Self-, other-, and joint monitoring using forward models

In the psychology of language, most accounts of self-monitoring assume that it is based on comprehension. Here we outline and develop the alternative account proposed by Pickering and Garrod (2013), in which speakers construct forward models of their upcoming utterances and compare them with the utterance as they produce them. We propose that speakers compute inverse models derived from the discrepancy (error) between the utterance and the predicted utterance and use that to modify their production command or (occasionally) begin anew. We then propose that comprehenders monitor other people’s speech by simulating their utterances using covert imitation and forward models, and then comparing those forward models with what they hear. They use the discrepancy to compute inverse models and modify their representation of the speaker’s production command, or realize that their representation is incorrect and may develop a new production command. We then discuss monitoring in dialogue, paying attention to sequential contributions, concurrent feedback, and the relationship between monitoring and alignment

Investigating complex networks with inverse models: analytical aspects of spatial leakage and connectivity estimation

Network theory and inverse modeling are two standard tools of applied physics, whose combination is needed when studying the dynamical organization of spatially distributed systems from indirect measurements. However, the associated connectivity estimation may be affected by spatial leakage, an artifact of inverse modeling that limits the interpretability of network analysis. This paper investigates general analytical aspects pertaining to this issue. First, the existence of spatial leakage is derived from the topological structure of inverse operators. Then, the geometry of spatial leakage is modeled and used to define a geometric correction scheme, which limits spatial leakage effects in connectivity estimation. Finally, this new approach for network analysis is compared analytically to existing methods based on linear regressions, which are shown to yield biased coupling estimates.Comment: 19 pages, 4 figures, including 5 appendices; v2: minor edits, 1 appendix added; v3: expanded version, v4: minor edit

Effectiveness of 3D Geoelectrical Resistivity Imaging using Parallel 2D Profiles

Acquisition geometry for 3D geoelectrical resistivity imaging in which apparent resistivity data of a set of parallel 2D profiles are collated to 3D dataset was evaluated. A set of parallel 2D apparent resistivity data was generated over two model structures. The models, horst and trough, simulate the geological environment of a weathered profile and refuse dump site in a crystalline basement complex respectively. The apparent resistivity data were generated for Wenner–alpha, Wenner–beta, Wenner–Schlumberger, dipole–dipole, pole–dipole and pole–pole arrays with minimum electrode separation, a (a = 2, 4, 5 and 10 m) and inter-line spacing, L (L = a, 2a, 2.5a, 4a, 5a and 10a). The 2D apparent resistivity data for each of the arrays were collated to 3D dataset and inverted using a full 3D inversion code. The 3D imaging capability and resolution of the arrays for the set of parallel 2D profiles are presented. Grid orientation effects are observed in the inversion images produced. Inter-line spacing of not greater than four times the minimum electrode separation gives reasonable inverse models. The resolution of the inverse models can be greatly improved if the 3D dataset is built by collating sets of orthogonal 2D profile

Model inversion of electrical engineering systems from bicausal bond graphs

In this paper, the application of bicausal bond graphs for model inversion of typical electrical engineering systems is emphasised. Inverse models are particularly useful for the synthesis step of the system design process. To illustrate these issues, a typical railway traction device and an Aeronautic Electro Hydrostatic Actuator are considered as case studies. From the requirements applied to the system outputs, we show how the synthesis of electrical constraints can be carried out from the inverse bicausal Bond Graph

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Application of Neural Networks for Classification of Eddy Current NDT Data

The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters given a transducer response signal. In general the governing equations and boundary conditions describing the underlying physical phenomena are complex. Consequently analytical closed form solutions can be obtained only under. strong simplifying assumptions with regard to geometry and linearity of the problem. This precludes their use as direct inverse models for solving realistic NDT problems necessitating the need for using indirect inverse models based on pattern recognition algorithms. These inverse models classify the NDT signal as belonging to one of the classes of defects stored in a data bank as shown in Fig. 1

Validation of Advanced EM Models for UXO Discrimination

The work reported here details basic validation of our advanced physics-based EMI forward and inverse models against data collected by the NRL TEMTADS system. The data was collected under laboratory-type conditions using both artificial spheroidal targets and real UXO. The artificial target models are essentially exact, and enable detailed comparison of theory and data in support of measurement platform characterization and target identification. Real UXO targets cannot be treated exactly, but it is demonstrated that quantitative comparisons of the data with the spheroid models nevertheless aids in extracting key target discrimination information, such as target geometry and hollow target shell thickness.Comment: 15 pages, 16 figure

Mathematical modelling of mass transfer in a multi-stage rotating disc contactor column

In this study, the development of an improved forward and inverse models for the mass transfer process in the Rotating Disc Contactor (RDC) column were carried out. The existing mass transfer model with constant boundary condition does not accurately represent the mass transfer process. Thus, a time-varying boundary condition was formulated and consequently the new fractional approach to equilibrium was derived. This derivation initiated the formulation of the modified quadratic driving force, called Time-dependent Quadratic Driving Force (TQDF). Based on this formulation, a Mass Transfer of A Single Drop (MTASD) Algorithm was designed, followed by a more realistic Mass Transfer of Multiple Drops (MTMD) Algorithm which was later refined to become another algorithm named the Mass Transfer Steady State (MTSS) Algorithm. The improved forward models, consisting of a system of multivariate equations, successfully calculate the amount of mass transfer from the continuous phase to the dispersed phase and was validated by the simulation results. The multivariate system is further simplified as the Multiple Input Multiple Output (MIMO) system of a functional from a space of functions to a plane. This system serves as the basis for the inverse models of the mass transfer process in which fuzzy approach was used in solving the problems. In particular, two dimensional fuzzy number concept and the pyramidal membership functions were adopted along with the use of a triangular plane as the induced output parameter. A series of algorithms in solving the inverse problem were then developed corresponding to the forward models. This eventually brought the study to the implementation of the Inverse Single Drop Multistage (ISDMS)-2D Fuzzy Algorithm on the Mass Transfer of Multiple Drops in Multistage System. This new modelling approach gives useful information and provides a faster tool for decision-makers in determining the optimal input parameter for mas

Learning coupled forward-inverse models with combined prediction errors

Challenging tasks in unstructured environments require robots to learn complex models. Given a large amount of information, learning multiple simple models can offer an efficient alternative to a monolithic complex network. Training multiple models—that is, learning their parameters and their responsibilities—has been shown to be prohibitively hard as optimization is prone to local minima. To efficiently learn multiple models for different contexts, we thus develop a new algorithm based on expectation maximization (EM). In contrast to comparable concepts, this algorithm trains multiple modules of paired forward-inverse models by using the prediction errors of both forward and inverse models simultaneously. In particular, we show that our method yields a substantial improvement over only considering the errors of the forward models on tasks where the inverse space contains multiple solution

Deep Forward and Inverse Perceptual Models for Tracking and Prediction

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA) 201