Cognitive Maps Research Articles

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

New approaches to improving the student experience at Aberystwyth University libraries: from library surveys to cognitive mapping

PurposeThe findings are based on experience at Aberystwyth Universities libraries using surveys and user experience (UX) activities to identify and understand user requirements.Design/methodology/approachThis paper was initially conceived and developed as a presentation for the LibPMC Conference in July 2023. It is included in this special conference issue for this conference.FindingsThere are challenges to successful integration of UX into user engagement processes across the library and IT services. To improve shared analysis and collaborative ideas generation we developed an interactive data dashboard.Originality/valueThe research is based on practical approaches to improving our understanding of student use and requirements at Aberystwyth University libraries. It explores mechanisms used to collect findings from all areas of UX, including library surveys, focus groups and UX activities to be able to analyse and share all student experience data – to be able to analyse themes and generate ideas and actions for development.

Correlates of spatial structure variability in Bushehr port-city: A comprehensive analysis using fuzzy cognitive mapping methodology

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

Sacred Water: Neo-Spiritual Consumerism in Catherine Chanter’s The Well

ABSTRACT Catherine Chanter’s The Well depicts a water-scarce Britain where the fertile farm of the Ardingly couple becomes a site of exploitation by a spiritual community called the Sisters of the Rose. By setting her novel on a farm named The Well, Chanter constructs a spatial map that not only exposes the cult’s manipulative power but also serves as a symbolic microcosm of their capitalist exploitation, highlighting the rise of a phenomenon I term “neo-spiritual consumerism.” Drawing on Fredric Jameson’s concept of “late capitalism” (1991) and his theory of “cognitive mapping” (1992), this study examines how Chanter uses The Well as a spatial narrative instrument to re-mythologize religious narratives as a form of cultural consumption. Through a literary analysis of Chanter’s novel, the study examines her ironic use of religious symbols to delve into the social, psychological, and political implications of associating environmental crises with divine retribution, deflecting attention from the socio-economic systems perpetuating environmental degradation. Chanter’s subversive use of religious symbolism highlights how capitalist frameworks exacerbate ecological vulnerabilities. This research prompts critical assessment of modern spiritual movements and emphasizes capitalism’s pervasive influence in sacred domains, compelling readers to consider alternative worldviews and ethical frameworks for navigating a changing world.

Spatial Behaviors and Mental Representations: Interactions of artistic and scientific perspectives

Contemporary artistic research has successively integrated preoccupations with the experimentation of space and mobility, as well as collective interactions between participants in situations of proximity or remoteness. In the scientific field, an important part of spatial cognition study has recently turned towards understanding the collective aspects of spatial cognition, integrating an understanding of the acquisition of spatial knowledge and studying cognitive maps, as well as research on navigation and individual and collective memorization. In both cases, spatial behaviors and mental representations are fundamental focuses. Using CORES research combining artistic and scientific perspectives as an example, we clarify specific differences within a framework common to the arts and sciences. This article will develop three points: 1) Artistic and scientific research contexts, 2) Interactions between artistic and scientific perspectives, 3) Proposal of a 7-step joint arts and sciences research cycle. In so doing, this study aims to counter the divide between the artistic and scientific fields by demonstrating a shared creative dynamic.

Fintech competencies in emerging markets: Cognitive hybrid decision-making approach

The purpose of this study is to make a priority analysis for the factors affecting fintech competency for emerging markets. A novel 3-stage decision-making model has been established to reach this objective. First, missing evaluations are completed by using recommender systems. The next stage includes the weight calculation of the criteria via quantum spherical fuzzy cognitive maps. The final stage gives information about the ranking of BRIC countries (Brazil, Russia, India, China) for the fintech competency performance. It is defined that technological infrastructure plays the most crucial role in this regard.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

The Cognitive Map Mechanism in Digital Reading: An Experimental Study on the Influence of Textual Representation on Cognitive Effects

ABSTRACTThe Cognitive Map Mechanism explains that different textual representations influence cognitive effects by affecting how readers mentally organize and navigate information. The application of this mechanism in digital reading remains underexplored. We conducted a lab experiment(N = 148) manipulating low/medium/high textual representation through non−/micro−/combined‐annotation modes. Our aim was to clarify how the cognitive map based digital reading variations impact reading comprehension, performance calibration, and reading speed. Sufficient results showed that high‐level textual representation provides superior cognitive effects in all dimensions. It is interesting to discover that medium‐level textual representation impaired cognitive effects compared to low‐level representation. Specifically, metacognition negatively moderated the influence of textual representation level on reading speed, suggesting that texts with both micro‐annotations and graphic organizers can provide comprehensive cues and visual correlations to build cognitive map, thereby enhancing cognitive effects. Our study contributes novel insights for future research and practical implementation of cognitive‐oriented strategies in digital context.

Mnemonically modulated perceptual processing to represent allocentric space in macaque inferotemporal cortex

To encode allocentric space information of a viewing object, it is important to relate perceptual information in the first-person perspective to the representation of an entire scene which would be constructed before. A substantial number of studies investigated the constructed scene information (e.g., cognitive map). However, only few studies have focused on its influence on perceptual processing. Therefore, we designed a visually guided saccade task requiring monkeys to gaze at objects in different locations on different backgrounds clipped from large self-designed mosaic pictures (parental pictures). In each trial, we presented moving backgrounds prior to object presentations, indicating a frame position of the background image on a parental picture. We recorded single-unit activities from 377 neurons in the posterior inferotemporal (PIT) cortex of two macaques. Equivalent numbers of neurons showed space-related (119 of 377) and object-related (125 of 377) information. The space-related neurons coded the gaze locations and background images jointly rather than separately. These results suggest that PIT neurons represent a particular location within a particular background image. Interestingly, frame positions of background images on parental pictures modulated the space-related responses dependently on parental pictures. As the frame positions could be acquired by only preceding visual experiences, the present results may provide neuronal evidence of a mnemonic effect on current perception, which might represent allocentric object location in a scene beyond the current view.

Category boundaries modulate memory in a place-cell-like manner

Concepts describe how instances of the same kind are related, enabling the categorization and interpretation of new information.1,2 How concepts are represented is a longstanding question. Category boundaries have been considered defining features of concept representations, which can guide categorical inference,3 with fMRI evidence showing category-boundary signals in the hippocampus.4,5 The underlying neural mechanism remains unclear. The hippocampal-entorhinal system, known for its spatially tuned neurons that form cognitive maps of space,6,7 may support conceptual knowledge formation, with place cells encoding locations in conceptual space.4,8,9,10,11 Physical boundaries anchor spatial representations and boundary shifts affect place and grid fields,12,13,14,15,16 as well as human spatial memory,17,18,19 along manipulated dimensions. These place cell responses are likely driven by boundary vector cells, which respond to boundaries at specific allocentric distances and directions,20,21,22,23 the neural correlates of which have been identified in the subiculum and entorhinal cortex20,24,25. We hypothesize similar patterns of memory adaptations in response to shifting category boundaries. Our findings show that after category boundary shifts, participants' memory for category exemplars distorts along the changed dimension, mirroring place field deformations. We demonstrate that the boundary vector cell model of place cell firing best accounts for these distortions compared with alternative geometric explanations. Our study highlights a role of category boundaries in human cognition and establishes a new complementary link between hippocampal coding properties with respect to boundaries and human concept representation, bridging spatial and conceptual domains.

Cohesion Model and Scenario Simulation for Risk Prevention of “Blockchain + Agriculture Crowdfunding” from the Synergistic Perspective

Aiming at the problems such as scattered subjects, static mode and single function, this paper explores a dynamic synergistic risk prevention mode of agricultural crowdfunding that adapts to the age of data intelligence. Based on multiple prevention subjects, the blockchain technology and cohesion principle are introduced into agricultural crowdfunding prevention, and then a risk prevention dynamic model of “blockchain + agricultural crowdfunding” is constructed from the synergistic perspective. Fuzzy cognitive map model and its reasoning mechanism are used to simulate four scenarios of 1-subject, 2-subjects, 3-subjects, and 4-subjects respectively in the dynamic model and the static model. The results show that: as the number of risk prevention subjects increases, so does the cohesion, and the cohesion of multi-subjects reaches a higher state in less time and the steady-state faster, even though the fluctuation range reduces. The risk value for the multi-agent reduces rapidly and reaches a lower risk value in a shorter period of time. Overall, cohesion increases while risk reduces. In the same scenario, dynamic mode cohesion is stronger than static mode cohesion, and it takes less time to attain the steady-state value. Under various scenarios, the dynamic model’s cohesiveness steady-state value increases with the number of subjects, which is stronger than the static model’s. This study not only enriches and develops the theory of risk management of agricultural crowdfunding, but also provides a decision-making basis for the enterprises and the government, etc., to formulate, adjust, and implement risk prevention measures.

PERCEPTIONS OF URBAN LANDSCAPE: A STUDY OF COGNITIVE MAPPING IN AIN BEIDA, ALGERIA

The urban landscape of Algerian cities features a mix of traditional and contemporary architectural forms, offering a diverse range of experiences and perspectives. The heterogeneous nature of this terrain motivated us to investigate the cognitive representation of this scenery in the minds of the inhabitants of Ain Beida.The study emphasizes the cognitive map, which manifests as visual representations, such as drawings or images, as well as sensory experiences and memories that are gathered and stored over a period of time. The perception of this mental image varies among individuals and locations. We conducted a comprehensive analysis of the existing literature to precisely identify the category under investigation and select a suitable approach for evaluating, comprehending, and interpreting the urban landscape of the above-mentioned city.An iconographic was employed as a research instrument selected to carry out a comprehensive survey with a sample of the population encompassing various socio-professional groups, with a primary focus on urban stakeholders such as architects, urban planners, and elected officials. The findings revealed a diverse range of mental maps, indicating the unique perspectives of each interviewee on their city.

Time Series Prediction Based on LSTM and High-Order Fuzzy Cognitive Map with Attention Mechanism

Fuzzy cognitive map (FCM) has been successfully applied to time series prediction due to its powerful dynamic system modeling and inference ability. Although many FCM-based methods have been proposed, their performance is far from satisfactory. The existing FCM-based methods have two limitations: First, the feature extraction of some methods is unreasonable and even leads to overfitting. Second, most methods ignore the local temporal features of time series. In this work, we propose a novel framework for time series prediction based on long short-term memory (LSTM) and high-order FCMs (HFCM) with an attention mechanism, termed LSTM-HFCMAM. To overcome the first limitation, different from other FCM-based methods that use Autoencoder to forcibly decompose each point of the time series into multiple points to represent the original sequences, we use a sliding window to preprocess the original time series and use the Encoder-Decoder framework to represent the sequences. This way makes the model has better generalization and interpretability. Then, HFCM is used to predict representations of sequences due to its powerful causal inference ability. Finally, self-attention is applied to restore the predicted sliding window data with focus, effectively improving the performance. To overcome the second limitation, we use the LSTM to learn the temporal features of time series fragments, thereby learning the local features of the whole time series. We validate the performance of LSTM-HFCMAM on twelve benchmark datasets. Compared with current methods, LSTM-HFCMAM has a maximum improvement of 51.02%. The experimental results demonstrate the effectiveness of LSTM-HFCMAM and overcome the above limitations.

Harnessing Fuzzy Cognitive Maps for Advancing AI with Hybrid Interpretability and Learning Solutions

Advancements that merge the clarity of symbolic AI with the adaptive learning traits of subsymbolic AI show great potential at the intersection of these two AI forms. This study introduces Fuzzy Cognitive Maps (FCMs). This hybrid model integrates the optimal characteristics of both frameworks to address the challenges of interpretability and explainability in artificial intelligence (AI) systems. FCMs provide a robust framework for logically and intuitively supporting decision-making processes and representing causal relationships. Their capacity to handle the inherent vagueness and uncertainty of real-world scenarios enables a more natural and flexible approach to problem-solving. Due to their intrinsic adaptability and learning capabilities derived from sub-symbolic AI, FCMs are particularly suited for applications demanding high levels of interpretability and explainability.

Triangular Fuzzy Cognitive Maps Method for Modelling Interrelation between Causal and Trigger Factors Towards Students' Mathematics Problem-Solving Ability

The purpose of this study is to model the interrelation between causal and trigger factors towards students' mathematics problem-solving ability by using the triangular fuzzy cognitive maps (TrFCM) method. Selection weaknesses and limitations in the method of relational analysis cause the interrelation and influence between variables not to be visualised and do not reveal the characteristics of the actual interaction. As a result, this study demonstrates TrFCM as a more effective way of analysing the relationship between variables based on the complexity that happens in analysing causal factors and triggers for students' problem-solving abilities in mathematics. The results of the influential relations map (IRM) demonstrate that emotion and metacognition are the triggers for problem-solving ability. While executive function is the main cause of success in completing mathematics problems, it is also influenced by additional factors such as motivation, attention, and working memory. These causal and triggering factors also mobilise parts of students' cognitive and behavioural performance to improve the process of solving mathematics problems. Based on the outcomes of this study, computational intelligence methods like fuzzy systems give useful procedures for analysing data from expert surveys. The TrFCM method offers a more accurate relational analysis procedure in modelling interrelation between human factors.

Learning-based framework for industrial accident prevention: fuzzy cognitive mapping approach

PurposeDespite efforts to improve safety management practices in industrial companies, major accidents seem to be inevitable. Many accidents still occur because companies are unable to learn from past occurrences due to ineffective incident and accident learning processes. This study proposes a learning-based framework for industrial accidents investigation and contributes to accident prevention research.Design/methodology/approachThe proposed learning process includes the analysis of the industrial accident using the Event Tree Analysis (ETA) method, capitalisation of causative factors using the Swiss Cheese Model (SCM), and finally modelling the relationships among the accident causative factors and analysing their causality using the Fuzzy Cognitive Mapping (FCM) technique and running learning scenarios.FindingsThe proposed learning process was applied to an industrial accident, and the results showed that human unsafe behaviours and unsafe supervision were the principal causative factors of the blowout accident.Practical implicationsThe proposed learning-based framework provides a structured approach for oil and gas companies to systematically analyse and learn from past accidents, enhancing their prevention strategies. Theoretically, the framework bridges the gap between theory and practice by demonstrating how established accident analysis methods can be combined and applied in a real-world industrial context.Originality/valueThe proposed learning process combines accident analysis and investigation techniques with simulations for an in-depth and robust learning-based framework for accident prevention.

Феномен релокации (Модели экономического поведения российских мигрантов после февраля 2022 г.)

The events of February 2022 became the starting point of a new wave of Russian emigration. On the one hand, this phenomenon can be considered within a series of previous migration waves, on the other hand, the modern context leaves its imprint on the current migration process, making it very special from the economic and sociocultural perspectives. The article presents an attempt to analyze this specificity with an emphasis on the characteristics of the economic behavior of emigrants. Empirically, the study is based on a series of in-depth interviews with people who left Russia after February 2022. On the basis of these interviews, the authors built idealized cognitive models of possible options for the economic behavior of migrants. The authors considered a total of four models: relocation, which involves searching for a job in the host country; relocation, which involves remote work for a Russian company; relocation, which involves creating or moving one’s own business; relocation to a branch of a Russian company. Since the economic strategies of migrants are influenced by signals broadcast by the Russian government structures, the article analyzed the statements of representatives of these structures using the method of network cognitive mapping, which made it possible to reveal two types of official discourse in relation to those who left Russia, which differed in the extent of their criticism towards them. In general, the study showed that migrants of the latest wave are internally heterogeneous and fall into several groups that differ markedly in their motivation and patterns of economic behavior. At the same time, a significant part of them continues to cooperate with Russia in one way or another and, in principle, is ready to return to Russia. For them, migration is driven largely by security and/or economic reasons. Only a minority of emigrants left the country due to their disagreement with the current political course and consider the possibility of coming back only if the political course changes radically.

Evaluating the vulnerability of forestry supply chains through fuzzy cognitive map

Recognizing the significant role of the supply chain in economic activity, it is crucial to address its vulnerability areas. This research focuses on the forestry industry, where obtaining and maintaining the primary raw material source is notably more challenging compared to other supply chains. The primary objective of this study is to analyze vulnerability factors specific to the forestry supply chain (FSC) by modeling their interactions and potential influences on each other. Initially, relevant factors were identified through a comprehensive literature review, encompassing vulnerabilities both general supply chain and pertinent to forestry industry such as natural disruptions. Then, using fuzzy cognitive map (FCM), the relationships between these factors were modeled and simulated. An influence map was constructed based on expert opinions, facilitated by triangular fuzzy numbers to express expert judgments accurately. FCM simulations were conducted to analyze the effects of vulnerability factors of FSC on one another across three sustainability-themed scenarios: economically related vulnerabilities, socially related vulnerabilities, and environmentally related vulnerabilities. This research is significant for stakeholders in the forestry industry as it elucidates the vulnerability factors within the forestry supply chain and demonstrates how different vulnerabilities can influence one another.

How to Help Older Adults Navigate Through VR? Effects in Outdoor–Indoor Transition Environments

In more complex outdoor–indoor transition scenarios, people were more prone to getting lost due to difficulties in effectively integrating proprioceptive cues and external navigation cues. Previous research had mostly focused on spatial perception driven by head or limb movements, with less discussion on locomotion methods that involved significant visual discrepancies. The challenge in designing navigation systems lies in balancing the enhancement of wayfinding performance with the potential negative impact on spatial cognition. We believe that navigation legibility might be a key optimization approach. Additionally, the differences in performance between different age groups were also worth investigating. Therefore, this study aimed to explore how locomotion methods and navigation legibility affected wayfinding performance, navigation interaction, navigation regression, and spatial cognition in younger and older adults during outdoor–indoor transition scenarios. Twenty-four younger (aged 19–27) and 24 older adults (aged 60–83) underwent VR wayfinding tasks. Participants initially followed virtual phone navigation from outdoor starting points to indoor endpoints, then drew cognitive route maps. Five main findings emerged: Continuous teleportation positively affected navigation performance and global representation of routes. Improving navigation legibility boosted navigation interaction, navigation regression, and global representation of routes. Older adults engaged more in navigation interaction but less in navigation regression, exhibiting poorer performance and route representation. Location-specific differences were observed in locomotion, legibility, and age effects. Furthermore, most older adults and over half of younger adults displayed a deflection effect based on outdoor–indoor (OI) space in route representation. These findings indicated that continuous teleportation and enhanced navigation legibility could improve user experience and spatial cognition, highlighting the importance of considering user age and the characteristics of outdoor–indoor transition scenarios when optimizing navigation methods.