Cognitive Computing Research Articles

When New Experience Leads to New Knowledge: A Computational Framework for Formalizing Epistemically Transformative Experiences

Abstract The discovery of a new kind of experience can teach an agent what that kind of experience is like. Such a discovery can be epistemically transformative, teaching an agent something they could not have learned without having that kind of experience. However, learning something new does not always require new experience. In some cases, an agent can merely expand their existing knowledge using, e.g., inference or imagination that draws on prior knowledge. We present a computational framework, grounded in the language of partially observable Markov Decision Processes (POMDPs), to formalize this distinction. We propose that epistemically transformative experiences leave a measurable “signature” distinguishing them from experiences that are not epistemically transformative. For epistemically transformative experiences, learning in a new environment may be comparable to “learning from scratch” (since prior knowledge has become obsolete). In contrast, for experiences that are not transformative, learning in a new environment can be facilitated by prior knowledge of that same kind (since new knowledge can be built upon the old). We demonstrate this in a synthetic experiment inspired by Edwin Abbott’s Flatland, where an agent learns to navigate a 2D world and is subsequently transferred either to a 3D world (epistemically transformative change) or to an expanded 2D world (epistemically non-transformative change). Beyond the contribution to understanding epistemic change, our work shows how tools in computational cognitive science can formalize and evaluate philosophical intuitions in new ways.

Building machines that learn and think with people.

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

Cognitive computing method based on decoding psychological emotional states

The current artificial intelligence is constrained to passively executing human command control. It cannot perceive, learn, and guide itself. Furthermore, the majority of these systems are unable to comprehend the human psychological cognitive state or emotional intention actively. To enhance the universality and generalization ability of existing emotion recognition methods, a psychological emotion state decoding method combining singular spectrum analysis and entropy measure is designed based on an electroencephalography signal. This method is developed to empower the cognitive computing system with the ability to perceive emotions. The entropy measure is accelerated by the vector dissimilarity criterion. Finally, dynamic sample entropy model learning is used to realize the cognitive calculation of psychological and emotional state recognition. The results demonstrated that the computational efficiency of the proposed decoding algorithm was significantly improved. The average recognition accuracy of positive and negative emotional states reached 82.78±16.22. The best recognition accuracy of the proposed electroencephalography emotion recognition method was 84.64 %. The proposed electroencephalography emotion recognition method demonstrated an accuracy of 64.15 % in recognizing cross-individual positive, neutral, and negative emotional states. The proposed emotion recognition method shows better universality and generalization performance, and can effectively identify people's psychological and emotional states.

Multimodal emotion recognition: A comprehensive review, trends, and challenges

AbstractAutomatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers from 2017 to 2023 to reflect on the current trends in multimodal affective computing. This article covers various modalities used in emotion recognition based on the emotional response system under four categories: subjective experience comprising text and self‐report; peripheral physiology comprising electrodermal, cardiovascular, facial muscle, and respiration activity; central physiology comprising EEG, neuroimaging, and EOG; behavior comprising facial, vocal, whole‐body behavior, and observer ratings. This review summarizes the measures and behavior of each modality under various emotional states. This article provides an extensive list of multimodal datasets and their unique characteristics. The recent advances in multimodal emotion recognition are grouped based on the research focus areas such as emotion elicitation strategy, data collection and handling, the impact of culture and modality on multimodal emotion recognition systems, feature extraction, feature selection, alignment of signals across the modalities, and fusion strategies. The recent multimodal fusion strategies are detailed in this article, as extracting shared representations of different modalities, removing redundant features from different modalities, and learning critical features from each modality are crucial for multimodal emotion recognition. This article summarizes the strengths and weaknesses of multimodal emotion recognition based on the review outcome, along with challenges and future work in multimodal emotion recognition. This article aims to serve as a lucid introduction, covering all aspects of multimodal emotion recognition for novices.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Technologies > Cognitive Computing Technologies > Artificial Intelligence

Treating Anxiety-Based Cognitive Distortions Pertaining to Somatic Perception for Better Chronic Pain Outcomes: A Recommendation for Better Practice in the Present Day and the Cyber Age of Medicine.

Anxiety-based cognitive distortions pertaining to somatic perception (ABCD-SPs)-primarily catastrophizing, fear avoidance, and kinesiophobia-have been repeatedly linked to worsening chronic, non-cancer pain (CNCP) outcomes of increased disability, amplified pain, ineffective opioid use, and opioid misuse. Several studies have suggested that treating ABCD-SPs can improve pain outcomes, yet identification and targeting of ABCD-SPs are not part of the standard medical pain assessment and treatment plan. Utilizing a narrative review of proposed mechanisms, published patient perspectives, and study correlations connecting these cognitive distortions with CNCP outcomes, an approach for better practice in the delivery of standard medical CNCP care can be deduced and formulated into a Belief and Behavior Action Plan (BBAP) for medical clinicians treating CNCP to implement into initial and maintenance care planning. These recommendations require relatively few resources to implement and have the potential to disseminate more effective CNCP treatment on a large scale now and in the future with the new frontier of cognitive computing in medicine.

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

Impact on ambulatory palliative appointment lead times by standardizing scheduling protocols based on cognitive computing models.

62 Background: Although palliative care is available across the United States, access continues to remain variable. In Texas, only 52% of hospital systems have palliative services. As a result, optimizing existing systems is critical to providing timely services to this vulnerable population. The Supportive Care Services Clinic at University Health in San Antonio, TX provides ambulatory palliative services to patients including physical and psychosocial symptom management and care coordination. For the period April 1, 2023 to June 30, 2023 average lead times for new palliative patient visits (NPPV) and hospital consult discharge visits (HCDV) was 53 days and 39 days respectively. The aim of this project was to decrease the lead times for NPPV and HCDV through standardization of scheduling criteria. We hypothesized that by applying a scheduling protocol utilizing cognitive computing models (CCM) calculating readmission risk scores to prioritize referrals, we could reduce appointment lead times. Methods: During the implementation phase, we developed referral triage criteria to prioritize patients on a scheduling wait list, including patients who were at higher risk of hospitalization or rapid decline based on CCM readmission risk scores calculated within the electronic medical record. Patients were assigned to next available in template. If a cancelation occurred, patients were pulled forward and vacancy was filled based on priority score. Results: For the initial post-implementation period between September 1, 2023 and November 30, 2023 NPPV average lead time was 47 days which was a reduction of 11% from baseline. The average lead time for HCDV was 33 days which was a reduction of 15% from baseline. In a second follow-up period from February 1, 2024 to April 30, 2024, the average lead time of NPPV was 22 days, a 58% decrease from baseline. The average lead time for HCSV was 24 days which was a significant reduction (p-value = 0.011) of 38% from the pre-implementation baseline. Conclusions: Utilization of CCM risk scores to triage NPPV and HCDV significantly reduced the lead times and resulted in more timely services for critical patients. We also noted increased template utilization. Future projects aim to study the effects of scheduling protocols on template utilization, cancellation rates, no-show rates, readmission rates and other health care utilization. Palliative appointment lead times. April-June 2023 Sept-Nov 2023 Feb-Apr 2024 Total Pt Seen 292 364 534 Avg Lead Time (d) 41 38 29 New Patient New Patient New Patient Completed 66 82 74 Avg Lead Time (d) 53 47 22 Hospital Discharge Hospital Discharge Hospital Discharge Completed 27 28 25 Avg Lead Time (d) 39 33 24

IMPRS 2024: Interdisciplinary Approaches to the Foundations of Human Speech

The International Max Planck Research School (IMPRS) biennial conference on psycholinguistics focused on the psychological, social, and biological foundations of human speech. The conference took an interdisciplinary approach, drawing on data from cognitive research, neuroscience, computer science, medicine, genetics, anthropology, and evolutionary theory. This article provides an overview of the keynote speeches and the most significant posters, presenting the findings of scholars from around the world.

Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.

The Challenges of Implementing Cognitive Computing in Small Construction Projects: A Data-Driven Perspective

This study aims to identify and analyze the challenges of implementing cognitive computing in small construction projects, where decision-making, process optimization, and sustainability enhancements are crucial yet challenging. The research adopts a mixed-methods approach, integrating a thorough literature review, quantitative evaluation, and structural equation modeling (SEM) to explore the relationships between the identified barriers and the effective application of cognitive computing. The findings reveal significant hurdles, including complexity in customization (β = 0.327, t = 9.848, p < 0.001), data integrity and integration issues (β = 0.389, t = 14.534, p < 0.001), financial and cultural constraints (β = 0.295, t = 7.850, p < 0.001), and ethical and privacy concerns (β = 0.319, t = 8.963, p < 0.001). These barriers impede the seamless adoption of cognitive computing technologies. This research contributes novel insights into the specific challenges faced by small construction projects and provides practical recommendations to overcome these obstacles. By addressing these challenges, this study offers valuable guidance for stakeholders aiming to leverage cognitive computing to improve project outcomes in the construction industry. The novelty of this research lies in its focus on small-scale projects, a relatively underexplored area, and its comprehensive analysis of the multifaceted barriers that hinder the successful implementation of cognitive computing. Doi: 10.28991/CEJ-2024-010-09-011 Full Text: PDF

Psychomatics-A Multidisciplinary Framework for Understanding Artificial Minds.

Although large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.

Label-refined EEG emotion classification based on intelligent media communication

With the development of cognitive computing and intelligent media communication, recognizing the users emotion using electroencephalography (EEG) has garnered increasing attention. However, building a cross-subject emotion recognition model with good generalization performance is really difficult. To achieve remarkable performance, a label-refined domain adversarial neural network is designed and EEG samples are subdivided into 6 groups according to the differences in users' brain activities under different movie stimuli. Based on the experiment of SEED and SEED-IV datasets, the classification accuracy of our method reaches 93.354.14% and 84.086.01%, achieving the state-of-the-art performance. Experiments show that using different movie stimuli to refine labels can achieve efficient cross-subject emotion recognition.

Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring.

Working Memory Guides Action Valuation in Model-based Decision-making Strategy.

Humans use both model-free (or habitual) and model-based (or goal-directed) strategies in sequential decision-making. Working memory (WM) is essential for the model-based strategy; however, its exact role in these processes remains elusive. This study investigates the influence of WM processes on decision-making and the underlying cognitive computing mechanisms. Specifically, we used experimental data from two-stage decision tasks and found that delay and load, two WM-specific variables, impact goal-revisiting behaviors. Then, we proposed possible computational mechanisms by which WM participates in information processing and integrated them into the model-based system. The proposed Hybrid-WM model reproduced the observed experimental effects and fit human behavior better than the classic hybrid reinforcement learning model. These results were verified with independent data sets. Furthermore, differences in model parameters explain the age-related difference in sequential decision-making. Overall, this study suggests that WM guides action valuation in model-based strategies, highlighting the contribution of higher cognitive functions to sequential decision-making.

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision

DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare.

Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite the high accuracy of supervised EEG classification models, they are constrained by labor-intensive annotations and poor generalization. Self-supervised models address these issues but encounter difficulties in matching the accuracy of supervised learning. Three challenges persist: 1) capturing temporal dependencies in EEG; 2) adapting loss functions to describe feature similarities in self-supervised models; and 3) addressing the prevalent issue of data imbalance in EEG. This study introduces the DreamCatcher Network (DCNet), a self-supervised EEG classification framework with a two-stage training strategy. The first stage extracts robust representations through contrastive learning, and the second stage transfers the representation encoder to a supervised EEG classification task. DCNet utilizes time-series contrastive learning to autonomously construct representations that comprehensively capture temporal correlations. A novel loss function, SelfDreamCatcherLoss, is proposed to evaluate the similarities between these representations and enhance the performance of DCNet. Additionally, two data augmentation methods are integrated to alleviate class imbalances. Extensive experiments show the superiority of DCNet over the current state-of-the-art models, achieving high accuracy on both the Sleep-EDF and HAR datasets. It holds substantial promise for revolutionizing sleep disorder detection and expediting the development of advanced healthcare systems driven by cognitive computing.

Guest Editorial Special Issue on Data-driven Cognitive Computing for Smart Healthcare Systems

Guest Editorial Special Issue on Data-driven Cognitive Computing for Smart Healthcare Systems

Metal-organic framework single crystal for in-memory neuromorphic computing with a light control

Neuromorphic architectures, expanding the limits of computing from conventional data processing and storage to advanced cognition, learning, and in-memory computing, impose restrictions on materials that should operate fast, energy efficiently, and highly endurant. Here we report on in-memory computing architecture based on metal-organic framework (MOF) single crystal with a light control. We demonstrate that the MOF with inherent memristive behavior (for data storage) changes nonlinearly its electric response when irradiated by light. This leads to three and more electronic states (spikes) with 81 ms duration and 1 s refractory time, allowing to implement 40 bits s−1 optoelectronic data processing. Next, the architecture is switched to the neuromorphic state upon the action of a set of laser pulses, providing the text recognition over 50 times with app. 100% accuracy. Thereby, simultaneous data storage, processing, and neuromorphic computing on MOF, driven by light, pave the way for multifunctional in-memory computing architectures.

Rise of the Machines: Exploring the Emergence of Machine Consciousness

Inquiry into the field of artificial intelligence (machines) and its potential to develop consciousness is presented in this study. This investigation explores the complex issues surrounding machine consciousness at the nexus of AI, neuroscience, and philosophy as we delve into the fascinating world of artificial intelligence (AI) and investigate the intriguing question: are machines on the verge of becoming conscious beings? The study considers the likelihood of machines displaying self-awareness and the implications thereof through an analysis of the current state of AI and its limitations. However, with advancements in machine learning and cognitive computing, AI systems have made significant strides in emulating human-like behavior and decision-making. Furthermore, the emergence of machine consciousness raises questions about the blending of human and artificial intelligence, and ethical considerations are also considered. The study provides a glimpse into a multidisciplinary investigation that questions accepted theories of consciousness, tests the limits of what is possible with technology, and do these advancements signify a potential breakthrough in machine consciousness.

Generative AI and Cognitive Computing-Driven Intrusion Detection System in Industrial CPS

Industrial Cyber-Physical Systems (ICPSs) are becoming more and more networked and essential to modern infrastructure. This has led to an increase in the complexity of their dynamics and the challenges of protecting them from advanced cyber threats have escalated. Conventional intrusion detection systems (IDS) often struggle to interpret high-dimensional, sequential data efficiently and extract meaningful features. They are characterized by low accuracy and a high rate of false positives. In this article, we adopt the computational design science approach to design an IDS for ICPS, driven by Generative AI and cognitive computing. Initially, we designed a Long Short-Term Memory-based Sparse Variational Autoencoder (LSTM-SVAE) technique to extract relevant features from complex data patterns efficiently. Following this, a Bidirectional Recurrent Neural Network with Hierarchical Attention (BiRNN-HAID) is constructed. This stage focuses on proficiently identifying potential intrusions by processing data with enhanced focus and memory capabilities. Next, a Cognitive Enhancement for Contextual Intrusion Awareness (CE-CIA) is designed to refine the initial predictions by applying cognitive principles. This enhances the system’s reliability by effectively balancing sensitivity and specificity, thereby reducing false positives. The final stage, Interpretive Assurance through Activation Insights in Detection Models (IAA-IDM), involves the visualizations of mean activations of LSTM and GRU layers for providing in-depth insights into the decision-making process for cybersecurity analysts. Our framework undergoes rigorous testing on two publicly accessible industrial datasets, ToN-IoT and Edge-IIoTset, demonstrating its superiority over both baseline methods and recent state-of-the-art approaches.