Cognitive Map Theory Research Articles

Impoverished details with preserved gist in remote and recent spatial memory following hippocampal and fornix lesions

IntroductionCognitive Map Theory predicts that the hippocampus (HPC) plays a specialized, time-invariant role in supporting allocentric spatial memory, while Standard Consolidation Theory makes the competing prediction that the HPC plays a time-limited role, with more remote memories gaining independence of HPC function. These theories, however, are largely informed by the results of laboratory-based tests that are unlikely to simulate the demands of representing real-world environments in humans. Validation of these theories is further limited by an overall focus on spatial memory of newly encountered environments and on individuals with extensive lesions to the HPC and to surrounding medial temporal lobe (MTL) regions. The current study incorporates naturalistic tests of spatial memory based on recently and remotely encountered environments navigated by individuals with lesions to the HPC/MTL or that are limited to the HPC's major output, the fornix. MethodsFour participants with bilateral HPC/MTL and/or fornix lesions drew sketch maps of recently and remotely experienced neighbourhoods and houses. Tests of the appearance, distances, and routes between landmarks from the same real-world environments were also administered. Performance on the tasks was compared to that of control participants closely matched in terms of exposure to the same neighbourhoods and home environments as well as to actual maps. ResultsThe performance of individuals with fornix/MTL lesions was found to be largely comparable to that of controls on objective tests of spatial memory, other than one case who was impaired on remote and recent conditions for several tasks. The nature of deficits in recent and remote spatial memory were further revealed on house floorplan drawings, which contained spatial distortions, room/structure transpositions, and omissions, and on neighbourhood sketch maps, which were intact in terms of overall layout but sparse in details such as landmarks. ConclusionLab-based tests of spatial memory of newly learned environments are unlikely to fully capture patterns of spared and impaired representations of real-world environments (e.g., peripheral features, configurations). Naturalistic tasks, including generative drawing tasks, indicate that contrary to Cognitive Map Theory, neither HPC nor MTL are critical for allocentric gross representations of large-scale environments. Conversely, the HPC appears critical for representing detailed spatial information of local naturalistic environments and environmental objects regardless of the age of the memory, contrary to Standard Consolidation Theory.

Abstract cognitive maps of social network structure aid adaptive inference.

Social navigation-such as anticipating where gossip may spread, or identifying which acquaintances can help land a job-relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and "field data" from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.

Spatial orientation: A relationship with inferential memory

Two branches of the scientific literature have dominated our understanding of hippocampal function. One focuses on the support this structure offers to declarative memory, while the other views the hippocampus as a part of a system dedicated to spatial navigation. These two different visions can be reconciled in relational theory, which suggests that the hippocampus processes all kinds of associations and sequences of events. According to this, processing would be similar to a route calculation based on associations of spatial information acquired during navigation and the associative relationship established between memories without spatial content. In this paper, we present a behavioral study of healthy individuals to explore the performance of inferential memory tasks and spatial orientation tasks in a virtual environment. Inferential memory and spatial orientation task performances were positively correlated. However, after controlling for a non-inferential memory task, only the correlation between allocentric spatial orientation and inferential memory remained significant. These results provide support for the similarity between the two cognitive functions, lending credence to the relational theory of the hippocampus. Additionally, our behavioral findings are in line with the cognitive map theory, which suggests a potential association between the hippocampus and allocentric spatial representations.

Domain-General and Domain-Specific Electrophysiological Markers of Cognitive Distance Coding for What, Where, and When Memory Retrieval.

The what, where, and when components of episodic memory can be differentiated based on their distinctive domain-specific underlying neural correlates. However, recent studies have proposed that a common neural mechanism of conceptual mapping may be involved in the coding of cognitive distance across all domains. In this study, we provide evidence that both domain-specific and domain-general processes occur simultaneously during memory retrieval by identifying distinctive and common neural representations for mapping what (i.e., semantic distance), where (i.e., spatial distance), and when (i.e., temporal distance) using scalp EEG from 47 healthy participants (age 21-30, 26 male and 21 female). First, we found that all three components commonly showed a positive correlation between cognitive distance and slow theta power (2.5-5 Hz) in parietal channels. Meanwhile, fast theta power (5-8.5 Hz) specifically represented spatial and temporal distance in occipital and parietal channels, respectively. Additionally, we identified a unique correlate of temporal distance coding in frontal/parietal slow theta power during the early phase of retrieval. All of the above neural markers of cognitive mapping, both domain-general and specific, were associated with individual differences in what, where, and when memory accuracy.SIGNIFICANCE STATEMENT The Cognitive Map Theory was originally founded to explain how we remember and organize the immense amount of spatial information that we face when we navigate. However, memory research has recently trended in the direction of emphasizing the generalizability of cognitive mapping mechanisms to information in any domain, represented as distances in an abstract conceptual space. In a single study, we show that both common and unique neural coding of semantic distance (i.e., what), spatial distance (i.e., where), and temporal distance (i.e., when) simultaneously support episodic memory retrieval. Our results suggest that our ability to accurately distinguish between memories is achieved through an integration of domain-specific and domain-general neurocognitive mechanisms that work in parallel.

Representation of the inferred relationships in a map-like space.

A cognitive map is an internal representation of the external world that guides flexible behavior in a complex environment. Cognitive map theory assumes that relationships between entities can be organized using Euclidean-based coordinates. Previous studies revealed that cognitive map theory can also be generalized to inferences about abstract spaces, such as social spaces. However, it is still unclear whether humans can construct a cognitive map by combining relational knowledge between discrete entities with multiple abstract dimensions in nonsocial spaces. Here we asked subjects to learn to navigate a novel object space defined by two feature dimensions, price and abstraction. The subjects first learned the rank relationships between objects in each feature dimension and then completed a transitive inferences task. We recorded brain activity using functional magnetic resonance imaging (fMRI) while they performed the transitive inference task. By analyzing the behavioral data, we found that the Euclidean distance between objects had a significant effect on response time (RT). The longer the one-dimensional rank distance and two-dimensional (2D) Euclidean distance between objects the shorter the RT. The task-fMRI data were analyzed using both univariate analysis and representational similarity analysis. We found that the hippocampus, entorhinal cortex, and medial orbitofrontal cortex were able to represent the Euclidean distance between objects in 2D space. Our findings suggest that relationship inferences between discrete objects can be made in a 2D nonsocial space and that the neural basis of this inference is related to cognitive maps.

An Optimal Distributed RES Sizing Strategy in Hybrid Low Voltage Networks Focused on EVs’ Integration

The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs’ charging by residential RES is presented.

Non-spatial similarity can bias spatial distances in a cognitive map

The cognitive map theory suggests the hippocampal-entorhinal system has a representation of space that encodes geometric properties. There is also evidence that the hippocampus plays a critical role in supporting declarative memory, and recent theories have hypothesized the mechanism for encoding space is the same as that for processing memory. If space is not represented independently, it might be influenced by non-spatial properties. This study tested whether connections between non-spatial properties can distort judgments about spatial distance. In virtual reality, subjects navigated through an environment to learn the locations of target houses, and then were tested on their ability to judge the pairwise distances between houses and reconstruct a map of the environment. The environment was constructed to have pairs of houses with the same spatial distance but either the same or different color. If memory for spatial and non-spatial properties interact, similar houses would be expected to be judged as closer. In Experiment 1, the similar pairs all had the same color, while in Experiment 2, each pair had a different color to make the pairs more distinctive. We observed that similar houses were drawn closer on reconstructed maps in both experiments, and pairwise distance judgments were smaller for similar houses in Experiment 2. Biases from color similarity are difficult to reconcile with independent representation of space. Our results support theories that space is represented with other properties, and the mechanisms for encoding space in the hippocampal-entorhinal system have a broader function.

Higher-Order Conditioning in the Spatial Domain.

Spatial learning and memory, the processes through which a wide range of living organisms encode, compute, and retrieve information from their environment to perform goal-directed navigation, has been systematically investigated since the early twentieth century to unravel behavioral and neural mechanisms of learning and memory. Early theories about learning to navigate space considered that animals learn through trial and error and develop responses to stimuli that guide them to a goal place. According to a trial-and error learning view, organisms can learn a sequence of motor actions that lead to a goal place, a strategy referred to as response learning, which contrasts with place learning where animals learn locations with respect to an allocentric framework. Place learning has been proposed to produce a mental representation of the environment and the cartesian relations between stimuli within it—which Tolman coined the cognitive map. We propose to revisit some of the best empirical evidence of spatial inference in animals, and then discuss recent attempts to account for spatial inferences within an associative framework as opposed to the traditional cognitive map framework. We will first show how higher-order conditioning can successfully account for inferential goal-directed navigation in a variety of situations and then how vectors derived from path integration can be integrated via higher-order conditioning, resulting in the generation of higher-order vectors that explain novel route taking. Finally, implications to cognitive map theories will be discussed.

Cognitive mapping: Anchoring our brain’s sense of space in a dynamic world

In complex environments, we rely on knowing our current location and pathways to others in order to flexibly navigate. But viable routes often change. New research suggests how the brain tracks where we are regardless of paths available to us.

Do hippocampal pyramidal cells respond to nonspatial stimuli?

There are currently a number of theories of rodent hippocampal function. They fall into two major groups that differ in the role they impute to space in hippocampal information processing. On one hand, the cognitive map theory sees space as crucial and central, with other types of nonspatial information embedded in a primary spatial framework. On the other hand, most other theories see the function of the hippocampal formation as broader, treating all types of information as equivalent and concentrating on the processes carried out irrespective of the specific material being represented, stored, and manipulated. One crucial difference, therefore, is the extent to which theories see hippocampal pyramidal cells as representing nonspatial information independently of a spatial framework. Studies have reported the existence of single hippocampal unit responses to nonspatial stimuli, both to simple sensory inputs as well as to more complex stimuli such as objects, conspecifics, rewards, and time, and these findings been interpreted as evidence in favor of a broader hippocampal function. Alternatively, these nonspatial responses might actually be feature-in-place signals where the spatial nature of the response has been masked by the fact that the objects or features were only presented in one location or one spatial context. In this article, we argue that when tested in multiple locations, the hippocampal response to nonspatial stimuli is almost invariably dependent on the animal's location. Looked at collectively, the data provide strong support for the cognitive map theory.

The Quest for the Hippocampal Memory Engram: From Theories to Experimental Evidence.

More than a century after Richard Semon's theoretical proposal of the memory engram, technological advancements have finally enabled experimental access to engram cells and their functional contents. In this review, we summarize theories and their experimental support regarding hippocampal memory engram formation and function. Specifically, we discuss recent advances in the engram field which help to reconcile two main theories for how the hippocampus supports memory formation: The Memory Indexing and Cognitive Map theories. We also highlight the latest evidence for engram allocation mechanisms through which memories can be linked or separately encoded. Finally, we identify unanswered questions for future investigations, through which a more comprehensive understanding of memory formation and retrieval may be achieved.

The Dynamic Extensions of Fuzzy Grey Cognitive Maps

Fuzzy Cognitive Map is recognized as an important model in soft computing. The Fuzzy Cognitive Map theory studies often treat uncertainty modeling and dynamic modeling separately. So far, there is no single Fuzzy Cognitive Map that can deal with uncertain data and dynamic environments simultaneously. This paper proposed an environment model to describe the link between changing weights and the dynamic environment. As the extensions of the classic Fuzzy Grey Cognitive Map, two dynamic models were designed and implemented in this work: Dynamic Fuzzy Grey Cognitive Map and Dynamic Fuzzy General Grey Map. In this work, we also analyze the characteristics of the two models and evaluate their performance using industrial control problems. The results showed that the proposed models could well handle uncertain data in dynamic environments.

Construction of Cognitive Maps to Improve Reading Performance by Text Signaling: Reading Text on Paper Compared to on Screen.

Reading text from a screen has been shown to be less effective compared with reading text from paper. Various signals may provide both background information and navigational cues, and may promote the construction of cognitive maps during on-screen reading, thus improving reading performance. This study randomly divided 75 college students into a paper reading group and an on-screen reading group. Both groups were tested for navigation and reading comprehension in response to three different forms of signaling (plain text, physical signaling, and verbal signaling). The results showed that when plain text was presented, the navigation and comprehension scores of the paper reading group were significantly higher than those of the on-screen reading group. However, no significant difference was found between both groups under signaling conditions. The navigation and comprehension scores of both groups were significantly higher under signaling conditions than under plain text. Moreover, the comprehension score of the on-screen reading group under physical signaling was significantly higher than that under verbal signaling. This research suggested that signals help to construct cognitive maps and effectively improve reading performance. Besides, physical signaling, such as underlining and bold formatting, is more effective for on-screen reading. The present study provides a practical and effective approach for improving on-screen reading based on cognitive map theory.

Personalizing Human-Agent Interaction Through Cognitive Models.

Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI.

Object and place information processing by CA1 hippocampal neurons of C57BL/6J mice.

Medial and lateral entorhinal cortices convey spatial/contextual and item/object information to the hippocampus, respectively. Whether the distinct inputs are integrated as one cognitive map by hippocampal neurons to represent location and the objects therein, or whether they remain as parallel outputs, to be integrated in a downstream region, remains unclear. Principal, or complex spike bursting, neurons of hippocampus exhibit location-specific firing, and it is likely that the activity of "place cells" supports spatial memory/navigation in rodents. Consistent with cognitive map theory, the activity of CA1 hippocampal neurons is also critical for nonspatial memory, such as object recognition. However, the degree to which CA1 neuronal activity represents the associations of object-context or object-in-place memory is not well understood. Here, the contributions of mouse CA1 neuronal activity to object recognition memory and the emergence of object-place conjunctive representations were tested using in vivo recordings and functional inactivation. Independent of arena configuration, CA1 place fields were stable throughout testing and object-place representations were not identified in CA1, although the number of fields per cell increased during object sessions, and few object-related firing CA1 neurons (nonplace) were recorded. The results of the inactivation studies confirmed the significant contribution of CA1 neuronal activity to object recognition memory when a delay of 20 min, but not 5 min, was imposed between encoding and retrieval. Together, our results confirm the delay-dependent contribution of the CA1 region to object memory and suggest that object information is processed in parallel with the ongoing spatial mapping function that is a hallmark of hippocampal memory.NEW & NOTEWORTHY We developed variations of the object recognition task to examine the contribution of mouse CA1 neuronal activity to object memory and the degree to which object-context conjunctive representations are formed during object training. Our results indicate that, within the CA1 region, object information is processed in a parallel but delay-dependent manner, with ongoing spatial mapping.

Why People Perceive Messages Differently: The Theory of Cognitive Mapping

Aim/Purpose: The paper introduces new concepts including cognitive mapping, cognitive message processing, and message resonance. Background: This paper draws upon philosophy, psychology, physiology, communications, and introspection to develop the theory of cognitive mapping. Methodology: Theory development Contribution: The theory offers new ways to conceptualize the informing process. Findings: Cognitive mapping has a far-reaching explanatory power on message resonance. Recommendation for Researchers: The theory of cognitive mapping offers a new conceptualization for those exploring the informing process that is ripe for exploration and theory testing. Future Research: This paper forms a building block toward the development of a fuller model of the informing process.

DIACHRONIC COGNITIVE MAP OF LINGUISTIC TERM AS A TERMINOGRAPHIC TOOL

The achievements of the cognitive-discursive paradigm of linguistics research in the field of cognitive mapping theory are analysed. In modern linguistics, the cognitive map of a word is considered as 1) a reflection of the most common contexts of the word; 2) a statement of all areas followed by the transformation of the word semantics; 3) a recommendation for a fuller lexicographic representation of word meanings. The basis for constructing a cognitive map is the processes of categorisation, and the map has the form of a hierarchical structure, it is usually visualised as a directed graph. The theory of the cognitive map is developed in cognitive terminology. Researchers consider that the creation of a cognitive map requires the separation of basic (nodal) concepts that fragmenting the subject area of specialised knowledge; the identification of essential features that are criteria for categorisation; clustering concepts with similar characteristics to specific categories-rubrics of the category of this discipline; construction of the frame scheme of separate fragments and all structure of science as a whole. It is shown that the idea of cognitive maps in modern Ukrainian lexicography reflected in the theory of words portraiture. The concept “diachronic cognitive map of a linguistic term” has been introduced into scientific usage and the following components have been described on the example of the term phonetics: 1) term – nominee of a term concept and/or its definition; 2) etymology of the term; 3) the first fixation in linguistic sources (if possible); 4) diachrony of conceptualisation of the term concept – formal and semantic; 5) diachrony of categorisation of the term concept – lexical, grammatical and modus; 6) textual representation of the terminological concept in diachrony; 7) model of intra- and intersectional system connections of the terminological concept. It is shown that the diachronic cognitive map of a linguistic term is an essential stage of preparatory work on compiling a historical dictionary of linguistic terms.

Fuzzy cognitive maps in the dependability analysis of systems

Aim. Dependability simulation of a complex system starts with its structuring, i.e. partitioning into components (blocks, units, elements), for which probabilities of failure are known. The classical dependability theory uses the concept of structural function that allows ranking elements by their importance, which is required for optimal distribution of the resources allocated to ensuring system dependability. Man-machine systems are structured using an algorithmic description of discrete processes of operation, where the presence of clear boundaries between individual operations allows collecting statistical data on the probabilities of error that is required for modeling. Algorithmization is complicated in case of man-machine systems with continuous human activity, where the absence of clear boundaries between operations prevents the correct assessment of the probability of their correct performance. For that reason, the process of operation has to be considered as a single operation, whose correct performance depends on heterogeneous and interconnected human-machine system-related, technical, software-specific, managerial and other factors. The simulated system becomes a “black box” with unknown structure (output is dependability, inputs are contributing factors), while the problem of element ranking typical to the dependability theory comes down to the problem of factor ranking. Regression analysis is one of the most popular means of multifactor dependability simulation of man-machine systems. It requires a large quantity of experimental data and is not compatible with qualitative factors that are measured by expert methods. The “if – then” fuzzy rule is a convenient tool for expert information processing. However, regression analysis and fuzzy rules have a common limitation: they require independent input variables, i.e. contributing factors. Fuzzy cognitive maps do not have this restriction. They are a new simulation tool that is not yet widely used in the dependability theory. The Aim of the paper is to raise awareness of dependability simulation with fuzzy cognitive maps.Method. It is proposed – based on the theory of fuzzy cognitive maps – to rank factors that affect system dependability. The method is based on the formalization of causal relationships between the contributing factors and the dependability in the form of a fuzzy cognitive map, i.e. directed graph, whose node correspond to the system’s dependability and contributing factors, while the weighted edges indicate the magnitude of the factors’ effect on each other and the system’s dependability. The rank of a factor is defined as an equivalent of the element’s importance index per Birnbaum, which, in the probabilistic dependability theory is calculated based on the structure function.Results. Models and algorithms are proposed for calculation of the importance indexes of single factors and respective effects that affect system dependability represented with a fuzzy cognitive map. The method is exemplified by the dependability and safety of an automobile in the “driver-automobile-road” system subject to the driver’s qualification, traffic situation, unit costs of operation, operating conditions, maintenance scheduling, quality of maintenance and repair, quality of automobile design, quality of operational materials and spare parts, as well as storage conditions.Conclusions. The advantages of the method include: a) use of available expert information with no collection and processing statistical data; b) capability to take into account any quantitative and qualitative factors associated with people, technology, software, quality of service, operating conditions, etc.; c) ease of expansion of the number of considered factors through the introduction of additional nodes and edges of the cognitive map graph. The method can be applied to complex systems with fuzzy structures, whose dependability strongly depends on interrelated factors that are measured by means of expert methods.

Evaluating the Relation Between CHC Cognitive Factors and Selected Components of Executive Functioning

Executive functioning remains an elusive paradigm in regard to their underlying constructs. The Cattell-Horn-Carroll (CHC) theory of cognitive functions is the predominant theory of the measurement of human intelligence in psychology in regard to test construction and interpretation. The purpose of this study was to evaluate the relations between components of the Tower Test and Color-Word Interference Test from the Delis-Kaplan Executive Function System (D-KEFS) and CHC theory, as measured by the Woodcock-Johnson III Tests of Cognitive Abilities (WJ-III-COG). Participants were 64 undergraduate students (women, n = 38; men, n = 26), with a mean age of 19.88 years. Results of a Structured Equation Model indicated a correlation between the two factors modeled for Intelligence and Executive functioning was estimated to be 0.575 (0.331), and was statistically significant (p < .001), with a 95% credible interval of (0.551, 0.599). Thus, approximately 33% of the variance for measures of Intelligence was accounted for by measures of Executive Functioning; the biggest CHC contributor was Numbers Reversed which argues for the importance of attention and working memory being an important component of executive functioning. The results suggest that despite a relation between some components of executive function and cognitive ability, much variance between the D-KEFS and WJ-III-COG remains unaccounted for. These findings have implications for evaluation and intervention planning within vocational and educational settings.

Reputational risk management in conditions of credibility gap in the banking system

PurposeThis paper aims to provide appropriate and effective methods for diagnosing and managing the reputational risk of a bank.Design/methodology/approachUsing the data-mining system CONFOR, the authors have defined and arranged in the objective way the factors influencing the reputational risk level of a bank, with specific factor groups assigned. The mathematical tools of the theory of graphs and cognitive mapping are used for building the cognitive map of reputational risk assessment of a bank. To enable choosing which risk management strategies to use, the methods of impulse modeling and scenario analysis are applied for the purpose of prognostication.FindingsThe authors proposed an integrated approach to the objective determination of the reputational risk level and an easy-to-use choice of risk management scenarios considering all components of management decision making and the main managerial functions.Practical implicationsThe range of tools proposed can be used as a simple measure of the bank's reputational risk level, as a component of risk management model, and as a strategic risk management tool. Implementing this approach at the level of the central bank will facilitate realization of its strategic program for restoring confidence in the banking system as a whole.Originality/valueA relatively greater objectivity in the reputational risk assessment of the bank was achieved, with each stage formalized and confirmed by an economic and mathematical basis, risk parameters quantified. The authors have developed a comprehensive approach to the reputational risk level assessment (the integral assessment included) as well as the management algorithm.