Bayesian Account of Perceptual Decision-Making

openBy making predictions, learning from mistakes, and updating memories to include new information, the brain enables adaptive behaviour in daily activities. For instance, in perceptual decision-making tasks, it is critical to rapidly select the best behaviours based on current sensory inputs, that are frequently ambiguous or masked by noise. Using random dot motion (RDM) tasks, previous research on perceptual decision-making emphasised the role of sensory information in directing behaviour by varying simply the stimulus coherence and analysed the data using models that more or less explicitly presuppose bottom-up processing (e.g., drift-diffusion models). However, accumulating evidence (e.g., Bayesian models and the Free Energy Principle applications) suggests that the brain approximates optimal Bayesian inference rather than simply being a passive information filter. As a result, we need to shed light on the computations involved in goal-directed decision-making, with a focus on the predictive mechanisms at work in volatile experimental contexts. Here we used a probabilistic Random Dot Kinematogram (pRDK) in which the probability of witnessing a rightward/leftward motion changes throughout the task. Furthermore, to operationalise the predictions of the left and right dot motion in each trial based on previous information, an Ideal Bayesian Observer was used. This allowed us to study top-down predictions' impact on decision-making. The behavioural analyses revealed a substantial impact on behaviour from both coherence levels and probabilistic contexts. Specifically, a significant interaction between the probability of motion and direction was found, indicating faster responses when predictions matched what was presented.By making predictions, learning from mistakes, and updating memories to include new information, the brain enables adaptive behaviour in daily activities. For instance, in perceptual decision-making tasks, it is critical to rapidly select the best behaviours based on current sensory inputs, that are frequently ambiguous or masked by noise. Using random dot motion (RDM) tasks, previous research on perceptual decision-making emphasised the role of sensory information in directing behaviour by varying simply the stimulus coherence and analysed the data using models that more or less explicitly presuppose bottom-up processing (e.g., drift-diffusion models). However, accumulating evidence (e.g., Bayesian models and the Free Energy Principle applications) suggests that the brain approximates optimal Bayesian inference rather than simply being a passive information filter. As a result, we need to shed light on the computations involved in goal-directed decision-making, with a focus on the predictive mechanisms at work in volatile experimental contexts. Here we used a probabilistic Random Dot Kinematogram (pRDK) in which the probability of witnessing a rightward/leftward motion changes throughout the task. Furthermore, to operationalise the predictions of the left and right dot motion in each trial based on previous information, an Ideal Bayesian Observer was used. This allowed us to study top-down predictions' impact on decision-making. The behavioural analyses revealed a substantial impact on behaviour from both coherence levels and probabilistic contexts. Specifically, a significant interaction between the probability of motion and direction was found, indicating faster responses when predictions matched what was presented

A predictive model for the acceptance of wearable ubiquitous activity monitoring devices

Acceptance of wearable ubiquitous activity monitoring devices that track activity has been a hot topic for the last decade. Several theories have been made, particularly how to think about the Technology Acceptance Model (TAM). These theories have been used in different situations to learn more about how people and organizations accept new technology. Even though the TAM is mature and works in different situations, there is not much published research that tries to expand its ability to predict how people will react to wearable ubiquitous activity monitoring devices. One reason for this gap could be that the TAM is based on the idea that people's acceptance behavior can only be predicted by two beliefs: Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Literature shows that PU and PEOU beliefs are not enough. This means that they may not be able to explain why people accept new things, like Activity Trackers (AT). Because of this, it is important to include any other factors that can help predict how likely people are to use activity trackers. As an extension of research on the TAM, this study created and tested two models of how people accept and use wearable ubiquitous activity monitoring devices, with two questionnaires with more than 200 respondents that shield light on the subject. The proposed models added key concepts from the research stream on how people accept information systems to the theoretical framework of the TAM and Health Information Technology Acceptance Model (HITAM). The resulting models were analyzed using a variety of statistical techniques including Structural Equation Analysis. The first model was reanalyzed via qualitative analysis with 20 interviews, and reanalyzed via another quantitative method of Artificial Neural Networks (ANN). The most significant contributions of this dissertation are: 1. The construction of two models that predict activity tracking adoption and usage. 2. Guidelines for designing activity trackers. These contributions can help promote activity trackers as an essential piece of equipment that helps monitor progress during workouts as well as other times, such as when the user is at rest or sleeping. We will see that by being continually reminded to walk about and avoid sitting for extended periods of time or doing nothing at all, this helps a person build healthy behaviors. Additionally, activity trackers should be designed to maintain a person's motivation to finish the daily activity routine, which is necessary for people to accomplish their health and fitness objectives. This thesis contributes with two quantitative models for the acceptance and use of activity trackers, and creates recommendations for different types of users.A aceitação de dispositivos ubíquos vestíveis de monitorização de atividade que rastreiam a atividade tem sido um tema cálido na última década. Várias teorias foram concebidas, principalmente como pensar o Modelo de Aceitação de Tecnologia (TAM). Essas teorias têm sido usadas em diferentes situações para aprender mais sobre como as pessoas e as organizações aceitam novas tecnologias. Conquanto o TAM seja maturo e funcione em diferentes situações, não há muitas investigações publicadas que tentem expandir a sua capacidade de prever como as pessoas reagirão a dispositivos ubíquos vestíveis de monitoramento de atividade. Uma razão para essa lacuna pode ser porque o TAM é baseado na ideia de que o comportamento de aceitação das pessoas só pode ser previsto por duas asseverações: Facilidade de Uso Percebida (PEOU) e Utilidade Percebida (PU). A literatura mostra que as asseverações nas PU e PEOU não são suficientes. Isso significa que essas duas asseverações podem não ser capazes de explicar o porquê de as pessoas aceitarem coisas novas, como monitores de atividade (AT). Por isso, é importante incluir quaisquer outros fatores que possam ajudar a prever a probabilidade de as pessoas usarem monitorizadores de atividade. Como extensão da pesquisa sobre o TAM, esta investigação criou e testou dois modelos de como as pessoas aceitam e usam dispositivos ubíquos vestíveis de monitorização de atividade, com dois questionários com mais de 200 repostas cada, que clarificam o assunto. Os modelos propostos agregaram conceitos-chave da pesquisa sobre como as pessoas aceitam os sistemas de informação ao referencial teórico do TAM e do Modelo de Aceitação de Tecnologia da Informação em Saúde (HITAM). Os modelos resultantes foram analisados usando uma variedade de técnicas estatísticas, incluindo Modelação de Equações Estruturais. O primeiro modelo foi reanalisado por meio de uma análise qualitativa com 20 entrevistas, e de novo reanalisado por meio de outro método quantitativo com Redes Neurais Artificiais (RNA). A construção de dois modelos que predizem a adoção e uso do monitorização da atividade é a contribuição mais significativa que pode ser retirada deste trabalho, juntamente com as diretrizes para o design de monitorizadores de atividade. Essas contribuições podem ajudar a promover os monitorizadores de atividade como um equipamento essencial que ajuda a monitorizar a evolução durante os treinos e em outros momentos, como quando o utilizador está em repouso ou dormindo. Ao ser continuamente lembrado para andar e evitar ficar sentado por longos períodos de tempo ou não fazer nada, isso ajuda o utilizador a construir comportamentos saudáveis. Além disso, os monitorizadores de atividade devem ser projetados para manter a motivação de uma pessoa em concluir a rotina diária de atividades, o que é necessário para que as pessoas atinjam seus objetivos de saúde e condição física. Esta tese contribui com modelos quantitativos para a aceitação e uso de monitorizadores de atividades e cria recomendações para diferentes tipos de utilizadores

Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review

Solar thermal systems (STS) are efficient and environmentally safe devices to meet the rapid increasing energy demand now a days. But it is very important to optimize their performance under required operating condition for efficient usage. Hence intelligent system-based techniques like artificial neural network (ANN) play an important role for system performance prediction in accurate and speedy way. In present paper, it is attempted to scrutinize the approach of ANN as an intelligent system-based method to accurately optimize the performance prediction of different solar thermal systems. Here, 25 research works related to various solar thermal systems have been reviewed and summarized to understand the impact of different ANN models and learning algorithms on performance prediction of STS. Using ANN, a brief stepwise summary of researchers’ work on various STS like solar air heaters, solar stills, solar cookers, solar dryers and solar hybrid systems, their predictions (results) and architectures (network and learning algorithms) in the literature till now, are also discussed here. This paper will genuinely help future researchers overview the work concisely related to solar thermal system performance prediction using various types of ANN models and learning algorithm and compare it with other global methods of machine learning. Citation: Ahmad, A., Ghritlahre, H. K., and Chandrakar, P. (2020). Implementation of ANN technique for performance prediction of solar thermal systems: A Comprehensive Review. Trends in Renewable Energy, 6, 12-36. DOI: 10.17737/tre.2020.6.1.0011

Contraction and partial contraction : a study of synchronization in nonlinear networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 121-128).This thesis focuses on the study of collective dynamic behaviors, especially the spontaneous synchronization behavior, of nonlinear networked systems. We derives a body of new results, based on contraction and partial contraction analysis. Contraction is a property regarding the convergence between two arbitrary system trajectories. A nonlinear dynamic system is called contracting if initial conditions or temporary disturbances are forgotten exponentially fast. Partial contraction, introduced in this thesis, is a straightforward but more general application of contraction. It extends contraction analysis to include convergence to behaviors or to specific properties (such as equality of state components, or convergence to a manifold). Contraction and partial contraction provide powerful analysis tools to investigate the stability of large-scale complex systems. For diffusively coupled nonlinear systems, for instance, a general synchronization condition can be derived which connects synchronization rate to net- work structure explicitly. The results are applied to construct flocking or schooling models by extending to coupled networks with switching topology. We further study the networked systems with different kinds of group leaders, one specifying global orientation (power leader), another holding target dynamics (knowledge leader). In a knowledge-based leader-followers network, the followers obtain dynamics information from the leader through adaptive learning. We also study distributed networks with non-negligible time-delays by using simplified wave variables and other contraction-oriented analysis. Conditions for contraction to be preserved regardless of the explicit values of the time-delays are derived.(cont.) Synchronization behavior is shown to be robust if the protocol is linear. Finally, we study the construction of spike-based neural network models, and the development of simple mechanisms for fast inhibition and de-synchronization.by Wei Wang.Ph.D

Biological Neuron Voltage Recordings, Driving and Fitting Mathematical Neuronal Models

The manual process of comparing biological recordings from electrophysiological experiments to their mathematical models is time-consuming and subjective. To address this problem, we have created a blended system that allows for objective, high-throughput, and computationally inexpensive comparisons of biological and mathematical models by developing a quantitative measure of likeness (error function). Voltage recordings from biological neurons, mathematically simulated voltage times series, and their transformations are inputted into the error function. These transformations and measurements are the action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels being open. The previously recorded biological voltage times series are first, translated into mathematical data to input into mathematical neurons, creating what we call a blended system. Using the sea slug Melibe Leonina\u27s swimming central pattern generator (CPG) as our circuit to compare and the source of our biological recordings, we performed a grid search of the conductance of the inhibitory and excitatory synapse found that a weighted sum of simple functions is required for a comprehensive view of a neuron\u27s rhythmic behavior. The blended system was also shown to be able to act as rhythm directors like pacemakers and drivers of Dendronotus Iris swimming interneuron (Si) cells and was able to replicate the perturbations of biological recordings. After verification steps using different configurations, calculated mean and variance of rhythmic characteristics, as well as recordings created from data augmentation. The form of data augmentation introduced can be generalized to other biological recordings or any time series. With all these tools developed and expanding the parameter dimensions a hypothesis was posited that there is a contralateral electric synapse not previously included in the Melibe CPG model

Review on solving the forward problem in EEG source analysis

Background. The aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods. While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results. It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter). In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM), the finite element method (FEM) and the finite difference method (FDM). In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative methods are required to solve these sparse linear systems. The following iterative methods are discussed: successive over-relaxation, conjugate gradients method and algebraic multigrid method. Conclusion. Solving the forward problem has been well documented in the past decades. In the past simplified spherical head models are used, whereas nowadays a combination of imaging modalities are used to accurately describe the geometry of the head model. Efforts have been done on realistically describing the shape of the head model, as well as the heterogenity of the tissue types and realistically determining the conductivity. However, the determination and validation of the in vivo conductivity values is still an important topic in this field. In addition, more studies have to be done on the influence of all the parameters of the head model and of the numerical techniques on the solution of the forward problem.peer-reviewe

Computational methods toward early detection of neuronal deterioration

In today's world, because of developments in medical sciences, people are living longer, particularly in the advanced countries. This increasing of the lifespan has caused the prevalence of age-related diseases like Alzheimer’s and dementia. Researches show that ion channel disruptions, especially the formation of permeable pores to cations by Aβ plaques, play an important role in the occurrence of these types of diseases. Therefore, early detection of such diseases, particularly using non-invasive tools can aid both patients and those scientists searching for a cure. To achieve the goal toward early detection, the computational analysis of ion channels, ion imbalances in the presence of Aβ pores in neurons and fault detection is done. Any disruption in the membrane of the neuron, like the formation of permeable pores to cations by Aβ plaques, causes ionic imbalance and, as a result, faults occur in the signalling of the neuron.The first part of this research concentrates on ion channels, ion imbalances and their impacts on the signalling behaviour of the neuron. This includes investigating the role of Aβ channels in the development of neurodegenerative diseases. Results revealed that these types of diseases can lead to ionic imbalances in the neuron. Ion imbalances can change the behaviour of neuronal signalling. Therefore, by identifying the pattern of these changes, the disease can be detected in the very early stages. Then the role of coupling and synchronisation effects in such diseases were studied. After that, a novel method to define minimum requirements for synchronicity between two coupled neurons is proposed. Further, a new computational model of Aβ channels is proposed and developed which mimics the behaviour of a neuron in the course of Alzheimer's disease. Finally, both fault computation and disease detection are carried out using a residual generation method, where the residuals from two observers are compared to assess their performance