Augmented Cognition

Task forces of expert knowledge workers would benefit from more advanced web tools supporting activity awareness and social sensemaking. This paper proposes the design of a task force workspace, which is under development. It introduces the problem through a scenario, specifies requirements, illustrates a modeling approach and the mockups of the functions in the proposed workspace. Design issues and future work are finally discussed.

It is of prime importance in everyday human life to cope with and respond appropriately to events that are not foreseen by prior experience. Machines to a large extent lack the ability to respond appropriately to such inputs. An important class of unexpected events is defined by incongruent combinations of inputs from different modalities and therefore multimodal information provides a crucial cue for the identification of such events, e.g., the sound of a voice is being heard while the person in the fieldof-view does not move her lips. In the project DIRAC ("Detection and Identification of Rare Audio-visual Cues") we have been developing algorithmic approaches to the detection of such events, as well as an experimental hardware platform to test it. An audio-visual platform ("AWEAR" -audio-visual wearable device) has been constructed with the goal to help users with disabilities or a high cognitive load to deal with unexpected events. Key hardware components include stereo panoramic vision sensors and 6-channel worn-behind-the-ear (hearing aid) microphone arrays. Data have been recorded to study audio-visual tracking, a/v scene/object classification and a/v detection of incongruencies.

Twelve individuals took part in an experiment which investigated the feasibility of using physiological sensors to apply a pacing mitigation strategy to help prevent cognitive-overload-induced performance declines. The participants performed tasks using a simulated Tactical Tomahawk Weapons Control System (TTWCS) while wearing electroencephalograph (EEG), electrocardiograph (EKG), and galvanic skin response (GSR) sensors. The tasks, retargeting missiles in response to emergent targets and responding to alerts in the form of questions about the ongoing strike, were performed in the context of low, medium, and high workload scenarios. In the control condition, data from the sensors were collected, but not used to influence task presentation. In the experimental condition, the collected data were used to apply the pacing mitigation-that is, to determine the appropriate times to present the interrupting alert tasks. Analysis revealed that using sensor data to influence task presentation led to a 66.8% reduction in the number of erroneous responses to alerts, a 34.6% decrease in decision making time for low workload scenarios and an overall reduction in decision making time of 20.4%, and a 25.5% increase in the number of missiles that participants could handle simultaneously. These results indicate that human performance may be improved by monitoring physiological responses and intervening before cognitive overload can cause performance degradation. This research effort is relevant to future TTWCS development because the cognitive workload demands placed on TTWCS operators are expected to increase as new capabilities of Tomahawk missiles are introduced and because military reduced manning initiatives will continue to require that job functions be performed by fewer personnel. However, before the technology which was tested during this experiment could be deployed in an operational environment, physiological sensors must become more comfortable, accurate and mobile and less sensitive to inter-individual variation. Furthermore, more detailed task analyses are needed to identify the specific task components within the TTWCS environment that will yield the greatest performance gain. This effort, combined with other additional research, could yield performance improvements in the operational environment that exceed those observed during the laboratory-based experiment reported here.

Abstract: This effort focused on developing EEG-derived indicators of verbal versus spatial working memory load. A wireless EEG headset acquired data during execution of both simple and complex tasks associated with a Tactical Tomahawk Weapons Control System ...

The development of robust, accurate, real-time cognitive state gauges, based upon operators' physiological data, is an essential pre-requisite to realizing performance improvements through augmented cognition technology. However, gauges that can identify undesirable cognitive states are only useful if corrective action can be taken when the gauges detect problematic states. Thus, in designing augmented cognition systems, it is equally essential to develop mitigation strategies that help operators avoid and/or move out of cognitive states that can cause degradations in operator performance. This report addresses three aspects of mitigation strategy design that need to be considered when deciding what systems should do in response to changes in operator cognitive state: 1) interaction of mitigation strategies with general human-computer interaction principles; 2) task-domain dependency; and 3) correlation between mitigation strategies and specific combinations of gauge values. The treatment of each issue is grounded in specific examples from the design and implementation of multiple augmented cognition systems.

Past work concerning the design and implementation of voice-operated personal assistants that are easy to configure and apply without directly worrying about the sophistication of leveraging Large Language Models or Neural Networks has been conducted at Makerere University via the use of intelligent parsing of user voice queries against simple plain-text Question-Answer Knowledge Bases (QAKBs). This work was part of the VOSA project, and was successfully presented at SE2024 in Copenhagen. However, one glitch in this Artificial Assistant model design is that the accuracy and relevance of the answers provided by the robot are so tightly coupled to the literal phrasing and expression of answers (and alternative answers) in the underlying manually prepared QA entries that define/compose a knowledge model against which the bot can correctly respond to questions about a particular domain. In reality though, such an approach can become too limiting; the authors of a QAKB need to know/anticipate all/most possible ways a user might pose a question to the robot in order to correctly match an entry in the KB; handling of answers that need to contain dynamic information (such as date and time, location, prices, weather and such) isn't easily catered for by the flat, literal and static JSON-formatted QAKB structures used in VOSA-like assistants much as they retain the advantage of being simpler for humans to reason about or tweak than most current alternatives using human-opaque structures such as use of matrices of probabilities in Hidden Markov [Knowledge] Models (HMKMs) or use of thousands if not millions or billions of weights and activation functions in Neural Network Models (NNMs). With the recent emergence of a feature-complete general-purpose Text Processing language, TEA, which, by its lightweight nature could allow for easy integration into existing or future intelligent systems, an idea for how VOSA-like Artificial Assistants could be further enhanced in terms of their cognitive processing power and efficiency is by leveraging smarter QAKB models that also allow for some runtime/just-in-time reprogramming/generation of answers to suit user-context and perhaps even add multi-turn QA Processing that is currently missing in VOSA but which is somewhat necessary given the state-of-the-art in assistant chatbot technologies across the industry. So, how might this better QAKB technology be realized? One idea is to add a TEA program processor inside of the VOSA client engine/architecture, and then, to allow for designers or authors of VOSA-targeting QAKBs to extend their QA-entries with specially crafted, domain relevant mini-programs or perhaps QAKB-wide QA-preprocessing Macro Programs either of which are written using the succinct, terse but powerful text processing language of TEA, the Transforming Executable Alphabet. This then would allow for VOSAC to sometimes take a user's query and the active QAKB, and then dynamically process it relative to preprocessing instructions embedded in the QAKB, and thus compile new or better answers to user queries on-the-fly and in a manner perhaps more straightforward and more transparent than leveraging pre-trained models or merely falling back to over-the-web queries to a search engine as the VOSA paper had originally proposed. This research shall also usher in a whole new approach to designing and programming Artificial Intelligence personal assistants that's different from approaches leveraging HMKMs, NNMs or just basic QAKBs and/or ontologies. Consider this a research proposal abstract.

This paper aims at describing the requirements of an off-highway human-machine system able to recognize potential risky situations and consequently prevent them. The developed methodology is based on two techniques derived from the field of human factors studies, namely the Hierarchical Task Analysis (HTA) and the Function Allocation (FA), which have been integrated and revised to suit the specific domain of off-highway machinery. The paradigms of adaptive automation and persuasive technology will be followed in the design process. After the off-highway domain analysis a system aiming at improving operator and machine safety is presented. The information system extends the human intelligence monitoring the stability of the machine.

In this paper the topic of the augmented cognition applied to the driving task, and specifically to the steering maneuver, is discussed. We analyze how the presence of haptic feedback on the steering wheel could help drivers to perform a visually-guided task by providing relevant information like vehicle speed and trajectory. Starting from these considerations, a Context-Dependant Steering Wheel force feedback (CDSW) had been developed, able to provide to the driver the most suitable feeling of the vehicle dynamics according to the driven context. With a driving simulator the CSWD software had been tested twice and then compared with a traditional steering wheel.

Chapter 2, “Electroencephalography (EEG)” (by Suraj Sood) is about electroencephalography as it is used in augmented cognition. Before background on how electroencephalography works is given, a short philosophical discussion clarifies why electroencephalography is relevant to cognition. More technical details about electroencephalography—including its equipment and implementation—are given before the discussion turns to how the neurotechnology has been used to study individual differences. In the last place, the neurotechnology is situated in a broader landscape of authorship and application, including varieties of electroencephalography outside of augmented cognition. Lastly, electroencephalography is situated back into human-computer interaction. Throughout the chapter, images showing how electroencephalography is read and used are shown.

Science involving the human brain, psychology, and cognition has progressed sufficiently that the technology exists to develop a mutually beneficial exchange of information between a human and an AI. Dubbed "AI Symbiosis," this process enables positive feedback between humans and adaptive computer algorithms in which both human and AI would "learn" how to perform tasks more efficiently than either could alone. Several new technologies and inventions allow a vast array of augmented input and/or output between humans and AI, including mental activity wirelessly operating computers, manipulation of targeted neurons with or without implants, non-invasive, surface-level implants the size of a coin transmitting real-time neural activity of senses, real-time video feed of human mental images, and estimation of thoughts and emotions. A research project is planned to study students' divided attention when they are learning content in on-line environments. The research will target eye-tracking, click timing, and task performance data to determine the levels of impact divided attention has on student learning. We believe that this line of research will also inform best practices in on-line instructional settings.

Resumen La presencia del coronavirus Coronavirus SARS-CoV-2, ha generado una pandemia de COVID-19 que ha alterado la normalidad alrededor del mundo. Sin una preparación estandarizada en el sector educativo para enfrentar esta nueva situación, los maestros de educación básica, y en especial para este estudio de educación secundaria de la materia de física, han tenido que afrontar los primeras semanas de clase con problemáticas relacionadas con el cumplimiento de los programas de física, el mantener la atención de los grupos y de los alumnos, el sacar provecho de los programas que la Secretaría de Educación Pública han puesto a disposición de los alumnos. Lo anterior plantea una situación estresante para los maestros, mismos que han no se dimensionan tanto en sus repercusiones inmediatas como mediatas tanto en la calidad de vida de los profesores, así como en la calidad educativa. En este trabajo se presentan los resultados de estudios realizados a profesores con el dispositivo MUSE 2®, mismo que arroja datos de la actividad cerebral a través de registros electroencefalográficos tanto en una línea basal, así como en situación de enseñanza virtual. Los metadatos obtenidos permiten arrojar gráficas que muestran en 3D de la actividad cerebral representada en modelos anatómicos del cerebro, mostrando las ondas cerebrales (delta, theta, alpha, beta, beta2, y gama) como significativas en patrones de estrés (en especial beta.

Human errors cause the majority of aviation accidents. Augmented cognition and automation systems enhance pilot performance by evaluating system limitations and flight precision and performance. This study examines the human-machine interface in cockpit design using the tenets of augmented cognition and automation systems theory in terms of task allocation, attentional resources, and situational awareness. The study compares how these principles apply to and interact with each other and with a human/pilot in a closed-loop system. We present a method for integrating augmented cognition systems into airplane flight management systems. We demonstrate systems enhancement with an experiment in which test pilots flew two simulated flights, once without and once with an augmented cognition system. We measured pilot and airplane performance, pilots' situational awareness, workload management, pilots' use of cockpit checklists, and flight precision along four axes: (1) altitude, (2) course, (3) radial/bearing and heading, and (4) airspeed.

Electroencephalography based brain computer interface has enabled communication of human's intention to a computer directly from the brain by decoding signatures that relay the intention information. Error-related potential has been adopted as a signature for natural communication and performance monitoring, among others. In this work, we investigate the use of error-related potential as an input channel to transfer human preference of a strategical advisory to a computer. Air traffic control task was used as a case study to make an empirical inquiry of error-related potential for higher level cognitive tasks (i.e. situation awareness in air traffic control tasks). The experimental task requires the subjects to monitor and assess air traffic scenarios presented on a simulated environment that provides conflict resolution advisories. The task is known to be highly mental demanding as it requires continuous situation awareness of the traffic. An interface and experimental protocol were developed for this experiment to validate that error-related potential can be used a new channel for preference. The implementation of the complete design was described together with the experimental evidence of error-related potential. According to the results, we found error-related potential that is in line with existing literature. We also discussed how the preliminary findings of this work can be used as an integral part of an intelligent conflict resolution advisory system that can learn from human preference and duplicate the decision making.

En este articulo de avances, se evidencian algunos resultados, a traves del uso de nuevas tecnologias, especificamente las interfaces cerebro-ordenador, con el registro de las neurosenales o senales encefalograficas, aplicandose en estudiantes que atienden una clase tradicional, con el fin de identificar los niveles de atencion, concentracion, compromiso y estres. A partir de alli se ve la necesidad de crear nuevas estrategias que mejoren los procesos de ensenanza, por lo que los autores plantean la justificacion por la cual se podria hacer uso de las tecnicas de mercadotecnia. Se esperan que estas tecnicas puedan mejorar los niveles de atencion de los estudiantes. Se pudo evidenciar de forma preliminar que el registro de neurosenales permiten corroborar de forma cuantitativa las emociones que emite un alumno a la hora de recibir una clase.

In this paper a brief introduction to some of the goals, recent developments, and open problems in BCI research are given. We mainly focus on presenting our research work in signal processing for single-trial P300-BCI and discuss our current plans for improving the BCI method.

The relative value of automation is debated. On one hand, it is lauded for making mundane work doable by machines. Not only can such machines carry out such work more efficiently than humans: the latter are then empowered and opened up to fulfill endeavors [58] (e.g., creative). Furthermore, automation can itself inspire innovations and the creation of work, such as in the creation of driverless vehicles (including software and hardware, as well as new regulatory practice). In an ideal scenario where work is fully automated, humans would be able to maximize leisure and not be bound by economic demands. Automation fits into a vision for societal AugCog where humans put machines to work so that the former can invest in more rewarding cognitive activities. Humans can do so by deepening their respective cultures with furthered devotion to art, philosophy, science, engineering, mathematics, humanities, politics, spirituality, and religion. In day-today life, automation can augment cognition via reduced stress and increased life satisfaction as people's lives become more centered on relationships and meaning. Automation of work should proceed in a manner sensitive to overall economic and human impacts.

Human errors cause the majority of aviation accidents. Augmented cognition and automation systems enhance pilot performance by evaluating system limitations and flight precision and performance. This study examines the human-machine interface in cockpit design using the tenets of augmented cognition and automation systems theory in terms of task allocation, attentional resources, and situational awareness. The study compares how these principles apply to and interact with each other and with a human/pilot in a closed-loop system. We present a method for integrating augmented cognition systems into airplane flight management systems. We demonstrate systems enhancement with an experiment in which test pilots flew two simulated flights, once without and once with an augmented cognition system. We measured pilot and airplane performance, pilots' situational awareness, workload management, pilots' use of cockpit checklists, and flight precision along four axes: (1) altitude, (2) course, (3) radial/bearing and heading, and (4) airspeed.

Robot-assisted surgery systems are a recent breakthrough in minimally invasive surgeries, offering numerous benefits to both patients and surgeons including, but not limited to, greater visualization of the operation site, greater precision during operation and shorter hospitalization times. Training on robot-assisted surgery (RAS) systems begins with the use of high-fidelity simulators. Hence, the increasing demand of employing RAS systems has led to a rise in using RAS simulators to train medical doctors. The aim of this study was to investigate the brain activity changes elicited during the skill acquisition of resident surgeons by measuring hemodynamic changes from the prefrontal cortex area via a neuroimaging sensor, namely, functional near-infrared spectroscopy (fNIRS). Twenty-four participants, who are resident medical doctors affiliated with different surgery departments, underwent an RAS simulator training during this study and completed the sponge suturing tasks at three d...

In this paper a brief introduction to some of the goals, recent developments, and open problems in BCI research are given. We mainly focus on presenting our research work in signal processing for single-trial P300-BCI and discuss our current plans for improving the BCI method.

In this work EEG signals decomposition, reconstruction and classification is achieved for the brain computer interface.EEG signals are most widely used in the medical field to analyse the patient condition, because it contains much information about human task. From several studies it has been suggested that EEG can be used to detect the severity of several diseases such as CJD, Alzheimer's, dementia and schizophrenia, in addition, specific events (known patterns in electrical stimulation) can indicate epileptic seizures. In general, EEG data analysis has been performed to either predict or classify signals. For the decomposition of EEG signals Discrete wavelet transform is observed and for the classification purpose KNN classifier, SVM classifier and LDA are observed. SVM classifier reaches to 100% efficiency.

Resumen. El incremento en el uso de dispositivos móviles ha generado un crecimiento de puntos de acceso, públicos y privados, esto es aprovechado por los sistemas mHealth, los cuales utilizan la infraestructura de los servicios móviles para brindar servicios de salud, realizando aplicaciones como el monitoreo de señales biológicas. Una de estas aplicaciones puede ser la medición de la señal de electroencefalografía (EEG), la cual consiste en adquirir la señal eléctrica de la corteza cerebral de forma no invasiva. Este artículo propone un sistema capaz de obtener múltiples registros por medio de módulos con 19 sensores de señal EEG y un envío de datos usando el protocolo Wi-Fi hacia un servidor por medio de socket TCP. El sistema desarrollado permite la realización de hasta 12 estudios en forma paralela y de manera remota, como puede ser el hogar del paciente, una clínica rural, etc., con una eficiencia del 99.21% de datos trasmitidos correctamente.

Next generation brain computer interfaces (BCI) are expected to provide robust and continuous control mechanism. In this study, we assessed integration of optical brain imaging (fNIR: functional near infrared spectroscopy) to a P300-BCI for improving BCI usability by monitoring cognitive workload and performance. fNIR is a safe and wearable neuroimaging modality that tracks cortical hemodynamics in response to sensory, motor, or cognitive activation. Eight volunteers participated in the study where simultaneous EEG and 16 optode fNIR from anterior prefrontal cortex were recorded while participants engaged with the P300-BCI for spatial navigation. The results showed a significant response in fNIR signals during high, medium and low performance indicating a positive correlation between prefrontal oxygenation changes and BCI performance. This preliminary study provided evidence that the performance of P300-BCI can be monitored by fNIR which in turn can help improve the robustness of the BCI classification.

En el desarrollo de sistemas de realidad virtual uno de los inconvenientes que se encuentran es la comunicación entre las aplicaciones y los dispositivos de adquisición, ya sea por no disponer de un método de acceso en forma directa de los dispositivos o por necesitar independencia entre ambos, es decir que las aplicaciones corran en una plataforma y los dispositivos en otras. Para lograr esta independencia y a su vez permitir la integración de todo el sistema de realidad virtual, es necesario la implementación de algún protocolo de comunicaciones que permita esta vinculación heterogénea en tiempo real. Los dispositivos generalmente están asociados a funciones o características de los individuos que los utilizan y se necesita integrar los movimientos que estos representan a la aplicación de Realidad Virtual correspondiente. El presente trabajo trata del análisis e implementación del protocolo de comunicaciones VRPN (Virtual Reality Protocol Network) entre las partes de un entorno mu...

Se propone un método de bajo costo computacional para detectar el potencial evocado P300 en aplicaciones ubicuas de comunicación y control, el cual se denomina Procesamiento Embebido P300 (EP-300). La entrada de EP-300 es una señal electroencefalografía (EEG) de un canal y la arquitectura de este método se basa en los algoritmos que utilizan redes neuronales convolucionales. Para implementar el método EP-300, también se presenta una interfaz cerebro-computadora embebida que utiliza cuatro estímulos para evocar el P300 y tiene conectividad con una red de Internet de las cosas. Con esta interfaz, se generó una base de datos para los experimentos y contiene las señales EEG de ocho sujetos. De acuerdo con los resultados, EP-300 se adapta a las señales EEG que genera cada sujeto, tiene un desempeño de 96% utilizando un electrodo y se procesa en tiempo real por su baja complejidad. Sin embargo, para evitar errores en la detección, los sujetos deben mantenerse concentrados y seguir el prot...

The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. While this presents a difficult technical challenge in any context, these concerns were particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically-derived cognitive state classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) based cognitive state estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for cognitive state classification using EEG.

In this paper the topic of the augmented cognition applied to the driving task, and specifically to the steering maneuver, is discussed. We analyze how the presence of haptic feedback on the steering wheel could help drivers to perform a visually-guided task by providing relevant information like vehicle speed and trajectory. Starting from these considerations, a Context-Dependant Steering Wheel force feedback (CDSW) had been developed, able to provide to the driver the most suitable feeling of the vehicle dynamics according to the driven context. With a driving simulator the CSWD software had been tested twice and then compared with a traditional steering wheel.

Resumen. Para la clasificación de las señales del parpadeo y dolor muscular en el brazo derecho ocasionado por un agente externo, se proponen dos modelos de arquitecturas de redes neuronales artificiales, específicamente del tipo perceptrón multicapa y sistema de inferencia neurodifuso adaptativo, ambos modelos utilizan aprendizaje supervisado. Se utilizan series de tiempo obtenidas del parpadeo y electroencefalografías de 15 personas en el rango de 23 a 25 años de edad, para generar una base de datos que se divide en dos conjuntos de datos: entrenamiento y prueba. Los resultados experimentales en el dominio del tiempo y de la frecuencia, de 50 pruebas aplicadas a cada modelo de red, muestran que ambas propuestas de arquitecturas de redes neuronales producen resultados exitosos.

Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann-Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.

We 1 present a novel signaling method for head-mounted displays, which surpasses eye (pupil) and delivers guiding light signals directly to retina through tissue near the eyes. is method preserves full visual acuity on the display and does not block view to the scene, while also delivering additional visual signals. CCS CONCEPTS • Human-centered computing ➝ Displays and imagers • Interface design prototyping • Gestural input • Computing methodologies ➝ Mixed / augmented reality • Virtual reality • Hardware ➝ Displays and imagers

Human augmentation is a field of research that aims to enhance human abilities through medicine or technology. This has historically been achieved by consuming chemical substances that improve a selected ability or by installing implants which require medical operations. Both of these methods of augmentation can be invasive. Augmented abilities have also been achieved with external tools, such as eyeglasses, binoculars, microscopes or highly sensitive microphones. Lately, augmented reality and multimodal interaction technologies have enabled non-invasive ways to augment human. In this article, we first discuss the field and related terms. We provide relevant definitions based on the present understanding of the field. This is followed by a summary of existing work in augmented senses, action, and cognition. Our contribution to the future includes a model for wearable augmentation. In addition, we present a call for research to realize this vision. Then, we discuss future human abilities. Wearable technologies may act as mediators for human augmentation, in the same manner as eyeglasses once revolutionized human vision. Non-invasive and easy-to-use wearable extensions will enable lengthening the active life for aging citizens or supporting the full inclusion of people with special needs in society, but there are also potential problems. Therefore, we conclude by discussing ethical and societal issues: privacy, social manipulation, autonomy and side effects, accessibility, safety and balance, and unpredictable future.

Rapid technological developments and definition of the cyber domain as a battlefield has challenged the cognitive attributes of its operators. In order to meet these demands, higher education programs in STEM (science, technology, engineering, and mathematics) need to recruit suited. Recruitment to STEM studies focuses on increasing the amount of females in these studies, and factors involving retention also needs to be understood. This research focused on assessing the educational setting of Norwegian Defense Cyber Academy and its factors in promoting female student retention in their computer engineering program, and profiling female officer cadets to see if any differences in personality, cognitions, and behaviours strategies exist between male and female cadets.

Metacognition aims at monitoring and regulating one's thinking devoted to problem-solving processes and learning habits among others cognitive tasks. Hence, individuals engaged in better acquisition of domain knowledge achieve higher scores when they are bewaring of how to exploit their metacognitive faculties. Thus, we present a review of some models and methods with the purpose to understand what metacognition is and know how stimulate metacognitive skills. In addition, we propose a Metacognition-Driven Learning paradigm as a reference to guide the design of Intelligent Educational Systems oriented to improve students' metacognitive skills.

Human-computer interaction (HCI) leads to augmented cognition (AC). Cognitive augmentation can be visualized as a pyramid with four layers: from bottom to top, these are data, information, knowledge, and wisdom. The data-informationknowledge hierarchy is well-known in data science; however, wisdom has often been left out. Wisdom is the ideal end of any practice that aims to augment user cognition. In this chapter, the cultural phenomenon of spirituality and the societal organizations of religion are implicated in HCI as important tools for wisdom-granting AC. The phenomenon and organizations in question are taken up in a landscape of digital HCI applications including video gaming and streamed visual and auditory content. It is shown that these media take up spirituality and religion in unique and novel ways, lending to interesting experiences for users. This chapter examines specific examples of the manifestation of spirituality and religion in contemporary digital culture. Such examples have been diffused across platforms like Twitch, YouTube, Discord, Amazon Prime Video and Netflix, Facebook, and Spotify. Attention is also paid to the global multimedia franchises Pokémon, Yu-Gi-Oh!, and Digimon, all of which take up god and the power of gods in reimagined ways. Role-playing games like Guild Wars and RuneScape are also discussed in this context. Through online dating, people are finding their soulmates virtually more than before, further implicating religion and spirituality in the computational realm. This chapter explores fake religion and playful mythology as a function of excited gaming communities and franchises. The "Godhead" phenomenon [1] is treated as the ultimate in augmented cognition that is not entirely or necessarily-yet is potentially-a function of human-machine symbiosis. Global possibilities for diverse activities, religious interaction, and learning about religion, spirituality, and mythology exist given HCI. There is potential for religious practice to augment cognition to the highest level in humanity, up to the soul and spirit levels of reality (both manifest and absolute reality, in Hindu metaphysics). With a strong cognitive foundation, augmented cognition can proceed for any given person. Augmented cognition can reach toward the highest levels of Wilberian holarchy and Blavatsky's sevenfold constitution of man.

Electroencephalography based brain computer interface has enabled communication of human's intention to a computer directly from the brain by decoding signatures that relay the intention information. Error-related potential has been adopted as a signature for natural communication and performance monitoring, among others. In this work, we investigate the use of error-related potential as an input channel to transfer human preference of a strategical advisory to a computer. Air traffic control task was used as a case study to make an empirical inquiry of error-related potential for higher level cognitive tasks (i.e. situation awareness in air traffic control tasks). The experimental task requires the subjects to monitor and assess air traffic scenarios presented on a simulated environment that provides conflict resolution advisories. The task is known to be highly mental demanding as it requires continuous situation awareness of the traffic. An interface and experimental protocol were developed for this experiment to validate that error-related potential can be used a new channel for preference. The implementation of the complete design was described together with the experimental evidence of error-related potential. According to the results, we found error-related potential that is in line with existing literature. We also discussed how the preliminary findings of this work can be used as an integral part of an intelligent conflict resolution advisory system that can learn from human preference and duplicate the decision making.

We present an ambulatory cognitive state classification system to assess the subject&#39;s mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

Resumen. El incremento en el uso de dispositivos móviles ha generado un crecimiento de puntos de acceso, públicos y privados, esto es aprovechado por los sistemas mHealth, los cuales utilizan la infraestructura de los servicios móviles para brindar servicios de salud, realizando aplicaciones como el monitoreo de señales biológicas. Una de estas aplicaciones puede ser la medición de la señal de electroencefalografía (EEG), la cual consiste en adquirir la señal eléctrica de la corteza cerebral de forma no invasiva. Este artículo propone un sistema capaz de obtener múltiples registros por medio de módulos con 19 sensores de señal EEG y un envío de datos usando el protocolo Wi-Fi hacia un servidor por medio de socket TCP. El sistema desarrollado permite la realización de hasta 12 estudios en forma paralela y de manera remota, como puede ser el hogar del paciente, una clínica rural, etc., con una eficiencia del 99.21% de datos trasmitidos correctamente.

We present an ambulatory cognitive state classification system to assess the subject&#39;s mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

It is of prime importance in everyday human life to cope with and respond appropriately to events that are not foreseen by prior experience. Machines to a large extent lack the ability to respond appropriately to such inputs. An important class of unexpected events is defined by incongruent combinations of inputs from different modalities and therefore multimodal information provides a crucial cue for the identification of such events, e.g., the sound of a voice is being heard while the person in the fieldof-view does not move her lips. In the project DIRAC ("Detection and Identification of Rare Audiovisual Cues") we have been developing algorithmic approaches to the detection of such events, as well as an experimental hardware platform to test it. An audiovisual platform ("AWEAR"-audiovisual wearable device) has been constructed with the goal to help users with disabilities or a high cognitive load to deal with unexpected events. Key hardware components include stereo panoramic vision sensors and 6-channel worn-behind-the-ear (hearing aid) microphone arrays. Data have been recorded to study audiovisual tracking, a/v scene/object classification and a/v detection of incongruencies.

High levels of stress, drowsiness and lack of concentration, are some of the main factors that affect the drivers, which can lead to traffic congestion and even accidents. One of the challenges that has caught the attention in the area of research of prevention of traffic accidents, it is the generation of mechanisms that contribute to monitoring and evaluating the driver behavior. This paper presents a prediction model based on Machine Learning techniques to detect cognitive states based on the monitoring of the electroencephalographic signals of drivers of vehicles acquired in various real driving scenarios. The proposed prediction model consists of three phases: 1.-Acquisition of electroencephalographic signals from drivers in real time. 2.-Select and extract the main characteristics of the signals. 3.-Develop the prediction model using the Support Vector Machine (SVM) algorithm. Finally, to evaluate the performance of the proposed model, it was compared with two Machine Learning Techniques: K-Nearest-Neighbors (KNN) and Logistic Regression (LR). The results obtained through the experiments demonstrate that the proposed model has the best performance in the evaluation and prediction of the cognitive workload of the physiological signals of the conductors, with a 93% accuracy in the classification of the information compared to other models that have an 84% and 54% accuracy in the classification.

3D virtual environments are increasingly used as generalpurpose medium for communicating spatial information. In particular, virtual 3D city models have numerous applications such as car navigation, city marketing, tourism, and gaming. In these applications, pointsof-interest (POI) play a major role since they typically represent features relevant for specific user tasks and facilitate effective user orientation and navigation through the 3D virtual environment. In this paper, we present strategies that aim at effectively visualizing points-of-interest in a 3D virtual environment used on mobile devices. Here, we additionally have to face the "keyhole" situation, i.e., the users can realize only a small part of the environment due to the limited view space and resolution. For the effective visualization of points-of-interest in 3D virtual environments we propose to combine specialized occlusion management for 3D scenes together with visual cues that handle out-of-frame points-of-interest. We also discuss general aspects and definitions of points-of-interest in the scope of 3D models and outline a prototype implementation of the mobile 3D viewer application based on the presented concepts. In addition, we give a first performance evaluation with respect to rendering speed and power consumptions.

Metacognition aims at monitoring and regulating one's thinking devoted to problem-solving processes and learning habits among others cognitive tasks. Hence, individuals engaged in better acquisition of domain knowledge achieve higher scores when they are bewaring of how to exploit their metacognitive faculties. Thus, we present a review of some models and methods with the purpose to understand what metacognition is and know how stimulate metacognitive skills. In addition, we propose a Metacognition-Driven Learning paradigm as a reference to guide the design of Intelligent Educational Systems oriented to improve students' metacognitive skills.

Transcranial Doppler monitoring (TCD) has been proposed as a tool to be used in Augmented Cognition (AugCog) systems to monitor brain activation during the performance of different cognitive tasks. In the present study, the main goal is to analyze variations in blood flow velocity (BFV) measured by TCD during the exposure to a virtual reality environment when there are changes in the focus of attention of the participants. Two abrupt events are forced during the navigation in a virtual environment in order to change their focus of attention to the real world. In one of them, the screen goes completely blue, and in the other one, a mesh appears in front of the virtual environment making it difficult to visualize. Results show that BFV values in both middle cerebral arteries remain similar when the first event occurs, but there is an increase during the second event. The origin of this increment may probably be found in the higher difficulty of having a mesh in front of the virtual environment, requiring more attention than before. These results show that changes in the stimuli can generate modifications in BFV that can be monitored by TCD, and can be useful for AugCog applications.

Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann-Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.

Electroencephalography based brain computer interface has enabled communication of human's intention to a computer directly from the brain by decoding signatures that relay the intention information. Error-related potential has been adopted as a signature for natural communication and performance monitoring, among others. In this work, we investigate the use of error-related potential as an input channel to transfer human preference of a strategical advisory to a computer. Air traffic control task was used as a case study to make an empirical inquiry of error-related potential for higher level cognitive tasks (i.e. situation awareness in air traffic control tasks). The experimental task requires the subjects to monitor and assess air traffic scenarios presented on a simulated environment that provides conflict resolution advisories. The task is known to be highly mental demanding as it requires continuous situation awareness of the traffic. An interface and experimental protocol were developed for this experiment to validate that error-related potential can be used a new channel for preference. The implementation of the complete design was described together with the experimental evidence of error-related potential. According to the results, we found error-related potential that is in line with existing literature. We also discussed how the preliminary findings of this work can be used as an integral part of an intelligent conflict resolution advisory system that can learn from human preference and duplicate the decision making.

Human augmentation is a field of research that aims to enhance human abilities through medicine or technology. This has historically been achieved by consuming chemical substances that improve a selected ability or by installing implants which require medical operations. Both of these methods of augmentation can be invasive. Augmented abilities have also been achieved with external tools, such as eyeglasses, binoculars, microscopes or highly sensitive microphones. Lately, augmented reality and multimodal interaction technologies have enabled non-invasive ways to augment human. In this article, we first discuss the field and related terms. We provide relevant definitions based on the present understanding of the field. This is followed by a summary of existing work in augmented senses, action, and cognition. Our contribution to the future includes a model for wearable augmentation. In addition, we present a call for research to realize this vision. Then, we discuss future human abilities. Wearable technologies may act as mediators for human augmentation, in the same manner as eyeglasses once revolutionized human vision. Non-invasive and easy-to-use wearable extensions will enable lengthening the active life for aging citizens or supporting the full inclusion of people with special needs in society, but there are also potential problems. Therefore, we conclude by discussing ethical and societal issues: privacy, social manipulation, autonomy and side effects, accessibility, safety and balance, and unpredictable future.