Multinomial Processing Models in Visual Cognitive Effort Diagnostics

The pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to diagnose the cognitive effort involved with a task that was presented visually. A multinomial processing tree (MPT) was used as an analytical tool in order to disentangle and predict separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. An MPT model describes observed response frequencies from a set of response categories. The parameter values of an MPT model are the probabilities of moving from latent state to the next. An EM algorithm was used to estimate the parameter values based on the response frequency of each category. This results in a parsimonious, causal model that facilitates in the understanding the pupillary response to cognitive load. This model eventually could be instrumental in bridging the gap between human vision and computer vision

Robust Understanding of Motor Imagery EEG Pattern in Voice Controlled Prostatic Arm Design

poster abstractIntroduction: Understanding neural mechanism of communication between human and machine has become more interesting research issue in last few decades. One of the most motivating purposes is to help the people with motor disabilities. This excites researchers to work on the interaction between brain-computer-interfacing (BCI) systems, which in turn needs a fast and accurate algorithm to decode the commands in the brain or electroencephalogram (EEG) signals. EEG signals are very noisy and contain several types of artifacts, so it would be very important to use efficient methods to train the BCI system. Aims and Goals: The goal of this project is to train an intelligent system based on the information in the sample EEG data. This system is going to predict the person’s intention in future experiments with new EEG data. Finally, this project can be used in controlling a moving object like a robot, a wheelchair, or many other devices. Data Acquisition and methods: In this project, we are working with the EEG signals taken from 20 subjects thinking about English vowels \a\, \e\, \i\, \o\, and \u\. This means we can define only 5 clusters, which contain all signals with similar features. We are going to use part of the signals for training and the rest for testing. In training section, we have to first preprocess the data, and then categorize it into 5 clusters. Robust Principle Component Analysis (PCA) helps us to analyze the data to extract the features. Afterwards based on principle component features of signals, we employ a Hidden Markov Model (HMM) classifier to send similar signals to the same cluster. As EEG data is a randomly variant signal, we are using Hybrid HMM classifier for classification of EEG pattern. Our Initial results are promising in robust understanding of auditory command, which is been explored from EEG pattern analysis

Smart Unit Care for Pre Fall Detection and Prevention

Generally falls may occur from moving or resting postures. This may include slipping from bed and fall from a sitting, or from running or walking. The pre-fall is a non-equilibrium state of human position that may lead to serious injuries, and may negatively impact the quality life condition, particularly for elders. Physical disabilities resulting from the fall incidences may lead to high costs involved with the healing process. In this work, an embedded sensor system using Arduino micro-controller was utilized to coordinate the data received from accelerometer and gyroscope. For a given threshold voltage and fall pattern, the fall decision is made by the microcontroller, citing an incoming fall. The study addresses the number of sensors to be coordinated for enhancing probability of receiving a real fall. Sensors are suggested to be placed on the human body within a belt, and safety devices at human body as well as incorporated in a smart room will be coordinated with the processor commands. Near 150 ms time frame was detected from the simulation results, suggesting a safety device to be triggered and activated for protection within this time frame. This paper discusses the research parameters such as response time for the device activation and interfacing the microcontroller to airbag switch, and means of activating the safety devices within the sharp edges in the smart unit care to minimize the impact of the fall injuries

Neural Mechanisms of Pupillary Dynamics and Cognitive Effort

poster abstractThe pupillary response has been used to measure mental workload because of its sensitivity to stimuli and high resolution. The goal of this study was to understand interconnections between the visual or auditory pathway and the resulting pupillary response relative to cognitive effort. A multinomial processing tree was used as a diagnostic tool in order to disentangle and measure separate cognitive processes, with the resulting output being a change in pupil diameter. This model was fitted to previous test data related to the pupillary response when presented a mental multiplication task. Two models were derived as a result: a subject response category tree and a pupil dilation response category tree. One tree compares the visual and aural pathways, while the other compares latent processes within the visual pathway. This results in a parsimonious model that facilitates in the understanding of the neural interconnections involved with the pupillary response to cognitive load

Developing Intelligent Negotiation System

poster abstractAbstract Negotiation has become an important aspect of our daily lives. Humans negotiate over a phone, face to face meeting and verbal and non-verbal activities. With the advent of intelligence system research, the requirement of efficient negotiation system became a prime issue. It has a number of applications including collaborating cyber human interaction, e-commerce negotiation and intelligent shared behavior study. Adapting game theory of mind concept in intelligent negotiation protocol implementation may make the future cyber system robust, social and adaptive. Keeping in mind the user’s policies and an intention to gain the maximum profit, we introduced the hybrid negotiation system to make a system robust and more useful. User’s intention to gain the maximum profit is considered important to figure out the opponent’s policies so that the system can make a right automatic decision. Based on our initial literature review, game theory of mind can be a good choice in sub-optimal intelligent negotiation system design. Therefore, a system based on game theory of mind is under design process that is being evaluated on Yahoo marketing data set

Semi-aural Interfaces: Investigating Voice-controlled Aural Flows

To support mobile, eyes-free web browsing, users can listen to ‘playlists’ of web content— aural flows . Interacting with aural flows, however, requires users to select interface buttons, tethering visual attention to the mobile device even when it is unsafe (e.g. while walking). This research extends the interaction with aural flows through simulated voice commands as a way to reduce visual interaction. This paper presents the findings of a study with 20 participants who browsed aural flows either through a visual interface only or by augmenting it with voice commands. Results suggest that using voice commands reduced the time spent looking at the device by half but yielded similar system usability and cognitive effort ratings as using buttons. Overall, the low-cognitive effort engendered by aural flows, regardless of the interaction modality, allowed participants to do more non-instructed (e.g. looking at the surrounding environment) than instructed activities (e.g. focusing on the user interface)

A content analysis of research on technology use for teaching mathematics to students with disabilities: word networks and topic modeling

Authors of article conduct a content analysis of research on technology use for teaching mathematics to students with disabilities. They applied word networks and structural topic modeling of 488 studies published from 1980 to 2021. Results showed that the words “computer” and “computer-assisted instruction” had the highest degree of centrality in the 1980s and 1990s, and “learning disability” was another central word in the 2000s and 2010s

Modeling Cognitive Ability-Demand Gaps in Collaborative Sense-making and Designing Assistive Technology Solutions

Sense-making is the ability to connect novel information to the familiar schema (or adapt to an unknown environment) and selection of actions. It can be viewed as a continuous effort to bridge the gap between human ability and agent’s task demand in the context of human-agent (system) interaction. A range of the gaps can be considered in the context of human-agent interaction depending on the agent’s role and functions. We focus on cognitive ability-demand gap for the sake of simplicity as it is critical in designing technology solutions that are adaptive, assistive, and can potentially enhance users experience through collaborative sense-making. The main goal of this study is to pursue new avenues in designing assistive solutions for people with varying degrees of disability. Every disability is unique and effective technology solution would require “assistive thinking” and “collaborative sense-making” between the user and the system (agent). The key idea is to develop a suite of techniques to model the ability-demand gap in order to have a deeper insight about human-agent interaction and leverage it in designing assistive technology solutions. The key objectives are to: (a) model the ability-demand gap in terms of cognitive ability using latent response theories, (b) establish the association of model parameters with cognitive resources and cognitive task demands in gap modeling, and (c) use the gap model for collaborative sense-making to design and develop novel assistive technology solutions. The proposed research connects psychometric ability and task difficulty parameters using the latent response model with the human ability and the task demand, respectively. This connection allows to model ability-demand gap using one parameter Item Response Model (IRM). A natural extension for polytomous response using graded response model (GRM) was also used to evaluate performance on complex cognitive tasks. Pilot studies were performed to estimate ability parameter using simple cognitive task (e.g., simple mental multiplication). To further the understanding, two types of abilities (primary and secondary) a dual task scenario (e.g., complex collaborative task) was considered. Discrepancy in secondary task performance was found to be related to the gap. A variation of response latitude around the particular difficulty level (response attitude) was considered as gap value. With a combination of task response attitude, latitude and response time – we propose a 3D response model of gap estimation. To investigate the source of low cognitive performance in terms of mental resource allocation with task ability and demand multiple resource theory with dynamic shift of working memory resources was considered in precision of mental resource computation, which is considered as a direct correlation to mental workload. Thus, the range of score might represent the mental resource level gap. To study deeper analysis of cognitive task performance that reflects cognitive and collaborative ability-demand gap, Maximal Information Coefficient and Maximal Aasymmetry Score between ability and demand vectors are considered. Studies were performed to understand the effect of ability demand gap in collaborative sense making, cognitive dissonance and overload to advance the concept of assistive thinking in designing technology solutions for people with disability. This research expected to have an increased understanding of the parameters to have a deeper insight about collaborative sense making, provide effective feedback, classification of agent’s roles in human-agent interaction and potentially transform assistive technology

Minority Cyber-CREWS: Minority Community Cybersecurity Research, Educational Engagement, Workforce Development, and Security Best Practices

Data management plan for the grant, "Minority Cyber-CREWS: Minority Community Cybersecurity Research, Educational Engagement, Workforce Development, and Security Best Practices." This project aims to improve cybersecurity research, education, and collaboration capabilities for the University of North Texas (UNT), a minority-serving institution (MSI) in the rapidly growing and diverse Dallas-Fort Worth (DFW) metropolitan area