A critical evaluation of computational mechanisms of binocular disparity processing

The past decades of research in visual neuroscience have generated a large and disparate body of literature on the computation of binocular disparity in the primary visual cortex. Models have been proposed to describe specific phenomena, yet we lack a theoretical framework which is grounded in neurophysiology and also explains the effectiveness of disparity computation. Here, we examine neural circuits that are thought to play an important role in the computation of binocular disparity. Starting with the binocular energy model (Ohzawa et al. 1990), we consider plausible extensions which include suppressive mechanisms from units tuned to different phase disparities (Tanabe et al. 2011), which is formerly theorized to perform false disparity detection (Read & Cumming 2007) as well as coarse-to-fine (Menz & Freeman 2004a,b) and recurrent processing (Samonds et al. 2013). We rigorously cross-examine the consistency of these circuits with neurophysiology data including ocular dominance and binocular modulation (Ohzawa & Freeman 1990), spike-triggered analysis and temporal dynamics of disparity tuning (Tanabe et al. 2011) and attenuation to anti-correlated stimuli (Cumming & Parker 1997; Tanabe et al. 2011). We further evaluate the ability of the resulting computational models to recover depth, both theoretically and experimentally, using a dataset of natural and synthetic images. Overall, we find that a computational model which combines suppressive mechanisms by units with non-zero phase disparity, contrast normalization as well as lateral interaction between units tuned to specific combinations of phase and position disparities, seems consistent with all of the available V1 neurophysiology data and achieves the highest accuracy in real-world depth computation

Development of a Rubric to Evaluate Implementation Quality of Simulation-based Courses: A Consensus Study

A. Background When simulation-based courses fail, there are at least two reasons: the content and structure were inadequate, or the quality of the implementation process was faulty. Evidence-based, stepwise approaches to ensure the development, delivery, and evaluation of simulation-based courses are well-established. However, best practices for implementation of these courses based on implementation science, are not widely known, or applied. The purpose of this study was to employ consensus building methodology to define content for a rubric, Implementation Quality Rubric for Simulation (IQR-SIM), to evaluate the implementation quality of simulation-based courses in health professions education. B. Methods A three-round, modified Delphi process involving international simulation and implementation experts was initiated to gather and converge opinions regarding criteria for evaluating the implementation quality of simulation-based courses. Candidate items for round 1 were developed based on the Adapted Implementation Model for Simulation (AIM-SIM). The items were revised and expanded to include descriptive anchors for evaluation in round 2. The criterion for inclusion was 70% of respondents selecting an importance rating of 4 or 5/5. Round 3 provided refinement and final approval of the content and descriptive anchors. C. Results Thirty-three experts from 9 countries participated. The initial rubric of 32 items was reduced to 18 items after three Delphi rounds, resulting in the IQR-SIM: a three-point rating scale, with non-scored options “Don’t know/can’t assess” and “Not applicable”, and a comment section. D. Conclusion The IQR-SIM is an operational tool that can be used to evaluate the implementation quality of simulation-based courses and aid in the implementation process to identify gaps, monitor the process, and promote the achievement of desired implementation and learning outcomes