Augmented Cognition Research Articles

Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.

Development of Individualized Cognitive Augmentation Regimen for Elderly (iCARE): Preliminary findings of a caregiver‐driven cognitive training for dementia in India

Development of Individualized Cognitive Augmentation Regimen for Elderly (iCARE): Preliminary findings of a caregiver‐driven cognitive training for dementia in India

Accumbal Dopamine Responses Are Distinct between Female Rats with Active and Passive Coping Strategies.

There is a gap in existing knowledge of stress-triggered neurochemical and behavioral adaptations in females. This study was designed to explore the short-term consequences of a single social defeat (SD) on accumbal dopamine (DA) dynamics and related behaviors in female Wistar rats. During the SD procedure, rats demonstrated different stress-handling strategies, which were defined as active and passive coping. The "active" subjects expressed a significantly higher level of activity directed toward handling stress experience, while the "passive" ones showed an escalated freezing pattern. Remarkably, these opposite behavioral manifestations were negatively correlated. Twenty-four hours following the SD exposure, decreased immobility latency in the Porsolt test and cognitive augmentation in the new object recognition evaluation were evident, along with an increase in electrically evoked mesolimbic DA release in passive coping rats. Rats exhibiting an active pattern of responses showed insignificant changes in immobility and cognitive performance as well as in evoked mesolimbic DA response. Furthermore, the dynamics of the decline and recovery of DA efflux under the depletion protocol were significantly altered in the passive but not active female rats. Taken together, these data suggest that female rats with a passive coping strategy are more susceptible to developing behavioral and neurochemical alterations within 24 h after stress exposure. This observation may represent both maladaptive and protective responses of an organism on a short timescale.

Automated Competence Assessment Procedures in Entrepreneurship

This study endeavors to automate the assessment of competencies within the domain of entrepreneurship, specifically targeting the augmentation of entrepreneurial cognition and conduct within universities in German rural regions, like Lower Franconia. Employing methods, including literature analyses and expert interviews, we formulated and validated an entrepreneurship competence profile and accompanying self-assessment tool. The ensuing evaluative framework is poised for seamless integration into learning management systems, thereby facilitating intelligent competence monitoring within educational environments. Purpose: The aim of this thesis is to develop an automated competence assessment procedure in the field of entrepreneurship. This can be used in the university environment in the long term to promote and teach entrepreneurial thinking and behavior in order to sustainably improve the quality of learning outcomes and achieve targeted promotion of entrepreneurship. Methodology: Based on a relevant literature analysis, four guideline-based expert interviews were created and conducted. The results of the interviews were compiled and validated in a structured competence profile (entrepreneurship competence profile). Based on this competence catalog for entrepreneurs, an empirically valid self-test was created using standard psychometric questionnaire construction methods. Results: The entrepreneurship competence profile and a consequential empirically validated self-test for competence assessment were created. This test provides the basis for the long-term competence development of students and can further be embedded automatically into a learning management system (LMS) as part of intelligent competence monitoring, which allows for the recording of competencies for each student and the individual incorporation of gap closure into the curriculum. Originality/value: In previous research, there were no competence profiles or competence assessment procedures in the field of entrepreneurship that derived relevant competencies directly from actors within this environment. This work illustrates the development of a competence assessment procedure for entrepreneurs and shows which methods can be used to close prevailing research gaps in the field of intelligent competence monitoring.

Merging Minds and Machines: The Role of Advancing AI in Robotics

The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and artificial intelligence (AI). Artificial intelligence is a theory. It is the development of computer systems that are able to perform tasks that would require human intelligence. This abstract explores the pivotal role that AI plays in advancing the capabilities and applications of robotic systems. The integration of AI algorithms and machine learning techniques has launched robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. Design fictions that vividly imagines future scenarios of AI or robotics in use offer a means both to explain and query the technological possibilities. Examples of these tasks are visual perception, speech recognition, decision-making, and translation between languages. The three key dimensions of AI’s role in robotics are Cognitive Augmentation, Human-Robot Collaboration, and Autonomous Intelligence. The abstract also discusses the societal implications of this AI-driven advancement in robotic systems, including ethical considerations, job market impacts, and the democratization of access to advanced technology. The convergence of human intellect and artificial intelligence in robotics marks a transformative era where machines become not just tools, but companions, collaborators, and cognitive extensions of human capabilities. Researchers are taking inspiration from the brain and considering alternative architectures in which networks of artificial neurons and synapses process information with high speed and adaptive learning capabilities in an energy-efficient, scalable manner. The indispensable role of AI in shaping the future of robotic systems and bridging the gap between human potential and machine capabilities is highlighted. The major impact of this synergy reverberates across industries, promising the world where robots become not just mechanical contraptions / defective apparatus but intelligent partners in our journey of progress.

Human-Centered augmented translation: against antagonistic dualisms

ABSTRACT Industry commentators have recently proposed the concept of ‘augmented translation’. Drawing on the notions of ‘antagonistic dualisms’ and ‘human-centered artificial intelligence’ (HCAI), this paper considers various definitions of ‘augmentation’ from an augmented cognition standpoint including definitions focussing on problem-solving, interdisciplinary field theories, and cognition supported by sensing technologies and AI. It is suggested that translation has been an augmented activity for some decades now. However, according to other views of augmented cognition, the level of augmentation is low in comparison to what could theoretically be achieved if the sensing and technological mitigations envisaged for augmented cognition could be realised. Translation technology has not been driven by an empowerment or intelligence amplification (IA) agenda, but by an emulation and artificial intelligence (AI) agenda. The mechanisms, technical and ethical challenges of achieving augmented translation, beyond what is currently in place in translation tools, are tentatively explored. It is, in conclusion, suggested that the HCAI focus on intelligence amplification rather than on replacement of human ability, on a move from emulation to empowerment, is pointing the way forward.

Improvement of motor function in post-stroke patients with severe paralysis by repeating cognitive augmentation of motor function is associated with the ability to induce motor imagery

Improvement of motor function in post-stroke patients with severe paralysis by repeating cognitive augmentation of motor function is associated with the ability to induce motor imagery

Effects of Sleep Deprivation on Performance during a Change Signal Task with Adaptive Dynamics.

Augmented cognition, which refers to real-time modifications to a human-system interface to improve performance and includes dynamic task environments with automated adaptations, can serve to protect against performance impairment under challenging work conditions. However, the effectiveness of augmented cognition as a countermeasure for performance impairment due to sleep loss is unknown. Here, in a controlled laboratory study, an adaptive version of a Change Signal task was administered repeatedly to healthy adults randomized to 62 h of total sleep deprivation (TSD) or a rested control condition. In the computerized task, a left- or right-facing arrow was presented to start each trial. In a subset of trials, a second arrow facing the opposite direction was presented after a delay. Subjects were to respond within 1000 ms of the trial start by pressing the arrow key corresponding to the single arrow (Go trials) or to the second arrow when present (Change trials). The Change Signal Delay (CSD)-i.e., the delay between the appearance of the first and second arrows-was shortened following incorrect responses and lengthened following correct responses so that subsequent Change trials became easier or harder, respectively. The task featured two distinct CSD dynamics, which produced relatively stable low and high error rates when subjects were rested (Low and High Error Likelihood trials, respectively). During TSD, the High Error Likelihood trials produced the same, relatively high error rate, but the Low Error Likelihood trials produced a higher error rate than in the rested condition. Thus, sleep loss altered the effectiveness of the adaptive dynamics in the Change Signal task. A principal component analysis revealed that while subjects varied in their performance of the task along a single dominant dimension when rested, a second inter-individual differences dimension emerged during TSD. These findings suggest a need for further investigation of the interaction between augmented cognition approaches and sleep deprivation in order to determine whether and how augmented cognition can be relied upon as a countermeasure to performance impairment in operational settings with sleep loss.

Immersive Horizons: Exploring the Transformative Power of Virtual Reality Across Economic Sectors

The scholarly discourse surrounding the manifold advantages, applications, and limitations of implementing Virtual Reality (VR) in the contemporary milieu has burgeoned over time. VR holds immense potential, attracting fervent interest from governmental and private entities alike. Nevertheless, the existing body of literature pertaining to the expanding utilization of VR in diverse economic sectors remains scant. Therefore, the primary objective of this study is to furnish a comprehensive literature review encompassing VR applications across various economic domains while elucidating concerns surrounding its integration within engineering education. A total of 108 publications were extracted from prominent databases such as Scopus, Elsevier, Science Direct, and Google Scholar, with a subsequent review of 51 relevant works. These scrutinized journals were published between 2015 and 2022 and were predominantly authored in English. The reviewed publications encompassed VR applications in education, robotics, healthcare, transportation, sports, agriculture, governance, security, and media. The study’s findings unveiled significant advancements in VR implementation within engineering education, medical training, cognitive augmentation, aircraft assembly, governance, and diverse other spheres. Notwithstanding these achievements, impediments to VR deployment were identified, stemming from financial exigencies, cultural and conventional norms, with scant evidence of VR’s prevalence in underdeveloped nations, given that all the assessed research originated from developed economies. Additionally, the limitations of this review encompassed a small sample size and a narrowly focused demographic in the examined articles. Nevertheless, despite these constraints, the research highlights substantial progress in VR utilization over the preceding decade.

The placebo effect of human augmentation: Anticipating cognitive augmentation increases risk-taking behavior

Human Augmentation Technologies improve human capabilities using technology. In this study, we investigate the placebo effect of Augmentation Technologies. Thirty naïve participants were told to be augmented with a cognitive augmentation technology or no augmentation system while conducting a Columbia Card Task. In this risk-taking measure, participants flip win and loss cards. The sham augmentation system consisted of a brain–computer interface allegedly coordinated to play non-audible sounds that increase cognitive functions. However, no sounds were played throughout all conditions. We show a placebo effect in human augmentation, where a sustained belief of improvement remains after using the sham system and an increase in risk-taking conditional on heightened expectancy using Bayesian statistical modeling. Furthermore, we identify differences in event-related potentials in the electroencephalogram that occur during the sham condition when flipping loss cards. Finally, we integrate our findings into theories of human augmentation and discuss implications for the future assessment of augmentation technologies.

A Cognitive Assistant for Operators: AI-Powered Knowledge Sharing on Complex Systems

Operating a complex and dynamic system, such as an agile manufacturing line, is a knowledge-intensive task. It imposes a steep learning curve on novice operators and prompts experienced operators to continuously discover new knowledge, share it, and retain it. In practice, training novices is resource-intensive, and the knowledge discovered by experts is not shared effectively. To tackle these challenges, we developed an AI-powered pervasive system that provides cognitive augmentation to users of complex systems. We present an AI cognitive assistant that provides on-the-job training to novices while acquiring and sharing (tacit) knowledge from experts. Cognitive support is provided as dialectic recommendations for standard work instructions, decision-making, training material, and knowledge acquisition. These recommendations are adjusted to the user and context to minimize interruption and maximize relevance. In this article, we describe how we implemented the cognitive assistant, how it interacts with users, its usage scenarios, and the challenges and opportunities.

Working Memory Load Impact on Effective Connectivity: a Dynamic Causal Modeling Study

This study aims to examine the roles of the brain regions in working memory (WM) processing and the modulatory effects of WM load. Electroencephalogram (EEG) signals were recorded when participants perform a letter-version n-back task with three different levels of WM load. The directional causal connections between brain regions were estimated using Dynamic Causal Modeling (DCM). The directions and strengths of the connections were compared for different WM load conditions. The results showed a right-lateralized, backward-only connection pattern for the high WM load condition. The results also showed changes in the roles of the brain regions when the WM load increases. These findings of the modulatory effects of WM load may be utilized in measuring cognitive states, and designing adaptive automation in augmented cognition programs.

Enhancing Military Training Using Extended Reality: A Study of Military Tactics Comprehension

This study identifies that increasing the fidelity of terrain representation does not necessarily increase overall understanding of the terrain in a simulated mission planning environment using the Battlefield Visualization and Interaction software (BVI; formerly known as ARES (M. W. Boyce et al., International Conference on Augmented Cognition, 2017, 411–422). Prior research by M. Boyce et al. (Military Psychology, 2019, 31(1), 45–59) compared human performance on a flat surface (tablet) versus topographically-shaped surface (BVI on a sand table integrated with top-down projection). Their results demonstrated that the topographically-shaped surface increased the perceived usability of the interface and reduced cognitive load relative to the flat interface, but did not affect overall task performance (i.e., accuracy and response time). The present study extends this work by adding BVI onto a Microsoft HoloLens™. A sample of 72 United States Military Academy cadets used BVI on three different technologies: a tablet, a sand table (a projection-based display onto a military sand table), and on the HoloLens™ in a within-subjects design. Participants answered questions regarding military tactics in the context of conducting an attack in complex terrain. While prior research (Dixon et al., Display Technologies and Applications for Defense, Security, and Avionics III, 2009, 7327) suggested that the full 3D visualization used by the Hololens™ should improve performance relative to the sand table and tablet, our results demonstrated that the HoloLens™ performed relatively worse than the other modalities in accuracy, response time, cognitive load, and usability. Implications and limitations of this work will be discussed.

Considerations for the implementation of machine learning into acute care settings.

Management of patients in the acute care setting requires accurate diagnosis and rapid initiation of validated treatments; therefore, this setting is likely to be an environment in which cognitive augmentation of the clinician's provision of care with technology rooted in artificial intelligence, such as machine learning (ML), is likely to eventuate. PubMed and Google Scholar with search terms that included ML, intensive/critical care unit, electronic health records (EHR), anesthesia information management systems and clinical decision support were the primary sources for this report. Different categories of learning of large clinical datasets, often contained in EHRs, are used for training in ML. Supervised learning uses algorithm-based models, including support vector machines, to pair patients' attributes with an expected outcome. Unsupervised learning uses clustering algorithms to define to which disease grouping a patient's attributes most closely approximates. Reinforcement learning algorithms use ongoing environmental feedback to deterministically pursue likely patient outcome. Application of ML can result in undesirable outcomes over concerns related to fairness, transparency, privacy and accountability. Whether these ML technologies irrevocably change the healthcare workforce remains unresolved. Well-resourced Learning Health Systems are likely to exploit ML technology to gain the fullest benefits for their patients. How these clinical advantages can be extended to patients in health systems that are neither well-endowed, nor have the necessary data gathering technologies, needs to be urgently addressed to avoid further disparities in healthcare.

Experience-based Intelligence Augmentation with Decisional DNA: Upcoming Direction

Intelligence amplification systems and technologies have gained significant interest from academia and industry during the past few decades. One of the main reasons behind this trend is the fact that most experts agree that truly intelligent artificial system is yet to be developed. The question increasing often asked is this: Is full replication of human intelligence desirable key aim in intelligence related technology and research? In this context, the concept of Augmented Intelligence, also known as Cognitive Augmentation or Intelligence Amplification (IA) comes into play. One of the main reasons behind this interest in this concept is the potential of such technologies to revolutionize human life as they intend to work under complex decision-making environments, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties aims to enhance human capabilities and create more intelligent and efficient human-centered environments. In this paper we discuss how Decisional DNA (DDNA), a multi-domain knowledge structure that has the Set of Experience Knowledge Structure (SOEKS) at its core can be utilized as an experience-based intelligence augmentation in a number of different domains.

Smart Knowledge Engineering for Cognitive Systems: A Brief Overview

Cognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies and guidelines are required to sustain the human–AI interface so that communication can occur. However, traditional Knowledge Management (KM) and Knowledge Engineering (KE) approaches encounter problems when dealing with cutting-edge technologies, imposing impediments for the use of traditional methods in cognitive systems (CS). This paper presents a brief overview of the Smart Knowledge Engineering for Cognitive Systems (SKECS), which is based on methods, technologies, and procedures that bring innovations to the fields of KE, KM, and CS. The goal is to bridge the gap in the hybrid cognitive interface by the combination of experience-based knowledge representation with the use of emerging technologies such as deep learning, context-aware indexing/retrieval, active learning with a human-in-the-loop, and stream reasoning. In this work Set of Experience Knowledge Structure (SOEKS) and Decision DNA (DDNA) is extended to the visual domain and utilized for knowledge capture, representation, reuse, and evolution. These technologies are examined throughout the layers of SKECS for applications in knowledge acquisition, formalization, storage/retrieval, learning, and reasoning, with the final goal of achieving knowledge augmentation (wisdom) in CS. Features of the SKECS and their practical implementation is discussed through a case study—the Cognitive Vision Platform for Hazard Control (CVP-HC)—suggesting that methods, techniques and procedures comprising the SKECS are suitable for advancing systems toward augmented cognition.

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

Neuro cognitive activator

The Onset fluency disorder is termed as stuttering or stammering, which is one of the most addressed disorder. There are numerous cause and stages of the stuttering, most of adolescent who run into this disorder will possesses either PWS or Dysfluency. These neuro cognitive disorders includes brain stimulation supported with significant research on brain and cognitive augmentation recommended and prescribed for the healthy adults and for the patients who suffered from neurological or psychiatric diseases. Number of sensing systems, for an instance functional near infrared spectroscopy (FNIRS) has been utilized in a significant manner to ascertain speech fluency and other related motor disabilities. In principle, FNIRS operated by quantifying hemoglobin-concentration changes in the brain based on optical intensity measurements, measuring the same hemodynamic changes. The said FNIRS provisioned to deliver as a neuro-imaging brain image interface (BCI) functioned to measure neuronal activities, significantly brain cortex considering neuro-vascular coupling through uniformly distributed opto-electronic devices. The dataset generated by the FNIRS confirmed the mapped reflection of the behavior of the brain depending upon the actions performed by the participants. Depending upon the disorder and severity, the placement of the optodes and sensitivity of the treatment or the module will vary. To boost it up a little bit, the simulation sessions will be conducted in a regular interval and the data which is being processed or collected through each sessions will be imparted onto the compute module as a prepossessed data to increase the scalability and reliability of the module.

Interface, interaction, and intelligence in generalized brain–computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

Augmented Intelligence

Augmented intelligence (AI), also known as intelligence augmentation (IA) or cognitive augmentation, is next level artificial intelligence. Augmented intelligence refers to the synergistic technology of humans and computers. This paper compares Augmented Intelligence with Artificial Intelligence, and describes its applications including sales, business, healthcare, military, decision-making, remote assistance and logistics. The article further describes the benefits and challenges associated with AI. In the foreseeable future, AI will be augmenting our capabilities, allowing us to do more in less amount of time. 9