Signal Integration in Human Visual Speed Perception.

Object motion in natural scenes results in visual stimuli with a rich and broad spatiotemporal frequency spectrum. While the question of how the visual system detects and senses motion energies at different spatial and temporal frequencies has been fairly well studied, it is unclear how the visual system integrates this information to form coherent percepts of object motion. We applied a combination of tailored psychophysical experiments and predictive modeling to address this question with regard to perceived motion in a given direction (i.e., stimulus speed). We tested human subjects in a discrimination experiment using stimuli that selectively targeted four distinct spatiotemporally tuned channels with center frequencies consistent with a common speed. We first characterized subjects' responses to stimuli that targeted only individual channels. Based on these measurements, we then predicted subjects' psychometric functions for stimuli that targeted multiple channels simultaneously. Specifically, we compared predictions of three Bayesian observer models that either optimally integrated the information across all spatiotemporal channels, or only used information from the most reliable channel, or formed an average percept across channels. Only the model with optimal integration was successful in accounting for the data. Furthermore, the proposed channel model provides an intuitive explanation for the previously reported spatial frequency dependence of perceived speed of coherent object motion. Finally, our findings indicate that a prior expectation for slow speeds is added to the inference process only after the sensory information is combined and integrated.

A new two-alternative forced choice method for the unbiased characterization of perceptual bias and discriminability.

Perception is often biased by secondary stimulus attributes (e.g., stimulus noise, attention, or spatial context). A correct quantitative characterization of perceptual bias is essential for testing hypotheses about the underlying perceptual mechanisms and computations. We demonstrate that the standard two-alternative forced choice (2AFC) method can lead to incorrect estimates of perceptual bias. We present a new 2AFC method that solves this problem by asking subjects to judge the relative perceptual distances between the test and each of two reference stimuli. Naïve subjects can easily perform this task. We successfully validated the new method with a visual motion-discrimination experiment. We demonstrate that the method permits an efficient and accurate characterization of perceptual bias and simultaneously provides measures of discriminability for both the reference and test stimulus, all from a single stimulus condition. This makes it an attractive choice for the characterization of perceptual bias and discriminability in a wide variety of psychophysical experiments.

A foundation for evaluating the surgical artificial intelligence literature.

With increasing growth in applications of artificial intelligence (AI) in surgery, it has become essential for surgeons to gain a foundation of knowledge to critically appraise the scientific literature, commercial claims regarding products, and regulatory and legal frameworks that govern the development and use of AI. This guide offers surgeons a framework with which to evaluate manuscripts that incorporate the use of AI. It provides a glossary of common terms, an overview of prerequisite knowledge to maximize understanding of methodology, and recommendations on how to carefully consider each element of a manuscript to assess the quality of the data on which an algorithm was trained, the appropriateness of the methodological approach, the potential for reproducibility of the experiment, and the applicability to surgical practice, including considerations on generalizability and scalability.

Karhunen-Loéve expansion of a set of rotated templates.

In this paper, we propose a novel method for efficiently calculating the eigenvectors of uniformly rotated images of a set of templates. As we show, the images can be optimally approximated by a linear series of eigenvectors which can be calculated without actually decomposing the sample covariance matrix.