The Assessment of Senior-Level Nursing Students’ Knowledge Regarding Informal Caregiver Role Strain and the Presence of Role Strain in Informal Caregivers of Dementia Patients

The goal of this study was to assess senior-level nursing students’ knowledge of informal caregivers of dementia patients, their confidence in their ability to work with informal caregivers, and how well they believe their nursing program prepared them to assist informal caregivers. Seventeen (n = 17) Bachelor of Science (BSN) nursing students participated in the study. A twenty-four-question survey was emailed to both junior- and senior-level students in the five-semester BSN program at the University of Southern Mississippi (USM). Responses from students of different levels were collected to determine if an accurate understanding of informal caregivers of dementia patients was more prevalent in senior-level students when compared to junior-level students. The survey included a total of four question sets. The first question set required students to select the semester of the BSN program in which they were enrolled at the time of taking the survey. After selecting their semester within the program, students were asked five questions regarding their attitude towards caregivers, five multiple-choice questions assessing the accuracy of their knowledge of caregivers, eight questions focusing on their beliefs on nurses’ role when working with caregivers, and five questions determining how well they felt their BSN program had prepared them to support caregivers. The majority of study participants believed they were well-prepared to collaborate with informal caregivers in clinical practice. However, the students’ responses to the multiple-choice questions assessing the accuracy of their knowledge indicate that further education on informal caregivers of dementia patients may be required

Bayesian model comparison and distinguishability

International audienceThis paper focuses on Bayesian modeling applied to the experimental methodology. More precisely, we consider Bayesian model comparison and selection, and the distinguishability of models, that is, the ability to discriminate between alternative theoretical explanations of experimental data. We argue that this last concept should be central, but is difficult to manipulate with existing model comparison approaches. Therefore, we propose a preliminary extension of the Bayesian model selection method that incorporates model distinguishability, and illustrate it on an example of modeling the planning of arm movements in humans

BBPRM: a behavior-based probabilistic roadmap method

International audienceThis paper focuses on the path planning problem. We offer an alternative to the probabilistic roadmap methods, from the perspective of modeling human or animal planning. In this context, hierarchies of representations are used to break down high-dimensional configuration spaces. We propose an approach for roadmap generation where low-level behaviors are used as articulations between level of the hierarchy. We also show how the obtained roadmap better represents low-level sensorimotor capabilities of the robot

Multiple object manipulation: is structural modularity necessary? A study of the MOSAIC and CARMA models

International audienceA model that tackles the Multiple Object Manipulation task computationally solves a higly complex cognitive task. It needs to learn how to identify and predict the dynamics of various physical objects in different contexts in order to manipulate them. MOSAIC is a model based on the modularity hypothesis: it relies on multiple controllers, one for each object. In this paper we question this modularity characteristic. More precisely, we show that the MOSAIC convergence during learning is quite sensitive to parameter values. To solve this issue, we define another model (CARMA) which tackles the manipulation problem with a single controller. We provide experimental and theoretical evidence that tend to indicate that non-modularity is the most natural hypothesis

Bayesian modeling of human performance in a visual processing training software

International audienceDyslexia is a deficit of the identification of words, which is thought to be a consequence of different possible cognitive impairments. Recent data suggest that one of these might be a specific deficit of the visual attention span (VAS). We are developing a remediation software for dyslexic children that focuses on the VAS and its training. A central component of this software is the estimation of the performance of a given participant for all possible exercises. We describe a preliminary probabilistic model of participant performance, based on Bayesian modeling and inference. We mathematically define the model, making explicit underlying generalization hypotheses. The model yields a computation of the most probable predicted performance space, and, as a direct extension, an exercise selection strategy

Common Bayesian Models for Common Cognitive Issues

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discusse

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach

International audienceThe remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the Central Nervous System selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way

The effects of time-variance on impedance measurements: examples of a corroding electrode and a battery cell

When performing electrochemical impedance spectroscopy (EIS) measurements on a system, we must make sure it fulfills certain conditions. One of them is that it should be stationary that is to say, steady-state and time-invariant. Commonly studied systems are time-variant, for example a corroding electrode or a battery under operation. A corroding electrode sees its polarization resistance decrease with time. A passivating electrode sees its polarization resistance increase with time. These phenomena cause a deformation of the Nyquist impedance at low frequencies. This result was first simulated and validated by experimental measurements on a corroding steel sample undergoing uniform corrosion. The effect of performing impedance measurements on a discharging battery was also shown. Several methods are available to check and correct time-variance. The nonstationary distortion (NSD) indicator is used to separate valid and invalid data samples and the so called “4D impedance” method can easily produce instantaneous impedance data

Entre l'ici et l'ailleurs : Louis-Philippe Dalembert l'aède vagabond

International audienceDe manière introductive, Dominique Diard, co-organisatrice de la Journée d’Études, caractérise la poétique dalembertienne

Bayesian Robot Programming

International audienceWe propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics