, use of priors to reduce dimensionality, methods for experiments with more than two response alternatives, a variant using multiplicative noise, and related methods for examining nonlinearities in visual processing, including second-order Volterra kernels and principal component analysis. I conclude with a selective review of how classification image methods have led to substantive findings in three representative areas of vision research, namely, spatial vision, perceptual organization, and visual search.

Bayesian cue combination models have been used to examine how human observers combine information from several cues to form estimates of linear quantities like depth. Here we develop an analogous theory for circular quantities like planar direction. The circular theory is broadly similar to the linear theory but differs in significant ways. First, in the circular theory the combined estimate is a nonlinear function of the individual cue estimates. Second, in the circular theory the mean of the combined estimate is affected not only by the means of individual cues and the weights assigned to individual cues but also by the variability of individual cues. Third, in the circular theory the combined estimate can be less certain than the individual estimates, if the individual estimates disagree with one another. Fourth, the circular theory does not have some of the closed-form expressions available in the linear theory, so data analysis requires numerical methods. We describe a vector sum model that gives a heuristic approximation to the circular theory's behavior. We also show how the theory can be extended to deal with spherical quantities like direction in three-dimensional space.

We elucidate two properties of the intrinsic constraint (IC) model of depth cue combination (F. Domini, C. Caudek, & H. Tassinari, 2006). First, we show that IC combines depth cues in a weighted sum that maximizes the signal-to-noise ratio of the combined estimate. Second, we show that IC predicts that any two depth-matched pairs of stimuli are separated by equal numbers of just noticeable differences (JNDs) in depth. That is, IC posits a strong link between perceived depth and depth discrimination, much like some Fechnerian theories of sensory scaling. We test this prediction, and we find that it does not hold. We also find that depth discrimination performance approximately follows Weber's law, whereas IC assumes that depth discrimination thresholds are independent of baseline stimulus depth.

Study Design: Cross-sectional study with repeated measurements., Objectives: To examine the patient's perspective of the impact of spinal cord injury (SCI) on physical, cognitive, emotional function, and quality of life (QOL)., Setting: Australia., Methods: A sample of 63 patients with SCI, 32 of whom had recent injuries, and 31 with established injuries were administered the Ruff Neurobehavioral Inventory to examine patients' subjective evaluation of pre- and post-injury functioning. Current happiness levels were also evaluated using the Subjective Happiness Scale. A follow up assessment was performed 6 months later to examine changes over time., Results: A significant difference was found between perception of pre- and postmorbid function on composite Cognitive (t=5.99, df=62, P<0.001), Physical (t=11.56, df=62, P<0.001), and QOL (t=7.16, df=62, P<0.001) scales and on several of the Emotional subscales including anxiety, paranoia and suspicion, and substance abuse (P<0.001). A series of hierarchical regression analyses indicate that post-SCI pain was a significant predictor of: cognitive (R(2)=0.20, P<0.001); emotional (R(2)=0.13, P<0.004); and of QOL (R(2)=0.22, P<0.001) functioning. With the exception of a decrease in happiness (P<0.01), there were no significant changes in any measures over the 6 month time period., Conclusions: There are significant changes in patients' perceptions of physical and cognitive functioning, and of QOL before and after SCI and some aspects of emotional functioning. Pain has a significant adverse effect on functioning. Happiness decreased slightly in the 6 months between surveys.

After observers see an object or pattern, their visual memory of what they have seen decays slowly over time. Nearly all current theories of vision assume that decay of short-term memory occurs because visual representations are progressively and randomly corrupted as time passes. We tested this assumption using psychophysical noise-masking methods, and we found that visual memory decays in a completely deterministic fashion. This surprising finding challenges current ideas about visual memory and sets a goal for future memory research: to characterize the deterministic "forgetting function" that describes how memories decay over time.

How well do classification images characterize human observers' strategies in perceptual tasks? We show mathematically that from the classification image of a noisy linear observer, it is possible to recover the observer's absolute efficiency. If we could similarly predict human observers' performance from their classification images, this would suggest that the linear model that underlies use of the classification image method is adequate over the small range of stimuli typically encountered in a classification image experiment, and that a classification image captures most important aspects of human observers' performance over this range. In a contrast discrimination task and in a shape discrimination task, we found that observers' absolute efficiencies were generally well predicted by their classification images, although consistently slightly (approximately 13%) higher than predicted. We consider whether a number of plausible nonlinearities can account for the slight under prediction, and of these we find that only a form of phase uncertainty can account for the discrepancy.

The bubbles method is a recently developed variant of reverse correlation methods that have been used in psychophysics and physiology. We show mathematically that for the broad and important class of noisy linear observers, the bubbles method recovers much less information about how observers process stimuli than reverse correlation does. We also show experimentally that the unusual type of noise used in the bubbles method can drastically change human observers' strategies in psychophysical tasks, which reduces the value of the information that is obtained from a bubbles experiment. We conclude that reverse correlation is generally preferable to the bubbles method in its present form, but we also give suggestions as to how the bubbles method could be modified to avoid the problems we discuss.

Can the oculomotor system use shape cues to guide search saccades? Observers searched for target letters (D, U, or X) among distractors (the letter O in the discrimination task and blank locations in the detection task) in Gaussian white noise. We measured the accuracy of first saccadic responses on each trial and perceptual (i.e., button-press) responses in separate trials with the stimulus duration chosen so that the saccadic and perceptual processing times were matched. We calculated the relative efficiency of saccadic decisions compared with perceptual decisions, eta(rel) = (d'(sac)/d'(per))2. Relative efficiency was low but consistently greater than zero in discrimination tasks (15% +/- 6%) and high in detection tasks (60% +/- 10%). We conclude that the saccadic targeting system can use shape cues, but less efficiently than the perceptual system can.

Implications: This case history describes the treatment of a patient suffering with persistent pain. He was treated surgically with implantation of a spinal cord stimulator. After surgery, a partial paralysis that could not be explained medically and that was probably related to emotional factors occurred, and cognitive behavioral treatment was begun. This paper discusses the importance of considering social and psychological factors when medical treatment options are considered.

Using a linear cue combination framework, we develop a measure of selective attention that describes the relative weight that an observer assigns to attended and unattended parts of a stimulus when making perceptual judgments. We call this measure attentional weight. We present two methods for measuring attentional weight by calculating the trial-by-trial correlation between the strength of attended and unattended parts of a stimulus and the observer's responses. We illustrate these methods in three experiments that investigate whether observers can direct selective attention according to contrast polarity when judging global direction of motion or global orientation. We find that when observers try to judge the global direction or orientation of the parts of a stimulus with a given contrast polarity (white or black), their responses are nevertheless strongly influenced by parts of the stimulus that have the opposite contrast polarity. Our measure of selective attention indicates that the influence of the opposite-polarity distractors on observers' responses is typically 65% as strong as the influence of the targets in the motion task, and typically 25% as strong as the targets in the orientation task, demonstrating that observers have only a limited ability to direct attention according to contrast polarity. We discuss some of the advantages of using a linear cue combination framework to study selective attention.

In signal detection theory, an observer's responses are often modeled as being based on a decision variable obtained by cross-correlating the stimulus with a template, possibly after corruption by external and internal noise. The response classification method estimates an observer's template by measuring the influence of each pixel of external noise on the observer's responses. A map that shows the influence of each pixel is called a classification image. Other authors have shown how to calculate classification images from external noise fields, but the optimal calculation has never been determined, and the quality of the resulting classification images has never been evaluated. Here we derive the optimal weighted sum of noise fields for calculating classification images in several experimental designs, and we derive the signal-to-noise ratio (SNR) of the resulting classification images. Using the expressions for the SNR, we show how to choose experimental parameters, such as the observer's performance level and the external noise power, to obtain classification images with a high SNR. We discuss two-alternative identification experiments in which the stimulus is presented at one or more contrast levels, in which each stimulus is presented twice so that we can estimate the power of the internal noise from the consistency of the observer's responses, and in which the observer rates the confidence of his responses. We illustrate these methods in a series of contrast increment detection experiments.

We measured the extent of amodal completion as a function of stimulus duration over the range of 15-210 msec, for both moving and stationary stimuli. Completion was assessed using a performance-based measure; a shape discrimination task that is easy if the stimulus is amodally completed and difficult if it is not. Specifically, participants judged whether an upright rectangle was longer horizontally or vertically, when the rectangle was unoccluded, occluded at its corners by four negative-contrast squares, or occluded at its corners by four zero-contrast squares. In the zero-contrast condition, amodal completion did not occur because there were no occlusion cues; in the unoccluded condition, the entire figure was present. Thus, comparing performance in the negative-contrast condition to these two extremes provided a quantitative measure of amodal completion. This measure revealed a rapid but measurable time course for amodal completion. Moving and stationary stimuli took the same amount of time to be completed (approximately 75 msec), but moving stimuli had slightly stronger completion at long durations.

The visual system is constantly faced with the problem of identifying partially occluded objects from incomplete images cast on the retinae. Phenomenologically, the visual system seems to fill in missing information by interpolating illusory and occluded contours at points of occlusion, so that we perceive complete objects. Previous behavioural [1] [2] [3] [4] [5] [6] [7] and physiological [8] [9] [10] [11] [12] studies suggest that the visual system treats illusory and occluded contours like luminance-defined contours in many respects. None of these studies has, however, directly shown that illusory and occluded contours are actually used to perform perceptual tasks. Here, we use a response-classification technique [13] [14] [15] [16] [17] [18] [19] [20] to answer this question directly. This technique provides pictorial representations - 'classification images' - that show which parts of a stimulus observers use to make perceptual decisions, effectively deriving behavioural receptive fields. Here we show that illusory and occluded contours appear in observers' classification images, providing the first direct evidence that observers use perceptually interpolated contours to recognize objects. These results offer a compelling demonstration of how visual processing acts on completed representations, and illustrate a powerful new technique for constraining models of visual completion.

During the past two decades a number of inherited polymorphic proteins and enzymes have been identified in different ethnic groups. Certain biochemical markers appear to be associated with susceptibility to harmful effects of the environment. This paper reviews the potential for using these markers to determine whether affected persons ought to be excluded from certain jobs or from the environment(s) of specific settings in the workplace. This paper will also attempt to identify the criteria that should be used before it is scientifically and ethically justified to use these markers to "protect" workers and/or persons from harm by not allowing them to work in a specific setting or with specific agents to which they have been deemed to be at special risk for injury.

Glucose-6-phosphate dehydrogenase (G6PD) phenotype studies were done on a black family with X-linked heredofamilial bilateral microphthalmia (HBM). Three crossovers and three non-crossovers were detected in three informative matings of four generations yielding a recombination value of 0.5. These findings do not provide evidence for linkage between the G6PD and HBM loci, suggesting either that the G6PD and HBM loci are far apart on the X chromosome or that HBM in this family is inherited as an autosomal dominant male sex-limited trait.

Parents who use information from prenatal diagnosis in family planning may be faced with difficult decisions. For a variety of reasons, the communications and decision-making process may be substantially more complex and difficult if the parents are members of a minority group. Unless a number of special considerations are kept in mind when programs of prenatal diagnosis for minorities are developed, such programs may be more harmful than beneficial.

Regular, formal structured evaluation of the counseling skills of the genetic counselor should be a part of every counseling program. There is no ideal method of evaluation. Each has advantages and disadvantages. A combination of methods, determined by funds and equipment available probably presents the best approach to this problem. Formal studies of the optimum methods of counselor evaluation and the best criteria to be used are needed to provide definitive answers to the problem of how best to evaluate the performance of genetic counselors.

A competent and effective genetic counselor must recognize and deal with the psychological defense mechanisms which affected persons and parents of affected children use to cope with the strain of genetic disease in the family. Denial, guilt, hostility, grief and mourning and the psychology of defectiveness are all potent emotional factors that must be dispelled or worked through before parents should make reproductive decisions. If the counseling experience is to be satisfactory, the counselor must help parents meet the immediate and long term social needs of affected individuals in addition to their medical needs.

The isoenzyme patterns of alcohol dehydrogenase from human liver and stomach are different in the fetal, newborn, childhood, and adult periods of life. This provides additional evidence that the fetal and adult alcohol dehydrogenases are qualitatively different. Gastric and hepatic isoenzyme patterns also differed from each other.

Hexokinases of Drosophila melanogaster were investigated by starchgel electrophoresis. A hexokinase is present in both sexes during earlier stages of development, but it persists only in male adults. In addition, in this species there is a testis-specific hexokinase which is first observed during the pupal period.