Content, granularity, and type 2 sensitivity of subjective measures of visual consciousness

According to several major theories in the field of consciousness research, the valid assessment of conscious awareness requires subjective measures, i.e. participants’ reports about their conscious experience. However, there is a considerable amount of uncertainty in the field if and how scientifically valuable data can be obtained from subjective measures. The present work empirically examines how subjective measures of conscious awareness need to be designed and applied to provide maximally useful data for empirical studies of visual consciousness. Specifically, it is investigated what contents subjective measures should require participants to report, at which granularity subjective measures ought to be recorded, and what statistical procedures should be used to quantify the relation between subjective measures and discrimination task performance. Concerning content, subjective measures that referred to the accuracy of a preceding discrimination response and subjective measures referring to participants’ visual experience of the task-relevant stimulus feature were compared during a series of visual psychophysical experiments. Subjective measures about the accuracy of the responses were associated with more liberal psychophysical thresholds: At lower stimulus quality, participants reported that they feel confident that their discrimination response was correct without reporting a visual experience of the stimulus feature. Only at greater stimulus quality, they reported that they had a visual experience of the stimulus feature in addition to being confident. Moreover, subjective measures about confidence in discrimination responses predicted task accuracy more efficiently than measures about visual experience. Finally, subjective measures of experience and task accuracy as content were compared while event-related potentials (ERP) were recorded. The earliest electrophysiological correlates of subjective measures where predictive of the fact if participants reported that they selected the response to the discrimination task based on knowledge instead of guessing, but were not yet predictive whether participants reported a clear experience over and above making the task response based on knowledge. The strongest ERP correlate of visual experience occurred a short period in time before participants responded to the discrimination task. As a consequence, it is argued that conceptual considerations are required which conscious contents are relevant for a specific research question, and subjective measures should be about the relevant contents accordingly. Concerning the granularity of subjective measures, a continuous scale and a scale with four discrete labelled categories were compared as subjective measure of conscious experience of motion. The subjective measures contained more information when participants used the continuous scale instead of the discrete scales. The greater amount of information provided by continuous scales rendered subjective measures more predictive of task accuracy and enhanced internal consistency. Regarding the statistical procedure to quantify the relation between subjective measures and task performance, it was found that logistic regression is a suboptimal method because the relationship between subjective measures and the transformed accuracy was frequently not linear. In contrast, meta-da, a measure of the relationship between subjective reports and task accuracy derived from signal detection theory (SDT), provided the most consistent results across all studies. Overall, it is concluded that subjective measures are suited to provide highly useful data to address non-trivial research questions for the scientific study of consciousness: As prerequisite, the content of a subjective measures should be tailored to the current research question. In addition, the problem of a lacking objective standard can be addressed by using the relation between subjective measures and task performance as a reference frame

Content, granularity, and type 2 sensitivity of subjective measures of visual consciousness

According to several major theories in the field of consciousness research, the valid assessment of conscious awareness requires subjective measures, i.e. participants’ reports about their conscious experience. However, there is a considerable amount of uncertainty in the field if and how scientifically valuable data can be obtained from subjective measures. The present work empirically examines how subjective measures of conscious awareness need to be designed and applied to provide maximally useful data for empirical studies of visual consciousness. Specifically, it is investigated what contents subjective measures should require participants to report, at which granularity subjective measures ought to be recorded, and what statistical procedures should be used to quantify the relation between subjective measures and discrimination task performance. Concerning content, subjective measures that referred to the accuracy of a preceding discrimination response and subjective measures referring to participants’ visual experience of the task-relevant stimulus feature were compared during a series of visual psychophysical experiments. Subjective measures about the accuracy of the responses were associated with more liberal psychophysical thresholds: At lower stimulus quality, participants reported that they feel confident that their discrimination response was correct without reporting a visual experience of the stimulus feature. Only at greater stimulus quality, they reported that they had a visual experience of the stimulus feature in addition to being confident. Moreover, subjective measures about confidence in discrimination responses predicted task accuracy more efficiently than measures about visual experience. Finally, subjective measures of experience and task accuracy as content were compared while event-related potentials (ERP) were recorded. The earliest electrophysiological correlates of subjective measures where predictive of the fact if participants reported that they selected the response to the discrimination task based on knowledge instead of guessing, but were not yet predictive whether participants reported a clear experience over and above making the task response based on knowledge. The strongest ERP correlate of visual experience occurred a short period in time before participants responded to the discrimination task. As a consequence, it is argued that conceptual considerations are required which conscious contents are relevant for a specific research question, and subjective measures should be about the relevant contents accordingly. Concerning the granularity of subjective measures, a continuous scale and a scale with four discrete labelled categories were compared as subjective measure of conscious experience of motion. The subjective measures contained more information when participants used the continuous scale instead of the discrete scales. The greater amount of information provided by continuous scales rendered subjective measures more predictive of task accuracy and enhanced internal consistency. Regarding the statistical procedure to quantify the relation between subjective measures and task performance, it was found that logistic regression is a suboptimal method because the relationship between subjective measures and the transformed accuracy was frequently not linear. In contrast, meta-da, a measure of the relationship between subjective reports and task accuracy derived from signal detection theory (SDT), provided the most consistent results across all studies. Overall, it is concluded that subjective measures are suited to provide highly useful data to address non-trivial research questions for the scientific study of consciousness: As prerequisite, the content of a subjective measures should be tailored to the current research question. In addition, the problem of a lacking objective standard can be addressed by using the relation between subjective measures and task performance as a reference frame

A Brief Note on Building Augmented Reality Models for Scientific Visualization

Augmented reality (AR) has revolutionized the video game industry by providing interactive, three-dimensional visualization. Interestingly, AR technology has only been sparsely used in scientific visualization. This is, at least in part, due to the significant technical challenges previously associated with creating and accessing such models. To ease access to AR for the scientific community, we introduce a novel visualization pipeline with which they can create and render AR models. We demonstrate our pipeline by means of finite element results, but note that our pipeline is generally applicable to data that may be represented through meshed surfaces. Specifically, we use two open-source software packages, ParaView and Blender. The models are then rendered through the platform, which we access through Android and iOS smartphones. To demonstrate our pipeline, we build AR models from static and time-series results of finite element simulations discretized with continuum, shell, and beam elements. Moreover, we openly provide python scripts to automate this process. Thus, others may use our framework to create and render AR models for their own research and teaching activities

Non-collinear spin states in bottom-up fabricated atomic chains

Non-collinear spin states with unique rotational sense, such as chiral spin-spirals, are recently heavily investigated because of advantages for future applications in spintronics and information technology and as potential hosts for Majorana Fermions when coupled to a superconductor. Tuning the properties of such spin states, e.g., the rotational period and sense, is a highly desirable yet difficult task. Here, we experimentally demonstrate the bottom-up assembly of a spin-spiral derived from a chain of Fe atoms on a Pt substrate using the magnetic tip of a scanning tunneling microscope as a tool. We show that the spin-spiral is induced by the interplay of the Heisenberg and Dzyaloshinskii-Moriya components of the Ruderman-Kittel-Kasuya-Yosida interaction between the Fe atoms. The relative strengths and signs of these two components can be adjusted by the interatomic Fe distance, which enables tailoring of the rotational period and sense of the spin-spiral.Comment: 16 pages, 5 figure

Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields

Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these complex materials with high accuracy while satisfying physics-based constraints. However, most of these approaches disregard the uncertainty in the estimates and the spatial heterogeneity of these materials. In this work, we leverage recent advances in generative models to address these issues. We use as building block neural ordinary equations (NODE) that -- by construction -- create polyconvex strain energy functions, a key property of realistic hyperelastic material models. We combine this approach with probabilistic diffusion models to generate new samples of strain energy functions. This technique allows us to sample a vector of Gaussian white noise and translate it to NODE parameters thereby representing plausible strain energy functions. We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries. We extensively test our method with synthetic and experimental data on biological tissues and run finite element simulations with various degrees of spatial heterogeneity. We believe this approach is a major step forward including uncertainty in predictive, data-driven models of hyperelasticityComment: 22 pages, 11 figure

Rigid, Complete Annuloplasty Rings Increase Anterior Mitral Leaflet Strains in the Normal Beating Ovine Heart

Background-Annuloplasty ring or band implantation during surgical mitral valve repair perturbs mitral annular dimensions, dynamics, and shape, which have been associated with changes in anterior mitral leaflet (AML) strain patterns and suboptimal long-term repair durability. We hypothesized that rigid rings with nonphysiological three-dimensional shapes, but not saddle-shaped rigid rings or flexible bands, increase AML strains

Python FPGA Programming with Data-Centric Multi-Level Design

Although high-level synthesis (HLS) tools have significantly improved programmer productivity over hardware description languages, developing for FPGAs remains tedious and error prone. Programmers must learn and implement a large set of vendor-specific syntax, patterns, and tricks to optimize (or even successfully compile) their applications, while dealing with ever-changing toolflows from the FPGA vendors. We propose a new way to develop, optimize, and compile FPGA programs. The Data-Centric parallel programming (DaCe) framework allows applications to be defined by their dataflow and control flow through the Stateful DataFlow multiGraph (SDFG) representation, capturing the abstract program characteristics, and exposing a plethora of optimization opportunities. In this work, we show how extending SDFGs with multi-level Library Nodes incorporates both domain-specific and platform-specific optimizations into the design flow, enabling knowledge transfer across application domains and FPGA vendors. We present the HLS-based FPGA code generation backend of DaCe, and show how SDFGs are code generated for either FPGA vendor, emitting efficient HLS code that is structured and annotated to implement the desired architecture

An introduction to the Ogden model in biomechanics: benefits, implementation tools and limitations

Constitutive models are important to biomechanics for two key reasons. First, constitutive modelling is an essential component of characterizing tissues' mechanical properties for informing theoretical and computational models of biomechanical systems. Second, constitutive models can be used as a theoretical framework for extracting and comparing key quantities of interest from material characterization experiments. Over the past five decades, the Ogden model has emerged as a popular constitutive model in soft tissue biomechanics with relevance to both informing theoretical and computational models and to comparing material characterization experiments. The goal of this short review is threefold. First, we will discuss the broad relevance of the Ogden model to soft tissue biomechanics and the general characteristics of soft tissues that are suitable for approximating with the Ogden model. Second, we will highlight exemplary uses of the Ogden model in brain tissue, blood clot and other tissues. Finally, we offer a tutorial on fitting the one-term Ogden model to pure shear experimental data via both an analytical approximation of homogeneous deformation and a finite-element model of the tissue domain. Overall, we anticipate that this short review will serve as a practical introduction to the use of the Ogden model in biomechanics. This article is part of the theme issue 'The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity'.R21 HL161832 - NHLBI NIH HHSAccepted manuscrip

Toma de decisiones científica en la ingeniería de software mediante inteligencia computacional y análisis de datos

La adopción de herramientas formales que complementen la experiencia y el buen juicio en las distintas actividades de un proceso de desarrollo de software todavía es un pendiente dentro la industria del software. La falta de conocimientos respecto de enfoques realistas para resolver problemas de la IS y la falta de herramientas software que auxilien a los tomadores de decisiones utilizando tales enfoques son dos carencias que pueden explicar las dificultades en esta adopción. Las líneas de investigación aquí propuestas tienden a suplir ambas. Para esta tarea se propone la utilización de tanto técnicas comprendidas en lo que se conoce como Inteligencia Computacional (IC), dentro de las cuales se encuentran la teoría de conjuntos difusos, las redes neuronales y la computación evolutiva, como también de herramientas de la Ciencia de Datos, incluyendo técnicas de aprendizaje automático, estadísticas y visualización de datos, entre otros. Estas técnicas son capaces de brindar la flexibilidad necesaria para crear métodos y modelos que sean tolerantes a la imprecisión, la falta de información y la aproximación, características que le son propias a los contextos de decisión en la IS.Eje: Ingeniería de Software.Red de Universidades con Carreras en Informátic