scholarly journals Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research

2021 ◽  
Vol 15 ◽  
Author(s):  
Michael J. Crosse ◽  
Nathaniel J. Zuk ◽  
Giovanni M. Di Liberto ◽  
Aaron R. Nidiffer ◽  
Sophie Molholm ◽  
...  
Keyword(s):  
Cognitive Processing ◽  
Speech Processing ◽  
Applied Research ◽  
Worked Examples ◽  
Linear Modeling ◽  
Design Data ◽  
Eeg Data ◽  
Model Training ◽  
Methodological Considerations ◽  
Extraction Model

Cognitive neuroscience, in particular research on speech and language, has seen an increase in the use of linear modeling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits under more naturalistic conditions. However, studying clinical (and often highly heterogeneous) cohorts introduces an added layer of complexity to such modeling procedures, potentially leading to instability of such techniques and, as a result, inconsistent findings. Here, we outline some key methodological considerations for applied research, referring to a hypothetical clinical experiment involving speech processing and worked examples of simulated electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing, stimulus feature extraction, model design, model training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate the implementation of each step in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied research. In doing so, we hope to provide better intuition on these more technical points and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically rich stimuli.

scholarly journals Linear Modeling of Neurophysiological Responses to Naturalistic Stimuli: Methodological Considerations for Applied Research

2021 ◽  
Author(s):  
Michael J Crosse ◽  
Nathaniel J Zuk ◽  
Giovanni M. Di Liberto ◽  
Aaron Nidiffer ◽  
Sophie Molholm ◽  
...  
Keyword(s):  
Experimental Design ◽  
Cognitive Processing ◽  
Cognitive Neuroscience ◽  
Applied Research ◽  
Worked Examples ◽  
Linear Modeling ◽  
Design Data ◽  
Neuroscience Research ◽  
Increased Risk ◽  
Methodological Considerations

Cognitive neuroscience has seen an increase in the use of linear modelling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits within an ecologically relevant context. However, studying clinical (and often highly-heterogeneous) cohorts introduces an added layer of complexity to such modelling procedures, leading to an increased risk of improper usage of such techniques and, as a result, inconsistent conclusions. Here, we outline some key methodological considerations for applied research and include worked examples of both simulated and empirical electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing and stimulus feature extraction, model design, training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate how to implement each stage in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied cognitive neuroscience research. In doing so, we highlight the importance of understanding these more technical points for experimental design and data analysis, and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically-rich stimuli.

Integrating molecular and morphological approaches for characterizing parasite cryptic species: implications for parasitology

Parasitology ◽  
2011 ◽  
Vol 138 (13) ◽  
pp. 1688-1709 ◽  
Author(s):  
STEVEN A. NADLER ◽  
GERARDO PÉREZ-PONCE DE LEÓN
Keyword(s):  
Cryptic Species ◽  
Applied Research ◽  
Molecular Data ◽  
Theory And Practice ◽  
Parasitic Diseases ◽  
Advantages And Disadvantages ◽  
Research Programmes ◽  
Parasite Biodiversity ◽  
Operational Practices ◽  
Methodological Considerations

SUMMARYHerein we review theoretical and methodological considerations important for finding and delimiting cryptic species of parasites (species that are difficult to recognize using traditional systematic methods). Applications of molecular data in empirical investigations of cryptic species are discussed from an historical perspective, and we evaluate advantages and disadvantages of approaches that have been used to date. Developments concerning the theory and practice of species delimitation are emphasized because theory is critical to interpretation of data. The advantages and disadvantages of different molecular methodologies, including the number and kind of loci, are discussed relative to tree-based approaches for detecting and delimiting cryptic species. We conclude by discussing some implications that cryptic species have for research programmes in parasitology, emphasizing that careful attention to the theory and operational practices involved in finding, delimiting, and describing new species (including cryptic species) is essential, not only for fully characterizing parasite biodiversity and broader aspects of comparative biology such as systematics, evolution, ecology and biogeography, but to applied research efforts that strive to improve development and understanding of epidemiology, diagnostics, control and potential eradication of parasitic diseases.

scholarly journals THE EVALUATIVE RESEARCH AS A BRIDGE BETWEEN SCIENCE AND PROFESSION

2016 ◽  
Vol 2 (1) ◽  
pp. 117
Author(s):  
Santo Di Nuovo
Keyword(s):  
Social Work ◽  
Applied Research ◽  
Scientific Research ◽  
Statistical Analyses ◽  
Laboratory Research ◽  
Evidence Based ◽  
Important Goal ◽  
Evaluative Research ◽  
Definition Of ◽  
Methodological Considerations

The evaluative research is an important goal of applied research in psychology, and can constitute a link between scientific research and the definition of an evidence-based profession, in many fields of psychology: e.g., educational, social, work, clinical psychology.But to make a good evaluative research some methodological considerations are needed. First of all, the complexity of this field of study overwhelms the traditional methods based on laboratory research, which defines and manages variables, sampling, and statistical analyses in a reductive way.

Verbal Training to Improve Explanations of Conservation with Aphasic Adults

1979 ◽  
Vol 22 (4) ◽  
pp. 818-828 ◽  
Author(s):  
Laurel Dee Cooper ◽  
Seymour Rigrodsky
Keyword(s):  
Cognitive Processing ◽  
Cognitive Task ◽  
Response Facilitation ◽  
Model Training ◽  
And Control ◽  
The Relationship ◽  
Order Of Presentation ◽  
Verbal Model

Recent research has indicated a need to study the relationship between the language of the adult aphasic and his attempts at cognitive processing. Nine aphasic adults who demonstrated a minimal ability to explain conservation (as defined by Piaget), a cognitive task which they understood, were given verbal model training to improve their explanations of weight and liquid conservation. Each subject was given a pretest, an experimental condition during which explanations for weight conservation only were trained, a control condition during which subjects named pictured common objects, and a posttest. Order of presentation of the experimental and control conditions was varied. As a result of training, a greater number of explanations (quantitative improvement) and a greater number of explanatory concepts (qualitative improvement) were expressed for both the trained and nontrained conservation tasks. It is suggested that the improvement in conservation explanations is the result of “response facilitation effects” as described by Bandura. Furthermore, the improvement in conservation explanations is supportive of Schuell’s concept of impaired linguistic retrieval mechanisms in aphasia.

scholarly journals Research on Image Acquisition Method Model Based on Implicit Association Test

2020 ◽  
Vol 179 ◽  
pp. 02006
Author(s):  
Zhuen Guo ◽  
Li Lin
Keyword(s):  
Cognitive Processing ◽  
Implicit Association Test ◽  
Measurement Data ◽  
Association Test ◽  
Implicit Association ◽  
Perceptual Evaluation ◽  
Model Based ◽  
Implicit Measurement ◽  
Product Image ◽  
Extraction Model

In the process of the traditional quantitative method is easily interfered with by subjective and external environment, and cannot reflect the real emotion of users. The implicit measurement method can better reflect the cognitive of users and has good reliability in perceptual evaluation. In this paper, the implicit cognitive processing process in users’ perceptual evaluation of products is quantitatively analyzed. The correlation between product image attribute values and implicit measurement data is obtained. Thus, an image extraction model based on implicit measurement data is obtained. The implicit association test is introduced into the image extraction process, and the relationship between the implicit association test data of users and the data of product image attribute values is analyzed. Taking UAV as the analysis prototype, the image extraction model is obtained. After verification and analysis, the image extraction results are consistent with the image attribute values.

scholarly journals Fuzzy Integral With Particle Swarm Optimization for CNN-Based Motor-Imagery EEG Classification

2021 ◽  
Author(s):  
Jian-Xue Huang ◽  
Chia-Ying Hsieh ◽  
Ya-Lin Huang ◽  
Chun-Shu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Imagery ◽  
Particle Swarm ◽  
Classification Performance ◽  
Fuzzy Integral ◽  
Ensemble Classifiers ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Eeg Data ◽  
Model Training

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed framework aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

Neuropsychological Approaches to Visually-Induced Vection: an Overview and Evaluation of Neuroimaging and Neurophysiological Studies

2020 ◽  
pp. 1-34
Author(s):  
Stefan Berti ◽  
Behrang Keshavarz
Keyword(s):  
Cognitive Processing ◽  
Neural Basis ◽  
Cognitive Correlates ◽  
Research Areas ◽  
Neurophysiological Studies ◽  
History Of ◽  
Temporal And Spatial Characteristics ◽  
Methodological Considerations ◽  
Self Motion ◽  
Healthy Participants

Abstract Moving visual stimuli can elicit the sensation of self-motion in stationary observers, a phenomenon commonly referred to as vection. Despite the long history of vection research, the neuro-cognitive processes underlying vection have only recently gained increasing attention. Various neuropsychological techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have been used to investigate the temporal and spatial characteristics of the neuro-cognitive processing during vection in healthy participants. These neuropsychological studies allow for the identification of different neuro-cognitive correlates of vection, which (a) will help to unravel the neural basis of vection and (b) offer opportunities for applying vection as a tool in other research areas. The purpose of the current review is to evaluate these studies in order to show the advances in neuropsychological vection research and the challenges that lie ahead. The overview of the literature will also demonstrate the large methodological variability within this research domain, limiting the integration of results. Next, we will summarize methodological considerations and suggest helpful recommendations for future vection research, which may help to enhance the comparability across neuropsychological vection studies.

scholarly journals A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Chen ◽  
Wenlong Hang ◽  
Shuang Liang ◽  
Xuejun Liu ◽  
Guanglin Li ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Structural Information ◽  
Classification Performance ◽  
Target Domain ◽  
Alternating Direction ◽  
Learning Procedure ◽  
Eeg Data ◽  
Support Matrix ◽  
Model Training ◽  
Learning Machine

In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.

scholarly journals TDJEE: A Document-Level Joint Model for Financial Event Extraction

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 824
Author(s):  
Peng Wang ◽  
Zhenkai Deng ◽  
Ruilong Cui
Keyword(s):  
Event Extraction ◽  
Distant Supervision ◽  
Sentence Level ◽  
Financial Domain ◽  
Level Information ◽  
Financial Events ◽  
Model Training ◽  
Joint Event ◽  
Extraction Model ◽  
Document Level

Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

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