52% Yes, a signiicant crisis 3% No, there is no crisis 7% Don't know 38% Yes, a slight crisis 38% Yes, a slight crisis 1,576 RESEARCHERS SURVEYED M ore than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature's survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research. The data reveal sometimes-contradictory attitudes towards reproduc-ibility. Although 52% of those surveyed agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature. Data on how much of the scientific literature is reproducible are rare and generally bleak. The best-known analyses, from psychology 1 and cancer biology 2 , found rates of around 40% and 10%, respectively. Our survey respondents were more optimistic: 73% said that they think that at least half of the papers in their field can be trusted, with physicists and chemists generally showing the most confidence. The results capture a confusing snapshot of attitudes around these issues, says Arturo Casadevall, a microbiologist at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland. "At the current time there is no consensus on what reproducibility is or should be. " But just recognizing that is a step forward, he says. "The next step may be identifying what is the problem and to get a consensus. "

In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.

Under typical viewing conditions, human observers readily distinguish between materials such as silk, marmalade, or granite, an achievement of the visual system that is poorly understood. Recognizing transparent materials is especially challenging. Previous work on the perception of transparency has focused on objects composed of flat, infinitely thin filters. In the experiments reported here, we considered thick transparent objects, such as ice cubes, which are irregular in shape and can vary in refractive index. An important part of the visual evidence signaling the presence of such objects is distortions in the perceived shape of other objects in the scene. We propose a new class of visual cues derived from the distortion field induced by thick transparent objects, and we provide experimental evidence that cues arising from the distortion field predict both the successes and the failures of human perception in judging refractive indices

H. R. Blackwell (1952) investigated the influence of different psychophysical methods and procedures on detection thresholds. He found that the temporal two-interval forced-choice method (2-IFC) combined with feedback, blocked constant stimulus presentation with few different stimulus intensities, and highly trained observers resulted in the "best" threshold estimates. This recommendation is in current practice in many psychophysical laboratories and has entered the psychophysicists' "folk wisdom" of how to run proper psychophysical experiments. However, Blackwell's recommendations explicitly require experienced observers, whereas many psychophysical studies, particularly with children or within a clinical setting, are performed with naïve observers. In a series of psychophysical experiments, we find a striking and consistent discrepancy between naïve observers' behavior and that reported for experienced observers by Blackwell: Naïve observers show the "best" threshold estimates for the spatial four-alternative forced-choice method (4-AFC) and the worst for the commonly employed temporal 2-IFC. We repeated our study with a highly experienced psychophysical observer, and he replicated Blackwell's findings exactly, thus suggesting that it is indeed the difference in psychophysical experience that causes the discrepancy between our findings and those of Blackwell. In addition, we explore the efficiency of different methods and show 4-AFC to be more than 3.5 times more efficient than 2-IFC under realistic conditions. While we have found that 4-AFC consistently gives lower thresholds than 2-IFC in detection tasks, we have found the opposite for discrimination tasks. This discrepancy suggests that there are large extrasensory influences on thresholds--sensory memory for IFC methods and spatial attention for spatial forced-choice methods--that are critical but, alas, not part of theoretical approaches to psychophysics such as signal detection theory.