Evidence for grid-cell-like activity in the time domain.

The relation between the processing of space and time in the brain has been an enduring cross-disciplinary question. Grid cells have been recognized as a hallmark of the mammalian navigation system, with recent studies attesting to their involvement in the organization of conceptual knowledge in humans. To determine whether grid-cell-like representations support temporal processing, we asked subjects to mentally simulate changes in age and time-of-day, each constituting "trajectory" in an age-day space, while undergoing fMRI. We found that grid-cell-like representations supported trajecting across this age-day space. Furthermore, brain regions concurrently coding past-to-future orientation positively modulated the magnitude of grid-cell-like representation in the left entorhinal cortex. Finally, our findings suggest that temporal processing may be supported by spatially modulated systems, and that innate regularities of abstract domains may interface and alter grid-cell-like representations, similarly to spatial geometry.

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications.

Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue.

The effect of observing trained conspecifics on the performance and motivation of goldfish, Carassius auratus, in a spatial task.

Spatial and social cognition are two aspects of fish behaviour that have been subject to an increasing amount of research in recent years, but few have investigated potential behaviour overlaps. Testing the ability for an individual to socially learn a spatial task would bridge this gap in understanding. We provided naïve goldfish, Carassius auratus, the opportunity to observe a trained conspecific navigate a T-shaped maze, and then recorded how many trials it took for them to learn the maze, time taken per trial, motivation, and acceptance of the food reward. We also recorded how many trials it took a control group to learn the maze without the opportunity to observe a demonstrator. The observer group took significantly longer to learn the maze than the control group. Although the observer group were significantly less motivated (trials without a choice made), they were significantly more likely to accept the food reward. The social learning of reward acceptance was taking place, but the process of the demonstration disrupted the training of the spatial task, with possible explanations as the passenger effect and trade-off mechanism being discussed. Future studies are needed to determine whether goldfish can acquire spatial information socially; however, this study contributes to the feasibility of studying social learning of environmentally information in goldfish.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake.

BACKGROUND: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. OBJECTIVE: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. METHODS: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. RESULTS: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. CONCLUSIONS: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.

Explainability as the key ingredient for AI adoption in Industry 5.0 settings.

Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the "transparency paradox" of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.

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.

[Influence of leadership style on knowledge management and hospital efficiency]. / Influencia del liderazgo en gestión del conocimiento y eficiencia hospitalaria.

OBJECTIVE: To analyse the effect of leadership style on knowledge management in hospitals and hospital efficiency based on the opinion of experts in hospital management, applying fuzzy cognitive maps (FCM). METHOD: FCM are relational models that can be used to graphically represent expert opinion and knowledge to infer cause-effect relationships between different concepts. The use of FCM as a simulation tool allows the evaluation of possible scenarios based on different leadership styles in hospitals. RESULTS: In the resulting augmented matrix, standardized effects range from 0.02 to 0.84, with the highest value representing the strongest relationship between knowledge exploitation and hospital efficiency. From the viewpoint of experts, knowledge creation within the hospital also influences hospital efficiency. Regarding variables reflecting leadership characteristics, positive effects have been identified, though with varying intensities, between authority, benevolence, and charisma, both in terms of knowledge creation and exploitation, as well as hospital efficiency. The transformational leadership style is associated with coefficients having higher values for knowledge management and hospital efficiency. CONCLUSIONS: Experts suggest that hospitals with authoritarian leadership styles would exhibit lower levels of knowledge creation and management, as well as lower hospital efficiency. On the other hand, they associate hospitals managed with a paternalistic leadership style with better values in both knowledge creation and exploitation, as well as hospital efficiency, compared to the authoritarian leadership style. Finally, they attribute the highest levels in aspects related to knowledge management and hospital efficiency to the transformational leadership style.

Hippocampal and medial prefrontal ensemble spiking represents episodes and rules in similar task spaces.

Episodic memory requires the hippocampus and prefrontal cortex to guide decisions by representing events in spatial, temporal, and personal contexts. Both brain regions have been described by cognitive theories that represent events in context as locations in maps or memory spaces. We query whether ensemble spiking in these regions described spatial structures as rats performed memory tasks. From each ensemble, we construct a state-space with each point defined by the coordinated spiking of single and pairs of units in 125-ms bins and investigate how state-space locations discriminate task features. Trajectories through state-spaces correspond with behavioral episodes framed by spatial, temporal, and internal contexts. Both hippocampal and prefrontal ensembles distinguish maze locations, task intervals, and goals by distances between state-space locations, consistent with cognitive mapping and relational memory space theories of episodic memory. Prefrontal modulation of hippocampal activity may guide choices by directing memory representations toward appropriate state-space goal locations.

Generative replay underlies compositional inference in the hippocampal-prefrontal circuit.

Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing.

Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation.

The flour milling industry-a vital component of global food production-is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters.

Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex.

Humans can navigate flexibly to meet their goals. Here, we asked how the neural representation of allocentric space is distorted by goal-directed behavior. Participants navigated an agent to two successive goal locations in a grid world environment comprising four interlinked rooms, with a contextual cue indicating the conditional dependence of one goal location on another. Examining the neural geometry by which room and context were encoded in fMRI signals, we found that map-like representations of the environment emerged in both hippocampus and neocortex. Cognitive maps in hippocampus and orbitofrontal cortices were compressed so that locations cued as goals were coded together in neural state space, and these distortions predicted successful learning. This effect was captured by a computational model in which current and prospective locations are jointly encoded in a place code, providing a theory of how goals warp the neural representation of space in macroscopic neural signals.

Interactive cognitive maps support flexible behavior under threat.

In social environments, survival can depend upon inferring and adapting to other agents' goal-directed behavior. However, it remains unclear how humans achieve this, despite the fact that many decisions must account for complex, dynamic agents acting according to their own goals. Here, we use a predator-prey task (total n = 510) to demonstrate that humans exploit an interactive cognitive map of the social environment to infer other agents' preferences and simulate their future behavior, providing for flexible, generalizable responses. A model-based inverse reinforcement learning model explained participants' inferences about threatening agents' preferences, with participants using this inferred knowledge to enact generalizable, model-based behavioral responses. Using tree-search planning models, we then found that behavior was best explained by a planning algorithm that incorporated simulations of the threat's goal-directed behavior. Our results indicate that humans use a cognitive map to determine other agents' preferences, facilitating generalized predictions of their behavior and effective responses.

Os ciclos de engajamento no trabalho de servidores públicos federais / Los ciclos de compromiso laboral de los servidores públicos federales / The work engagement cycles of federal civil servants

Resumo Servidores públicos têm diferentes níveis de engajamento no trabalho ao longo da vida laboral. Essas variações, denominadas "ciclos de engajamento no trabalho", ocorrem graças aos recursos disponíveis e às demandas do ambiente de trabalho. Diante disso, o presente artigo busca descrever os ciclos de engajamento no trabalho de servidores públicos federais com base em suas histórias de vida profissional, evidenciando demandas e recursos relevantes do ambiente ocupacional na trajetória dessas pessoas. Usando o modelo job demands-resources (JD-R) e a metodologia de mapas cognitivos, foi possível identificar um ciclo positivo do engajamento no trabalho, um ciclo reforçador - relacionado com oportunidades e valorização - e dois ciclos de desequilíbrio, um ligado à produtividade disfuncional e, outro, à descontinuidade administrativa. A análise dos ciclos de engajamento permitiu identificar recursos do ambiente de trabalho que interferem de diferentes maneiras no engajamento de servidores públicos. Por fim, foi utilizado o conceito de "ciclo de enfrentamento" como subsídio de políticas para servidores desengajados.

Resumen Los servidores públicos tienen diferentes niveles de compromiso en el trabajo a lo largo de su vida laboral. Estas variaciones, llamadas ciclos de compromiso laboral, ocurren debido a los recursos disponibles y las demandas del entorno laboral. Frente a eso, el presente trabajo buscó describir los ciclos de compromiso en el trabajo de los servidores públicos federales a partir de sus historias de vida profesional, destacando demandas y recursos del ambiente laboral relevantes en la trayectoria laboral de esas personas. Utilizando el modelo job demands-resources (JD-R) y la metodología de mapas cognitivos, fue posible identificar un ciclo positivo de compromiso en el trabajo, un ciclo de refuerzo ‒ relacionado con oportunidades y apreciación ‒ y dos ciclos de desequilibrio, uno relacionado con la productividad disfuncional y el otro con la discontinuidad administrativa. El análisis de los ciclos de compromiso permitió identificar los recursos del ambiente de trabajo que interfieren de diferentes formas en el compromiso de los servidores públicos. Finalmente, se utilizó el concepto de "ciclo de afrontamiento" como base de las políticas para los servidores desinteresados.

Abstract Civil servants have different levels of work engagement throughout their working lives. These variations are the work engagement cycles, which occur based on available resources and work environment demands. This study describes the work engagement cycles of federal civil servants based on their professional life histories, highlighting the demands and resources of the work environment in their professional trajectory. Using the job demands-resources (JD-R) model and the cognitive maps methodology, it was possible to identify a positive cycle of work engagement, a reinforcing cycle (related to opportunities and appreciation), and two disequilibrium cycles, one related to dysfunctional productivity and the other to administrative discontinuity. The analysis of the engagement cycles allowed the identification of work environment resources that interfere in the engagement of public servants in different ways. Finally, the concept of "coping cycle" was used as a subsidy of policies for disengaged servants.

Combining SWOT analysis and neutrosophic cognitive maps for multi-criteria decision making: a case study of organic agriculture in India.

The conventional agricultural system heavily depends on chemicals and inorganic fertilizers, which cause environmental issues. Organic agriculture impacts 6 of the 17 Sustainable Developmental Goals (SDGs) of the United Nations. Strategies to develop organic agriculture have used SWOT and MCDM techniques for analysis. However, the examination of the influence of one strategy over the other strategies has yet to be investigated. This paper proposes a model that combines the existing SWOT analysis with neutrosophic cognitive maps (NCM) models to analyze interconnections among the various strategies obtained from SWOT. This research deploys the proposed SWOT-NCM model to analyze the case study of developing organic farming in Tamil Nadu, India. It offers insights into the strategy's influence over other strategies so that the best is given maximum importance while implementing organic farming. The framework captures the interconnections and ranks the strategies by order of influence, providing fresh insights by taking the farmers' perspective while working with the strategies from the SWOT analysis to model an NCM. A comparative analysis of this SWOT-NCM model with other MCDM models that use SWOT to analyze the agriculture problem, and a sensitivity analysis of the proposed model, is performed. According to our study, the best possible strategy to encourage organic farming is minimum support price (MSP) and centralized procurement. This proposed model can analyze other MCDM problems that use SWOT analysis.

Supplier selection in the industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology.

Organizations will be increasingly concerned about maintaining their positions in today's changing world, the high-tech era, and the emergence of innovative technologies because of the industrial revolutions. Everyone has come to believe that to survive and continue their constructive roles, they must achieve competitive advantages by working based on the trends. It is undeniable that the introduction of Industry 4.0 has had a significant impact on enterprises, organizations, and, of course, supply chains. In the meantime, selecting a supplier is one of the main strategic decisions of the organization because choosing the right supplier leads to increasing profitability, improving market competition, better accountability, enhancing product quality, and reducing costs. While the issue of supplier evaluation has been one of the interesting topics for researchers in recent decades, its development in the fourth supply chain generation needs further consideration. In this regard, current technologies in the fourth-generation industrial revolution, methods, and criteria used in previous studies based on industry 4.0 and before that are reviewed separately. By reviewing previous articles and experts' opinions, thirteen sub-criteria considering industry 4.0 have been identified for selecting suppliers in three categories, economic, environmental, and social. The weight of each criterion has been determined using a set of fuzzy cognitive maps (FCMs) and considering the centrality of criteria in the concept of communication networks. To prioritize the suppliers, the hesitant fuzzy linguistic term sets (HFLTS) VIKOR method has been used in hesitant fuzzy linguistic terms. Finally, a case study is introduced to illustrate the effectiveness and usefulness of our integrated methodology and prioritize its four suppliers.

Exploiting the haptic and audio channels to improve orientation and mobility apps for the visually impaired.

Orientation and mobility apps for visually impaired people are well known to be effective in improving the quality of life for this target group. A mobile application that guides a visually impaired person step-by-step through a physical space is a valuable aid, but it does not provide an overview of a complex environment "at a glance," as a traditional hard-copy tactile map does. The aim of this study is to investigate whether a smartphone GPS map, enriched with haptic and audio hints, can facilitate cognitive mapping for visually impaired users. Encouraged by a preliminary study conducted in co-operation with two visually impaired volunteers, we designed and developed an Android prototype for exploration of an urban area. Our goal was to provide an affordable, portable and versatile solution to help users increase awareness of an environment through the positions of its landmarks and points of interest. Vibro-tactile and audio hints were linked to the coordinates on the map via the GeoJSON data format and were issued exploiting the text-to-speech and vibration features of the mobile device, as they were displayed through the operating system's APIs. Test sessions and interviews with visually impaired users produced encouraging results. Results, to be verified by more extensive testing, overall confirm the validity of our approach and are in line with results found in the literature.

Influencia del liderazgo en gestión del conocimiento y eficiencia hospitalaria / Influence of leadership style on knowledge management and hospital efficiency

Objetivo: Analizar el efecto del estilo de liderazgo sobre la gestión del conocimiento y la eficiencia hospitalaria aplicando para ello mapas cognitivos difusos (MCD). Método: Los MCD son modelos relacionales que se pueden utilizar para representar gráficamente la opinión y el conocimiento de un grupo de personas expertas e inferir las relaciones causa-efecto que hay entre distintos conceptos. La utilización de MCD como herramienta de simulación permite evaluar posibles escenarios basados en distintos estilos de liderazgo en los hospitales. Resultados: En la matriz aumentada resultante, los efectos estandarizados varían de 0,02 a 0,84, representando el valor más alto la relación entre explotación del conocimiento y eficiencia hospitalaria. Para las personas expertas, la creación de conocimiento en el hospital también influye en la eficiencia hospitalaria. Se han identificado efectos positivos, aunque con distinta intensidad, de la autoridad, la benevolencia y el carisma del líder en la creación y la explotación del conocimiento y en la eficiencia hospitalaria. El liderazgo transformacional se asocia a los valores más altos de gestión de conocimiento y eficiencia hospitalaria. Conclusiones: El estilo de liderazgo autoritario parece ofrecer niveles más bajos de creación y gestión de conocimiento, así como una menor eficiencia hospitalaria. Se asocian al estilo de liderazgo paternalista mejores valores tanto en la creación y la explotación de conocimiento como en la eficiencia hospitalaria, en comparación con el estilo de liderazgo autoritario. Por último, se atribuyen al liderazgo transformacional las mayores cotas en los aspectos relacionados con gestión del conocimiento y eficiencia hospitalaria.(AU)

Objective: To analyse the effect of leadership style on knowledge management in hospitals and hospital efficiency based on the opinion of experts in hospital management, applying fuzzy cognitive maps (FCM). Method: FCM are relational models that can be used to graphically represent expert opinion and knowledge to infer cause–effect relationships between different concepts. The use of FCM as a simulation tool allows the evaluation of possible scenarios based on different leadership styles in hospitals. Results: In the resulting augmented matrix, standardized effects range from 0.02 to 0.84, with the highest value representing the strongest relationship between knowledge exploitation and hospital efficiency. From the viewpoint of experts, knowledge creation within the hospital also influences hospital efficiency. Regarding variables reflecting leadership characteristics, positive effects have been identified, though with varying intensities, between authority, benevolence, and charisma, both in terms of knowledge creation and exploitation, as well as hospital efficiency. The transformational leadership style is associated with coefficients having higher values for knowledge management and hospital efficiency. Conclusions: Experts suggest that hospitals with authoritarian leadership styles would exhibit lower levels of knowledge creation and management, as well as lower hospital efficiency. On the other hand, they associate hospitals managed with a paternalistic leadership style with better values in both knowledge creation and exploitation, as well as hospital efficiency, compared to the authoritarian leadership style. Finally, they attribute the highest levels in aspects related to knowledge management and hospital efficiency to the transformational leadership style.(AU)

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults.

BACKGROUND: Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions. METHODS: To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed. RESULTS: The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society. CONCLUSIONS: From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.

Spatial Representations Without Spatial Computations.

Cognitive maps are assumed to be fundamentally spatial and grounded only in perceptual processes, as supported by the discovery of functionally dedicated cell types in the human brain, which tile the environment in a maplike fashion. Challenging this view, we demonstrate that spatial representations-such as large-scale geographical maps-can be as well retrieved with high confidence from natural language through cognitively plausible artificial-intelligence models on the basis of nonspatial associative-learning mechanisms. More critically, we show that linguistic information accounts for the specific distortions observed in tasks when college-age adults have to judge the geographical positions of cities, even when these positions are estimated on real maps. These findings indicate that language experience can encode and reproduce cognitive maps without the need for a dedicated spatial-representation system, thus suggesting that the formation of these maps is the result of a strict interplay between spatial- and nonspatial-learning principles.