Cognitive Maps Research Articles

Fuzzy and Neutrosophic Cognitive Maps Analysis of the Emergence of COVID-19 Post-vaccination

After several years of the spread of coronavirus disease 2019 (COVID-19) around the world occurred, World Health Organization (WHO) has stimulated great efforts to develop a vaccine against the COVID-19. Therefore, all of citizen is obliged to take the vaccine for precaution. However, the vaccine is only one step to reduce the spreadness of COVID-19, where the appearance of COVID-19 can occur after vaccination. Some peoples do not understand the vaccine function where it is used to prevent spreading COVID-19. This research analysed the emergence of COVID-19 post-vaccination by using the method of fuzzy and neutrosophic cognitive maps. The collected data from an interview with two experts called as a concept and from there the directed graph and adjacency matrix are constructed. Multiplication between vectors and matrices are repeated until the fixed point in finding the hidden pattern for all the concepts for COVID-19 post-vaccination obtained. The results obtained from this study are useful to all to know the factors caused of the epidemic after vaccinated as well as the procedures to be followed to protect from this disease.

Distributed midbrain responses signal the content of positive identity prediction errors

Recent work across species has shown that midbrain dopamine neurons signal not only errors in the prediction of reward value but also in the prediction of value-neutral sensory features. To support learning of associative structures in downstream areas, identity prediction errors (iPEs) should signal specific information about the mis-predicted outcome. Here, we used pattern-based analysis of functional magnetic resonance imaging (fMRI) data acquired during reversal learning to characterize the information content of iPE responses in the human midbrain. We find that fMRI responses to value-neutral identity errors contain information about the identity of the unexpectedly received reward (positive iPE+) but not about the identity of theomitted reward (negative iPE-). Exploratory analyses revealed representations of iPE- in the dorsomedial prefrontal cortex. These results demonstrate that ensemble midbrain responses to value-neutral identity errors convey information about the identity of unexpectedly received outcomes, which could shape the formation of novel stimulus-outcome associations that constitute cognitive maps.

Logistics SMEs’ marketing budget re-engineering for sustainable digital marketing development

Nowadays, the survival and sustainability of SMEs and the promotion of their services have become key goals for the economic growth of many countries. Logistics SMEs seek to improve their digital marketing development, by managing the re-engineering process of their marketing budget. To achieve this goal, logistics SMEs should capitalize on big data from their websites that are capable of depicting their digital development level. The authors selected 5 SMEs from the logistics sector and collected big data from their websites for 365 days. Then, statistical analysis tools (correlation and linear regression models), as well as Fuzzy Cognitive Mapping (FCM) models were utilized. It is highlighted that various marketing budget levels affect the digital development of logistics SMEs differently. More specifically, a marketing budget of 500$ to 2500$ could bring more users (new and returning) to logistics SMEs’ websites, but only if the investments transcend 5000$ could help these firms rank higher in the search engines (authority score). Thus, logistics SMEs could simulate the course of specific digital development metrics by setting the desired levels of their marketing budget.

Concurrent vertical and horizontal federated learning with fuzzy cognitive maps

Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model without compromising the privacy of their data. However, a significant challenge arises from the differences in feature spaces among participants, known as non-IID data. This research introduces a novel federated learning framework employing fuzzy cognitive maps, designed to comprehensively address the challenges posed by diverse data distributions and non-identically distributed features in federated settings. The proposal is tested through several experiments using four distinct federation strategies: constant-based, accuracy-based, AUC-based, and precision-based weights. The results demonstrate the effectiveness of the approach in achieving the desired learning outcomes while maintaining privacy and confidentiality standards.

Replay-triggered brain-wide activation in humans

The consolidation of discrete experiences into a coherent narrative shapes the cognitive map, providing structured mental representations of our experiences. In this process, past memories are reactivated and replayed in sequence, fostering hippocampal-cortical dialogue. However, brain-wide engagement coinciding with sequential reactivation (or replay) of memories remains largely unexplored. In this study, employing simultaneous EEG-fMRI, we capture both the spatial and temporal dynamics of memory replay. We find that during mental simulation, past memories are replayed in fast sequences as detected via EEG. These transient replay events are associated with heightened fMRI activity in the hippocampus and medial prefrontal cortex. Replay occurrence strengthens functional connectivity between the hippocampus and the default mode network, a set of brain regions key to representing the cognitive map. On the other hand, when subjects are at rest following learning, memory reactivation of task-related items is stronger than that of pre-learning rest, and is also associated with heightened hippocampal activation and augmented hippocampal connectivity to the entorhinal cortex. Together, our findings highlight a distributed, brain-wide engagement associated with transient memory reactivation and its sequential replay.

Egocentric neural representation of geometric vertex in the retrosplenial cortex

Egocentric neural representations of environmental features, such as edges and vertices, are important for constructing a geometrically detailed egocentric cognitive map for goal-directed navigation and episodic memory. While egocentric neural representations of edges like egocentric boundary/border cells exist, those that selectively represent vertices egocentrically are yet unknown. Here we report that granular retrosplenial cortex (RSC) neurons in male mice generate spatial receptive fields exclusively near the vertices of environmental geometries during free exploration, termed vertex cells. Their spatial receptive fields occurred at a specific orientation and distance relative to the heading direction of mice, indicating egocentric vector coding of vertex. Removing physical boundaries defining the environmental geometry abolished the egocentric vector coding of vertex, and goal-directed navigation strengthened the egocentric vector coding at the goal-located vertex. Our findings suggest that egocentric vector coding of vertex by granular RSC neurons helps construct an egocentric cognitive map that guides goal-directed navigation.

Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling

The circular economy has emerged as a crucial way for companies to achieve their sustainability goals. Numerous businesses, especially small and medium-sized enterprises (SMEs), are integrating circular-economy projects into their operations. However, this undertaking presents multiple challenges as many managers must grapple with constraints in resources and expertise. This study’s primary objective is to develop a process-oriented decision-making system designed to deal with complex circular-economy scenarios. The proposed analysis system can help SMEs identify the driving forces behind circular-economy principles and evaluate the intricate connections between these determinants, using a unique combination of multiple criteria decision analysis methods (i.e., cognitive mapping, and interpretive structural modeling). Collaborative sessions involving circular-economy experts were instrumental in refining the analysis system, and in-depth discussions with other specialists from the International Labor Organization further enriched this decision-support system. The findings include that circular-economy drivers can be grouped into five clusters: products, processes, policies/regulations, attitudes/behaviors, and communication/awareness. This structured breakdown provides SMEs with the tools to comprehend and address the pivotal factors that shape circular-economy initiatives. This pioneering study thus produced a comprehensive decision-making model attuned to the intricacies of the circular economy while highlighting the benefits of collaborative endeavors involving industry experts and global decision makers.

Разработка концепции познавательного картографирования

The article deals with the idea of cognitive mapping, which is going through a new round of development today and is aimed at understanding the surrounding world with the help of specialized cartographic products, taking into account certain aspects. The author indicates the main objectives of government programs related to spreading scientific knowledge and development of civil science, stated by the government of the Russian Federation. The previously published cognitive cartographic products are examined; the research method and that of cognitive mapping are used. A list of corresponding production is given; it requires theoretical justification and description, as well as developing conceptual components of geospatial cognitive models-and-map-creating idea. Three aspects of the concept of cognitive mapping are described; they are personal characteristics of the user, functional features of the mentioned method and principles for creating geospatial cognitive models and maps

Mental models of aquaculture governance in Indonesia

Aquaculture stakeholders have mental models, which are the internal cognitive representations of how they understand and prioritize the different features of their aquaculture systems. Individuals and stakeholder groups are likely to have different mental models, with implications for making cooperative governance work and guiding the rapidly emerging sector’s sustainable development. We apply a participatory approach called fuzzy cognitive mapping to capture and compare the mental models of community-based coastal pond aquaculture stakeholders in Indonesia, including farmers, government managers, and researchers who need to work together to govern a rapidly expanding aquaculture sector which faces critical sustainability challenges. To conceptually structure our comparison, we use Elinor Ostrom’s social–ecological systems framework. Our results highlight important differences between stakeholder group mental models which represent potential conflicts of interest and barriers for collaborative governance. Fish farmer models emphasize resource system challenges relating to production instability and risk, while government managers emphasize increasing production intensity to meet sectoral growth targets. Researchers, in contrast, tend to focus on pond waste treatment and water quality management. Governance attributes were consistently perceived as less frequent and less influential compared to other social–ecological dimensions, reflecting perceptions of weak governance in the sector. We identify a critical need for programs aimed at strengthening community-level institutional arrangements for governing shared aquaculture resources, increasing technical knowledge capacity, and managing financial risk. By merging all stakeholder models into a single “community” model, we identify key consensus action situations across the three groups as potential focal points for aquaculture development which may serve as a starting point for actors to work together to identify context-appropriate institutional solutions to these sustainability challenges.

Integrating knowledge systems for holistic approaches to addressing knowledge and health inequities: combining fuzzy cognitive maps.

This paper describes fuzzy cognitive mapping as an accessible and robust tool to strengthen community engagement in health promotion research. We outline how fuzzy cognitive mapping can combine, compare, and contextualize knowledge and priorities from diverse population groups as well as from evidence syntheses. We present procedures to represent a shared perspective across populations or population groups through reconciling maps by simple or weighted averaging. We present a novel second approach to reconciling derived from discourse analysis. We then present two procedures to contextualize one knowledge in another knowledge. The first procedure draws on Bayesian updating, providing a formal way to account for stakeholder knowledge in contextualizing other knowledge sources, including evidence syntheses. A second approach compares discourse patterns across maps derived from different sources. We provide examples of each procedure, describe how each may contribute to greater incorporation of patient- and community-level input in decision-making, and share tools for researchers interested in applications of fuzzy cognitive mapping.

Learning, Fast and Slow: Single- and Many-Shot Learning in the Hippocampus.

The hippocampus is critical for memory and spatial navigation. The ability to map novel environments, as well as more abstract conceptual relationships, is fundamental to the cognitive flexibility that humans and other animals require to survive in a dynamic world. In this review, we survey recent advances in our understanding of how this flexibility is implemented anatomically and functionally by hippocampal circuitry, during both active exploration (online) and rest (offline). We discuss the advantages and limitations of spike timing-dependent plasticity and the more recently discovered behavioral timescale synaptic plasticity in supporting distinct learning modes in the hippocampus. Finally, we suggest complementary roles for these plasticity types in explaining many-shot and single-shot learning in the hippocampus and discuss how these rules could work together to support the learning of cognitive maps.

A non-memory-based functional neural framework for animal caching behavior

The brain’s extraordinary abilities are often attributed to its capacity to learn and adapt. But memory has its limitations, especially when faced with tasks such as retrieving thousands of food items—a common behavior in scatter-hoarding animals. Here, we propose a brain mechanism that may facilitate caching and retrieval behaviors, with a focus on hippocampal spatial cells. Rather than memorizing the locations of their caches, as previously hypothesized, we suggest that cache-hoarding animals employ a static mechanism akin to hash functions commonly used in computing. Our mathematical model aligns with the activity of hippocampal spatial cells, which respond to an animal’s positional attention. We know that the region that activates each spatial cell remains consistent across subsequent visits to the same area but not between areas. This remapping, combined with the uniqueness of cognitive maps, produces persistent hash functions that can serve both food caching and retrieval. We present a simple neural network architecture that can generate such a probabilistic hash that is unique to the animal and not sensitive to environmental changes. This mechanism could serve a virtually boundless capacity for the encoding of any structured data.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Self‐location in perceptual experience: A top‐down account

Perceptual experience is self‐locating. This claim aligns with our intuitions and is the dominant view in philosophy. To defend the claim, some philosophers have advanced perspectival accounts and others have advanced agentive accounts. Here, I explore tensions between the two accounts and propose a novel, integrative account: the top‐down view, which defends that visual experience is self‐locating in virtue of cognitive maps that modulate visual processing in a top‐down fashion. I assess recent neuroscientific evidence of spatial modulation in the visual cortex and show how it turns existing notions of self‐location upside‐down, shifting the focus from bottom‐up to top‐down processes.

Mapping the way to a dark green enterprise: fuzzy cognitive map based analysis of factors affecting environmental performance for sustainable development

Mapping the way to a dark green enterprise: fuzzy cognitive map based analysis of factors affecting environmental performance for sustainable development

Space is a latent sequence: A theory of the hippocampus.

Fascinating phenomena such as landmark vector cells and splitter cells are frequently discovered in the hippocampus. Without a unifying principle, each experiment seemingly uncovers new anomalies or coding types. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves numerous phenomena and suggests that the place field mapping methodology that interprets sequential neuronal responses in Euclidean terms might itself be a source of anomalies. Our model, clone-structured causal graph (CSCG), employs higher-order graph scaffolding to learn latent representations by mapping aliased egocentric sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs yields allocentric cognitive maps that are suitable for planning, introspection, consolidation, and abstraction. By explicating the role of Euclidean place field mapping and demonstrating how latent sequential representations unify myriad observed phenomena, our work positions the hippocampus in a sequence-centric paradigm, challenging the prevailing space-centric view.

Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: The case of Prague

Extreme heat in urban areas has a severe impact on urban populations worldwide. In light of the threats posed by climate change, it is clear that more holistic and people-oriented approaches to reducing heat stress in urban areas are needed. From this perspective we aim to identify and compare thermal hotspots and places with favourable thermal conditions, based on three different methods – thermal walk, participatory-based cognitive mapping, and remote sensing in a Central European city. Although major hotspots in large low-rise development zones were identified by all three methods, the overall agreement between on-site thermal sensation votes, cognitive maps and surface temperatures is low. In the urban canyon of compact mid-rise and open mid-rise development, the thermal walk method proved to be useful in the identification of the specific (parts of) streets and public spaces where citizens can expect thermal discomfort and experience heat stress, e.g. crossroads, arterial streets with a lack of greenery, north facing unshaded parts of streets, and streets with inappropriate tree spacing. Cognitive maps on an urban neighbourhood scale are not specific enough on a street level; however, as a supplementary method they can help identify discrepancies between on-site sensations and thermal conditions. For further research on effective and cost-efficient urban heat mitigation, we suggest combining thermal walks with numerical model simulations.

Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting

Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting

Сценарное моделирование протестного потенциала на основе событийной идентификации динамики факторов

In this study, we present a scenario-based approach to investigate the protest potential in society using mathematical tools from graph theory and simulation modeling. The research objective. The research aims to construct a cognitive map that reflects key factors, such as population deprivation levels, overall well-being, social fears, trust in authority, social expectations, and stability of social structures. Materials and methods. The model of functional oriented graphs, which in turn is a development of the classical cognitive model, is used as a metamathematical apparatus in the study. In the software implementation, the method of linear regression data analysis was used to calculate e-scenarios for the behavior of vertices. Results. We report the results of a scenario analysis of the developed model, obtaining forecasted scenarios of the dynamics of society’s protest potential under various conditions and influences. At the core of this research is the examination of a complex system’s possible response to external destructive impacts, and we propose a set of measures to counteract these influences on societal dynamics. The scenario methodology combines graph theory for representing complex relationships between key factors and simulation modeling for predicting potential developmental scenarios. Conducting this study required modifying the traditional mathematical apparatus of cognitive modeling. We developed methods and algorithms for scenario-event identification of the model’s significant factors’ behavior and utilizing the results of such identification when forming complex functional relationships in the scenario model. In generating scenarios, the range of events is significantly expanded compared to traditional cognitive modeling approaches, which could have been overlooked but may be crucial for situational analysis. Conclusion. The scenario modeling technology presented in the work is implemented in the appropriate software and analytical complex, the purpose of which is to automate the processes of scenario research of socio-economic and political systems.

A holistic framework to model student's cognitive process in mathematics education through fuzzy cognitive maps

This study introduces a pioneering framework for modeling students' cognitive processes in mathematics education through Fuzzy Cognitive Maps (FCMs). By integrating key educational theories—Duval's Semiotic Representation Theory, Niss's Mathematical Competencies, Marton's Variation Theory, and the broad Engagement, Motivation, and Participation framework— the model offers a comprehensive and holistic understanding of students' cognitive landscapes. This research underscores the necessity of a multidimensional approach to capturing the intricate interplay of cognitive, affective, and behavioral factors in students' mathematical learning experiences. The novelty lies in its methodological innovation, employing FCMs to transcend traditional qualitative analyzes and facilitate quantitative insights into students' cognitive processes. This approach is particularly relevant in the current era dominated by digital learning environments and artificial intelligence, where real-time, automated analysis of student interactions is increasingly vital. The proposed FCM has been developed over the years with a data-driven approach; the concepts and relationships in it have been derived from the literature and refined by the author's experience in the field. Illustrated through case studies, the framework's utility is demonstrated in diverse contexts, highlighting how the quantitative data obtained are confirmed by qualitative approach: analyzing the impact of remote learning during the Covid-19 pandemic on student engagement and exploring Augmented Reality's role in enhancing mathematical conceptualization. These applications show the framework's adaptability and its potential to integrate new technologies in educational practices. However, the transition from qualitative to quantitative methodologies poses a challenge, given the prevalent use of qualitative approaches in mathematics education research. Additionally, the technological implementation of the FCM model in educational software presents practical hurdles, necessitating further development to ensure ease of integration and use in real-time educational settings. Future work will focus on bridging these methodological gaps and overcoming technological challenges to broaden the FCM model's applicability and enhance its contribution to advancing mathematics education.