Cognitive Maps Research Articles

Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex.

How does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.

Sustaining altitude pastures in mountain landscapes–a fuzzy cognitive model approach

Similarly to other European mountain areas, in Serra da Estrela the grazing pressure has been reducing due to social and economic drivers that have pushed shepherds and sheep to the foothill, or plainly out of the sector. Shrub encroachment on commons and other previously grazed land is one of the most tangible effects of pastoral abandonment in Serra de Estrela. The impacts of the resulting increase in landscape continuity and biomass availability were made clear in the severe fires of 2017 and 2022. As fire risk is likely to increase with climate change, it becomes urgent to understand what strategies can be deployed to keep fragmentation in these landscapes. Key actors such as shepherds should be involved in this discussion to understand their perceptions, points of view and reasons for abandoning upland pastures. In this study, we use fuzzy cognitive mapping to identify the key variables and mechanisms affecting the pastoral system according to local shepherds. In our study, we developed with local stakeholders a framework outlining the local pastoral system. Based on that, we carried out the fuzzy cognitive mapping collecting 14 questionnaires. We found that shepherds' income is a central issue, but that it is highly dependent on many factors. Increasing the Common Agricultural Policy payments alone is not enough to incentivise the use of upland pastures. More targeted strategies, such as more support for shrub clearing, and direct payments conditional to transhumance are more impactful. Despite a contentious discourse between conservation and shepherding values in Serra da Estrela, we find that shepherd's values are aligned with biodiversity conservation and a potential nature-based solution for minimizing fire risk through woody fuel management. This opens up possibilities for new governance strategies, that put Serra da Estrela's social, environmental and cultural values at its core.

Directions of improving management of innovative activities in pharmaceutical companies

The purpose of the work is to substantiate theoretical provisions and practical recommendations for improving the management of innovative activities of pharmaceutical companies (PC) for the implementation of strategic tasks regarding innovative development and increasing competitiveness. Materials and methods. To achieve the goal of the study, scientific sources on innovation management and intellectual property management and the peculiarities of the organization of innovation processes in pharmacy were analyzed. When conducting research, methods of information search, systematization, comparison, and generalization were used, as well as the method of expert evaluations, taxonomic analysis, and a graphic method to increase the clarity of the presentation of the material. The results. The main directions and features of innovative activity in pharmacy have been studied. A generalized algorithm for managing innovative activities in pharmaceutical companies is given. The relevance of evaluating and managing the innovative potential of PC to achieve a synergistic effect from the use of resources involved in innovative activities is substantiated. A structural model of the innovative potential of PC is proposed, which considers the peculiarities of the flow of innovative processes in pharmacy, related to the development and introduction of new pharmaceuticals to the market. A methodical toolkit for quantitative and qualitative evaluation of the innovative potential of PC has been defined. To improve the management of the PC's innovative activity, a methodology for determining the range of management decisions is proposed based on the results obtained in the framework of creating a report on the patent landscape. Conclusions. In order to achieve the provision of effective and safe domestic drugs to the population of Ukraine, it is necessary to intensify research in the direction of the formation of an effective system of management of the innovative activity of pharmaceutical companies (PC), focused on their gradual transition to an innovative development model. The conducted analysis showed that despite a sufficiently large number of publications devoted to the problems of innovation management, issues related to the problem of forming an effective system of managing the innovative activity of PCs, as well as choosing an effective toolkit for evaluating and managing their innovative potential, taking into account the specifics of the flow of innovative processes in pharmacy, remain insufficiently disclosed and require further research. The proposed structural model of the innovative potential of the FC, a justified methodological toolkit for its assessment, as well as the construction of a cognitive map to determine the most appropriate key components and directions for further improvement and development of the innovative potential will contribute to increasing the efficiency of the innovative activities of enterprises and the implementation of the goals of the innovation strategy. The developed algorithm of preventive management of innovative activity of the PC, aimed at improving the quality of decisions made on innovation management thanks to the use of patent landscape tools, will also contribute to the faster entry into the market of domestically produced innovative medicinal products

Backpropagation through time learning for recurrence-aware long-term cognitive networks

Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it have firmly established their strong position in the landscape of machine learning algorithms. Specifically designed for pattern classification and multi-output regression, the recently introduced Recurrence-aware Long-term Cognitive Network (r-LTCN) model is one of these FCM-inspired extensions. On the one hand, this recurrent neural network connects all temporal states generated during the reasoning process with the decision-making layer. On the other hand, it uses a quasi-nonlinear reasoning rule devoted to avoiding convergence issues caused by unique fixed points, which typically emerge in other FCM models. In the original paper, the authors employed a combination of unsupervised and supervised learning to compute the r-LTCNs’ learnable parameters. Despite r-LTCNs’ astounding performance for a wide variety of pattern classification problems, the literature reports no attempt to train these recurrent neural systems in a fully supervised manner nor provide insights into their performance in other machine learning settings. This paper brings forward a modified Backpropagation Through Time learning (BPTT) algorithm devoted to training r-LTCN models used for multi-output regressions tasks rather than pattern classification. The proposed BPPT includes a simple yet effective mechanism to deal with the vanishing gradient within the recurrent layer that operates as a closed system while being tailored to the quasi-nonlinear reasoning mechanism. Empirical evaluation of the proposed BPTT algorithm using 20 multi-output regression problems reveals that it produces lower prediction errors compared with other state-of-the-art learning approaches.

Multivariate time series clustering based on fuzzy cognitive maps and community detection

Most time series clustering methods mainly focus on univariate time series (UTS). Compared with UTS, multivariate time series (MTS) consists of multiple components. Although interest in MTS clustering is increasing, its performance is far from satisfactory. Most traditional MTS clustering methods may have two limitations. First, they do not consider both the temporal features of each component and the relationship between the components. Second, they can only identify the local relationship between adjacent data, but cannot obtain long-distance global relationships and capture clusters of arbitrary shapes. In this paper, we develop a method for MTS clustering based on fuzzy cognitive maps (FCMs) and community detection, termed as MTSC-FCM-CD. To overcome the first limitation, we use FCM to represent MTS; FCM can extract temporal features while preserving the relationship between components in MTS. To overcome the second limitation, we use the community detection algorithm to cluster the global relations, which is different from the traditional nearest neighbor distance-based method. In the calculation process, the similarity between FCMs should be used to build a complex network. Existing methods calculate the similarity between FCMs, only considering the numerical characteristics but ignoring the topological structure. To solve this problem, we design a two-stage similarity measure to build a complex network. In comparison to the existing methods, the experimental results on twenty benchmark datasets demonstrate the effectiveness of MTSC-FCM-CD in MTS clustering.

A framework for understanding the human experience of nature through cognitive mapping.

Human behavior is a key driver of the biodiversity crisis, and addressing it requires changing individual choices and actions. Yet, the same processes that imperil biodiversity (e.g., urbanization) also alienate people from the experience of nature, eroding care for the natural world. Although averting this extinction of experience is increasingly recognized as a major contemporary conservation challenge, understanding of what constitutes nature experience remains elusive and few empirical studies have explored it directly. Most researchers have used nature interactions as a stand-in for experience, even though experience extends beyond interactions. We aimed to determine what constitutes the experience of nature and to propose a holistic, empirically derived framework that incorporates the multiple dimensions and components of the experience of nature. Using a mixed-method approach across 3 countries (the United States, Switzerland, and Israel), we conducted a multistage, conceptual content, cognitive mapping (3CM) exercise with 106 participants. This methodology included developing a prompt to capture participants' perceptions of nature experiences and subsequently refining and organizing their input into distinct components and underlying dimensions through an iterative engagement process. Beyond multisensory interactions with nature, experience of nature consisted of 2 dimensions: the circumstances in which interactions occur and the internal responses that encompass various cognitive, affective, and restorative benefits associated with nature interactions. These 3 dimensions had 33 components that occurred consistently across participants in the 3 countries. Frequently mentioned components included seeing animals, landscapes, or scenery; lack of human influence; weather conditions; relaxing, recharging; feeling good; and awe for nature. Fear and nature experienced at home were the least mentioned components. Together, our results showed that nature experience is a combination of nature interactions, circumstances, and internal responses. The emphasized components underscore the significance of offering access to extensive, less human-influenced natural spaces. This in turn can foster a profound nature experience, cultivating feelings of connectedness and care for nature.

Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy

Accurate electricity consumption forecasting is the crucial to ensure the stable operation of the power system and optimize the smart grid dispatch. However, existing methods may not effectively capture complex potential features and change patterns, and are susceptible to noise, leading to deficiencies such as suboptimal electricity consumption forecasting accuracy, poor stability, and weak generalization capabilities. To fill this gap, a high order fuzzy cognitive map (FCM) forecasting method based on sample entropy and minimax concave penalty (MCP) regularization is proposed. Firstly, two mode decomposition methods are innovatively fused by utilizing sample entropy to extract the complex features of electricity consumption data, then a high order FCM model with powerful system state prediction capability and interpretable knowledge representation is constructed, and finally a FCM learning method based on MCP regularization is proposed. The forecasting method has the merits of low forecasting error, high stability and strong generalization capabilities. Comprehensive experimental results on multiple electricity consumption datasets show that the proposed method achieves optimal performance compared to 10 state-of-the-art baseline methods, demonstrating effectiveness in the task of electricity consumption forecasting, which contributes to more efficient energy management and cost savings, and provides a reliable tool for smart grid planning and management.

Altered grid-like coding in early blind people

Cognitive maps in the hippocampal-entorhinal system are central for the representation of both spatial and non-spatial relationships. Although this system, especially in humans, heavily relies on vision, the role of visual experience in shaping the development of cognitive maps remains largely unknown. Here, we test sighted and early blind individuals in both imagined navigation in fMRI and real-world navigation. During imagined navigation, the Human Navigation Network, constituted by frontal, medial temporal, and parietal cortices, is reliably activated in both groups, showing resilience to visual deprivation. However, neural geometry analyses highlight crucial differences between groups. A 60° rotational symmetry, characteristic of a hexagonal grid-like coding, emerges in the entorhinal cortex of sighted but not blind people, who instead show a 90° (4-fold) symmetry, indicative of a square grid. Moreover, higher parietal cortex activity during navigation in blind people correlates with the magnitude of 4-fold symmetry. In sum, early blindness can alter the geometry of entorhinal cognitive maps, possibly as a consequence of higher reliance on parietal egocentric coding during navigation.

Multivariate mapping of low-resilient neurocognitive systems within and around low-grade gliomas.

Accumulating evidence suggests that the brain exhibits a remarkable capacity for functional compensation in response to neurological damage, a resilience potential that is deeply rooted in the malleable features of its underlying anatomofunctional architecture. This propensity is particularly exemplified by diffuse low-grade glioma, a subtype of primary brain tumour. However, functional plasticity is not boundless, and surgical resections directed at structures with limited neuroplasticity can lead to incapacitating impairments. Yet, maximizing diffuse low-grade glioma resections offers substantial oncological benefits, especially when the resection extends beyond the tumour margins (i.e. supra-tumour or supratotal resection). In this context, the primary objective of this study was to identify which cerebral structures were associated with less favourable cognitive outcomes after surgery, while accounting for intra-tumour and supra-tumour features of the surgical resections. To achieve this objective, we leveraged a unique cohort of 400 patients with diffuse low-grade glioma who underwent surgery with awake cognitive mapping. Patients benefitted from a neuropsychological assessment consisting of 18 subtests administered before and 3 months after surgery. We analysed changes in performance and applied topography-focused and disconnection-focused multivariate lesion-symptom mapping using support vector regressions, in an attempt to capture resected cortico-subcortical structures less amenable to full cognitive compensation. The observed changes in performance were of a limited magnitude, suggesting an overall recovery (13 of 18 tasks recovered fully despite a mean resection extent of 92.4%). Nevertheless, lesion-symptom mapping analyses revealed that a lack of recovery in picture naming was linked to damage in the left inferior temporal gyrus and inferior longitudinal fasciculus. Likewise, for semantic fluency abilities, an association was established with damage to the left precuneus/posterior cingulate. For phonological fluency abilities, the left dorsomedial frontal cortex and the frontal aslant tract were implicated. Moreover, difficulties in spatial exploration were associated with injury to the right dorsomedial prefrontal cortex and its underlying connectivity. An exploratory analysis suggested that supra-tumour resections were associated with a less pronounced recovery following specific resection patterns, such as supra-tumour resections of the left uncinate fasciculus (picture naming), the left corticostriatal tract and the anterior corpus callosum (phonological fluency), the hippocampus and parahippocampus (episodic memory) and the right frontal-mesial areas (visuospatial exploration). Collectively, these patterns of results shed new light on both low-resilient neural systems and the prediction of cognitive recovery following glioma surgery. Furthermore, they indicate that supra-tumour resections were only occasionally less well tolerated from a cognitive viewpoint. In doing so, they have deep implications for surgical planning and rehabilitation strategies.

Guiding Fuzzy Cognitive Mapping With Structured Decision Making to Inform Complex Natural Resource Management Problems in Waiʻanae Hawaiʻi

AbstractFire is one example of a larger class of wicked environmental management problems that require community‐based management for long‐term success. This study aimed to support participatory decision making in a data poor and complex system by using Fuzzy Cognitive Mapping (FCM) within the decision‐making framework Structured Decision Making (SDM) to develop a community‐based wildfire management plan in partnership with a local group in Wai‘anae Hawaiʻi. SDM is an organized process to create and evaluate decisions that can clarify trade‐offs and increase transparency. FCM is a semi‐quantitative method to describe system relationships that can handle low data and high uncertainty. We used SDM to define the problem, articulate community concerns related to wildfire, and identify management actions. We then used FCM to model how these actions affected valued ecosystem services and other stakeholder‐identified objectives. Modeling results highlighted the trade‐offs across three valued ecosystem services from three community‐led wildfire management actions. We found that native species outplanting was the most beneficial action to each of the three valued ecosystem services. Through this participatory research, we conclude that the SDM and FCM pairing provided an inclusive and cost‐effective way to engage with a specific wildfire management context, and that the process effectively engaged stakeholders while tackling uncertainty. This process also aided consensus‐building and group member communication. This pairing can be used to aid community decision‐making across diverse management problems as it provides a way to elicit objectives and model trade‐offs, even under uncertainty and data limitations, while including stakeholders impacted by such decisions.

Blind Federated Learning without initial model

Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two innovative methodologies for Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in a privacy-preserving way. In addition, one relevant contribution this research includes is the lack of an initial model in the federated learning process, making it effectively blind. This proposal is tested with several open datasets, improving both accuracy and precision.

Insights into the structure and function of the hippocampus: implications for the pathophysiology and treatment of autism spectrum disorder.

The hippocampus is one of the brain areas affected by autism spectrum disorder (ASD). Individuals with ASD typically have impairments in hippocampus-dependent learning, memory, language ability, emotional regulation, and cognitive map creation. However, the pathological changes in the hippocampus that result in these cognitive deficits in ASD are not yet fully understood. In the present review, we will first summarize the hippocampal involvement in individuals with ASD. We will then provide an overview of hippocampal structural and functional abnormalities in genetic, environment-induced, and idiopathic animal models of ASD. Finally, we will discuss some pharmacological and non-pharmacological interventions that show positive impacts on the structure and function of the hippocampus in animal models of ASD. A further comprehension of hippocampal aberrations in ASD might elucidate their influence on the manifestation of this developmental disorder and provide clues for forthcoming diagnostic and therapeutic innovation.

ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ ГРАФОВЫХ НЕЙРОННЫХ СЕТЕЙ КАК ИНТЕЛЛЕКТУАЛЬНЫХ ПОМОЩНИКОВ ПРИ ПОСТРОЕНИИ ДИНАМИЧЕСКИХ КОГНИТИВНЫХ КАРТ

Approaches to the study of complex dynamic systems are considered. Methods of graph construc-tion with the help of neural networks are considered. The possibility of using trained generative neural networks to help in building dynamic cognitive maps is presented

Relations between supply chain performance and circular economy implementation: A fuzzy cognitive map‐based analysis for sustainable development

AbstractSupply chain sustainability is now an imperative, particularly considering the circular supply chain (CSC) paradigm, which offers opportunities for performance improvement. Thus, it is possible to infer that there is a causal link between circularity and supply chain performance. However, the analysis of this relationship occurs under subjective perspectives. To computationally process human language can overcome this issue. Hence, this paper aims to propose an approach for assessing the influence of circular economy (CE) implementation principles on supply chain performance using the fuzzy cognitive map (FCM), a well‐established soft computing technique. The study develops a computational model and brings an illustrative application. Methodologically, is model‐based, quantitative, axiomatic and descriptive. It contributes by providing means to support the formulation of strategies for aligning CE implementation efforts with supply chain performance improvement efforts. The FCM visually represents how supply chain performance attributes are influenced by CE principles, supporting informed decision‐making towards circularity.

Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.

Enhanced planning capacity in urban renewal: Addressing complex challenges using neutrosophic logic and DEMATEL

Urban renewal is imperative due to growing urbanization (i.e., population migration into cities), requiring acceptable planning to meet peoples' housing needs and playing a key role in highlighting and satisfying societies' needs. Complex urban renewal decisions encompass environmental degradation, poverty, inequality, and inaccessibility to services. Decision makers must meet on-going challenges by transforming urban spaces so that they satisfy all constraints while satisfying residents' needs. A holistic view of urban renewal facilitates efficient strategies for meeting economic, social, and environmental obligations. Multicriteria methodology may ideally be used to develop a complete, transparent, and realistic model that identifies planning factors best resolving urban renewal efforts. Therefore, we rely on cognitive mapping, the decision-making trial and evaluation laboratory technique, and neutrosophic logic, applied by a panel of experts with experience in urban renewal planning in two group work meetings. The results were analyzed and validated by an independent specialist in this field. The contributions and limitations of this methodological framework were also examined.

Conjunctive encoding of exploratory intentions and spatial information in the hippocampus

The hippocampus creates a cognitive map of the external environment by encoding spatial and self-motion-related information. However, it is unclear whether hippocampal neurons could also incorporate internal cognitive states reflecting an animal’s exploratory intention, which is not driven by rewards or unexpected sensory stimuli. In this study, a subgroup of CA1 neurons was found to encode both spatial information and animals’ investigatory intentions in male mice. These neurons became active before the initiation of exploration behaviors at specific locations and were nearly silent when the same fields were traversed without exploration. Interestingly, this neuronal activity could not be explained by object features, rewards, or mismatches in environmental cues. Inhibition of the lateral entorhinal cortex decreased the activity of these cells during exploration. Our findings demonstrate that hippocampal neurons may bridge external and internal signals, indicating a potential connection between spatial representation and intentional states in the construction of internal navigation systems.

The use of artificial intelligence in the diagnosis and detection of complications of diabetes

Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies.
 Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts.
 Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.

Analysis of hippocampal local field potentials by diffusion mapped delay coordinates

Spatial navigation through novel spaces and to known goal locations recruits multiple integrated structures in the mammalian brain. Within this extended network, the hippocampus enables formation and retrieval of cognitive spatial maps and contributes to decision making at choice points. Exploration and navigation to known goal locations produce synchronous activity of hippocampal neurons resulting in rhythmic oscillation events in local networks. Power of specific oscillatory frequencies and numbers of these events recorded in local field potentials correlate with distinct cognitive aspects of spatial navigation. Typically, oscillatory power in brain circuits is analyzed with Fourier transforms or short-time Fourier methods, which involve assumptions about the signal that are likely not true and fail to succinctly capture potentially informative features. To avoid such assumptions, we applied a method that combines manifold discovery techniques with dynamical systems theory, namely diffusion maps and Takens’ time-delay embedding theory, that avoids limitations seen in traditional methods. This method, called diffusion mapped delay coordinates (DMDC), when applied to hippocampal signals recorded from juvenile rats freely navigating a Y-maze, replicates some outcomes seen with standard approaches and identifies age differences in dynamic states that traditional analyses are unable to detect. Thus, DMDC may serve as a suitable complement to more traditional analyses of LFPs recorded from behaving subjects that may enhance information yield.

Empowerment of individuals in Iranian health systems: a qualitative study using the Z-cognitive map approach

The empowerment of people is considered as one of the most effective approaches in national healthcare systems. Identifying the effective criteria for this empowerment approach can be useful for planning enhancements. Therefore, studying and researching different aspects of people empowerment, and identifying the various relationships among related factors are of great importance. In this study – after identifying and extracting the effective factors in empowering individuals/insured persons, and interviewing health insurance and healthcare experts through content analysis – a causal model examining variables and their impact intensity through cognitive mapping is designed and drawn up. In modeling the concept of empowerment, to cover the ambiguity of expert comments, a combination of the Z-number approach with cognitive mapping has been used. Results demonstrate how various factors relate to insured empowerment. According to the results of empowerment strategies, the insurance participation strategy with the highest central index was determined as the most effective strategy, and the appropriate component for individuals gained the highest score in the centrality index. The results of this article help a lot to policy making in medical insurance.