Showing papers on "Cognitive map published in 2008"

Haptic-feedback support for cognitive mapping of unknown spaces by people who are blind

Abstract: Mental mapping of spaces is essential for the development of efficient orientation and mobility skills. Most of the information required for this mental mapping is gathered through the visual channel. People who are blind lack this information, and in consequence, they are required to use compensatory sensorial channels and alternative exploration methods. In this study, people who are blind use a virtual environment (VE) that provides haptic and audio feedback to explore an unknown space. The cognitive mapping of the space based on the VE and the subject's ability to apply this map to accomplish tasks in the real space are examined. Results show clearly that a robust and comprehensive map is constructed, contributing to successful performance in real space tasks.

The Network of Reference Frames Theory: A Synthesis of Graphs and Cognitive Maps

Abstract: The network of reference frames theory explains the orientation behavior of human and non-human animals in directly experienced environmental spaces, such as buildings or towns. This includes self-localization, route and survey navigation. It is a synthesis of graph representations and cognitive maps, and solves the problems associated with explaining orientation behavior based either on graphs, maps or both of them in parallel. Additionally, the theory points out the unique role of vista spaces and asymmetries in spatial memory. New predictions are derived from the theory, one of which has been tested recently.

Neural basis of the cognitive map: path integration does not require hippocampus or entorhinal cortex.

Abstract: The hippocampus and entorhinal cortex have been linked to both memory functions and to spatial cognition, but it has been unclear how these ideas relate to each other. An important part of spatial cognition is the ability to keep track of a reference location using self-motion cues (sometimes referred to as path integration), and it has been suggested that the hippocampus or entorhinal cortex is essential for this ability. Patients with hippocampal lesions or larger lesions that also included entorhinal cortex were led on paths while blindfolded (up to 15 m in length) and were asked to actively maintain the path in mind. Patients pointed to and estimated their distance from the start location as accurately as controls. A rotation condition confirmed that performance was based on self-motion cues. When demands on long-term memory were increased, patients were impaired. Thus, in humans, the hippocampus and entorhinal cortex are not essential for path integration.

Partially segregated neural networks for spatial and contextual memory in virtual navigation.

Abstract: Finding our way in a previously learned, ecologically valid environment concurrently involves spatial and contextual cognitive operations. The former process accesses a cognitive map representing the spatial interactions between all paths in the environment. The latter accesses stored associations between landmark objects and their milieu. Here, we aimed at dissociating their neural basis in the context of memory-based virtual navigation. To do so, subjects freely explored a virtual town for 1 h, then were scanned using fMRI while retrieving their way between two locations, under four navigation conditions designed to probe separately or jointly the spatial and contextual memory components. Besides prominent commonalities found in a large hippocampo-neocortical network classically involved in topographical navigation, results yield evidence for a partial dissociation between the brain areas supporting spatial and contextual components of memory-based navigation. Performance-related analyses indicate that hippocampal activity mostly supports the spatial component, whereas parahippocampal activity primarily supports the contextual component. Additionally, the recruitment of contextual memory during navigation was associated with higher frontal, posterior parietal and lateral temporal activity. These results provide evidence for a partial segregation of the neural substrates of two crucial memory components in human navigation, whose combined involvement eventually leads to efficient navigation behavior within a learned environment.

Biologically-inspired robot spatial cognition based on rat neurophysiological studies

Abstract: This paper presents a robot architecture with spatial cognition and navigation capabilities that captures some properties of the rat brain structures involved in learning and memory. This architecture relies on the integration of kinesthetic and visual information derived from artificial landmarks, as well as on Hebbian learning, to build a holistic topological-metric spatial representation during exploration, and employs reinforcement learning by means of an Actor-Critic architecture to enable learning and unlearning of goal locations. From a robotics perspective, this work can be placed in the gap between mapping and map exploitation currently existent in the SLAM literature. The exploitation of the cognitive map allows the robot to recognize places already visited and to find a target from any given departure location, thus enabling goal-directed navigation. From a biological perspective, this study aims at initiating a contribution to experimental neuroscience by providing the system as a tool to test with robots hypotheses concerned with the underlying mechanisms of rats' spatial cognition. Results from different experiments with a mobile AIBO robot inspired on classical spatial tasks with rats are described, and a comparative analysis is provided in reference to the reversal task devised by O'Keefe in 1983.

Modeling and measuring individuals' mental representations of complex spatio-temporal decision problems

Abstract: Based on mental model theory, we expect individuals to construct a mental representation of the system they interact with which tends to be a strong reduction of reality and is tailored to the specific situation and task at hand. Such reductions may be particularly significant in complex decision situations involved in local spatial choice behavior. In this article, we develop a method to model and measure mental representations of decision problems involving individual spatio-temporal choice behavior in different situations. The so-called CNET method consists of an interview protocol to elicit the structures at the individual level as a causal network. We test the proposed method in a case study involving 180 respondents and an experimental shopping-trip planning task. The results indicate that the method is an adequate way of eliciting mental representations. We show how the networks revealed can be used to model and simulate reasoning and decision-making processes.

Autonomous vision-based navigation: Goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning

Abstract: This article presents a bio-inspired neural network providing planning capabilities in autonomous navigation applications. The proposed architecture (hippocampus model) learns, recognizes and predicts transitions between places for any system able to provide a localization gradient from the current position to each learned place. The recurrent synapses of a cognitive map (prefrontal cortices model) encoded the spatio-temporal connectivity of the performed transitions. Particular transitions of interest (goal transitions) are associated to the satisfaction of drives. While planning, the diffusion of an activity from the goal transitions in the cognitive map allows to compute a proximity gradient to the goal from each learned transition. The shortest plan of transition to reach the goal is computed by merging the cognitive map information and the prediction of the possible transitions (nucleus accumbens). In parallel, a sensory-motor learning between the performed transitions and the corresponding movements occurs and enables to physically execute the proposed plan (cerebellum). Refinements (active forgetting capabilities) are proposed for the cognitive map building. The whole system is experimented on a real robot which autonomously learns a stable representation of its environment during a long random walk and proves to be able to return to the goal from any position of the environment.

Social influences on spatial memory.

Abstract: Three experiments were performed to examine the joint influences of spatial and social categories on memory for maps. Participants learned a map and descriptive information about small town businesses and, afterward, completed distance estimation and person-location matching tasks. Experiments 1 and 2 demonstrated that social (i.e., racial) and spatial information influenced memory, but not equivalently: Social information affected distance and matching task performance, whereas spatial information affected only distance estimates. This pattern was obtained for racially segregated and racially integrated neighborhoods and when the salience of the spatial categories was heightened. The social information influence did not generalize to political affiliation categories (Experiment 3). These results demonstrate that spatial and nonspatial information may interact to structure mental maps but that the salience of the social category is critically important. Furthermore, these findings suggest the applicability of a model of category salience (Blanz, 1999) for interactive products of spatial experiences--in this case, map learning. Norms for this article may be downloaded from www.psychonomic.org/archive.

Review: Causal knowledge and reasoning by cognitive maps: Pursuing a holistic approach

Abstract: Due to the lack of an integral study about cognitive maps (CM) that focus on the causal phenomenon, this paper introduces the underlying concepts towards a holistic conceptual model, enhanced by a profile of several versions. We illustrate the use of CM through their application into the Web-based Education Systems (WBES). From the causal perspective, CM depict and simulate the systems dynamics based upon qualitative knowledge about a specific domain. A CM is a visual digraph that identifies the concepts of a given subject of analysis. CM show causal-effect relationships among the concepts and outline complex structures. This tool aims to predict the evolution of a model through causal inference. This kind of inference estimates the degree of significance of change of the concepts in the context of the whole system. The behavior of a CM is given away during iterations that update the variation of the concept state values until reach a stable point in a search space, a pattern of states or a chaotic region. The purpose of this research is to share its findings, depict the work done and promote the use of CM in a broad spectrum of domains.

Learning-oriented vehicle navigation systems: a preliminary investigation in a driving simulator

Abstract: Vehicle navigation systems aim to reduce the mental workload for drivers by automating elements of the driving task. Concern has been raised, however, that their long-term use may cause unforeseen problems, including suppressing cognitive map development.A driving simulator study was conducted to discover if this effect could be ameliorated by the use of a novel, learning-oriented, navigation system. The user-interface of this system provided a range of additional features including landmarks, compass bearings and previously driven routes within the visual and auditory guidance instructions.It was found that the users of the learning-oriented system displayed better memory for driven routes, when compared with those using a basic guidance system. It is also suggested that they had developed a better cognitive map of the area. Glance analysis demonstrated that the learning-oriented system was no more visually demanding than the basic system.

Interpreting urban space through cognitive map sketching and sequence analysis

Abstract: Traditionally, analysis of sketch maps of urban areas has focused on the interpretation of hand-drawn renditions of features that are most familiar to individuals. Few researchers have investigated the sequence that sketchers use to identify features on the urban landscape and how these features are linked together to form a coherent ‘picture’ of an area. This article builds upon previous research by exploring the sequential pattern of sketch map creation. Two research questions are proposed, namely, can a repetitive sequential order in element inclusion be identified for different individuals sketching the same urban environment? If so what features are mapped in which order to create the sketchers' image of the city? Findings suggest that three distinct groups of cognitive maps exist, namely, sequential, spatial and hybrid, and that the map elements of each group are organised in a distinctive manner with paths and landmarks as principal elements. It is suggested that insights into this process provide more substance to understanding how individuals interpret and structure urban space and use this information to navigate both known and new environments.

An Elastic Map System with Cognitive Map-based Operations

Abstract: An elastic map system is presented that enables users to stretch a digital map as if it were an elastic sheet or a tangible device. The system enhances focus+context maps, which displays both local detailed data and global context data in the same view. The system enables users to treat elements of cognitive maps (or mental images of maps), such as paths, landmarks, districts, and others, which were classified by Lynch, as elements of a city’s image. Users are able to collect map objects into a context area and enlarge them in a focus area based on previous geographical knowledge. The system provides uniform scaling of focus and context areas by introducing a glue area between the two areas. This scaling is controlled by using a displacement function and ensures angles, proportionality in distances, and parallelism between lines in focus and context areas are preserved. Furthermore, the system enables users to treat any shaped focus area with an arrangement function to arrange the displacement function so as to be suitable for a focus area of any shape, e.g. a user-specified district or both sides of a user-specified road.

Thinking Maps®: A Visual Language for Learning

Abstract: There have been a range of different types of visual tools used in schools over the past 50 years such as “graphic organizers,” mind mapping, and concept mapping. These tools are grounded in the mapping metaphor, reflecting our capacities to network information and create cognitive maps of content knowledge and concepts. This writing investigates a language of eight cognitive maps called Thinking Maps® and Thinking Maps® Software, used from early grades through college courses to foster cognitive development and content learning across disciplines by all students across entire schools.

1.12 – Learning and Representation

Abstract: Behavioral evidence implies that even insect brains compute representations of behaviorally relevant aspects of the world. For example, they compute the animal’s position in the word by integrating its velocity with respect to time. Other examples are the learning of the solar ephemeris, the construction of a cognitive map, and episodic memory in food caching. Representations require a symbolic read-write memory that carries information extracted from experience forward in time in a computationally accessible form. The close analogy between the architecture required to access memory in a computer and the architecture of the molecular machinery that accesses genetic information suggests the sort of memory structure to be looked for in the nervous system.

Learning with Virtual Verbal Displays: Effects of Interface Fidelity on Cognitive Map Development

Abstract: We investigate verbal learning and cognitive map development of simulated layouts using a non-visual interface called a virtual verbal display (VVD). Previous studies have questioned the efficacy of VVDs in supporting cognitive mapping (Giudice, Bakdash, Legge, & Roy, in revision). Two factors of interface fidelity are investigated which could account for this deficit, spatial language vs. spatialized audio and physical vs. imagined rotation. During training, participants used the VVD (Experiments 1 and 2) or a visual display (Experiment 3) to explore unfamiliar computer-based layouts and seek-out target locations. At test, participants performed a wayfinding task between targets in the corresponding real environment. Results demonstrated that only spatialized audio in the VVD improved wayfinding behavior, yielding almost identical performance as was found in the visual condition. These findings suggest that learning with both modalities led to comparable cognitive maps and demonstrate the importance of incorporating spatial cues in verbal displays.

Spatial cognition VI : learning, reasoning, and talking about space ; International Conference Spatial Cognition 2008, Freiburg, Germany, September 15-19, 2008 : proceedings

Abstract: Invited Talks.- Virtual Reality as a Valuable Research Tool for Investigating Different Aspects of Spatial Cognition.- On the "Whats" and "Hows" of "Where": The Role of Salience in Spatial Descriptions.- Learning about Space.- Spatial Orientation.- Does Body Orientation Matter When Reasoning about Depicted or Described Scenes?.- Spatial Memory and Spatial Orientation.- Spatial Navigation.- Map-Based Spatial Navigation: A Cortical Column Model for Action Planning.- Efficient Wayfinding in Hierarchically Regionalized Spatial Environments.- Analyzing Interactions between Navigation Strategies Using a Computational Model of Action Selection.- A Minimalistic Model of Visually Guided Obstacle Avoidance and Path Selection Behavior.- Spatial Learning.- Route Learning Strategies in a Virtual Cluttered Environment.- Learning with Virtual Verbal Displays: Effects of Interface Fidelity on Cognitive Map Development.- Cognitive Surveying: A Framework for Mobile Data Collection, Analysis, and Visualization of Spatial Knowledge and Navigation Practices.- Maps and Modalities.- What Do Focus Maps Focus On?.- Locating Oneself on a Map in Relation to Person Qualities and Map Characteristics.- Conflicting Cues from Vision and Touch Can Impair Spatial Task Performance: Speculations on the Role of Spatial Ability in Reconciling Frames of Reference.- Spatial Communication.- Epistemic Actions in Science Education.- An Influence Model for Reference Object Selection in Spatially Locative Phrases.- Spatial Language.- Tiered Models of Spatial Language Interpretation.- Perspective Use and Perspective Shift in Spatial Dialogue.- Natural Language Meets Spatial Calculi.- Automatic Classification of Containment and Support Spatial Relations in English and Dutch.- Similarity and Abstraction.- Integral vs. Separable Attributes in Spatial Similarity Assessments.- Spatial Abstraction: Aspectualization, Coarsening, and Conceptual Classification.- Concepts and Reference Frames.- Representing Concepts in Time.- The Network of Reference Frames Theory: A Synthesis of Graphs and Cognitive Maps.- Spatially Constrained Grammars for Mobile Intention Recognition.- Modeling Cross-Cultural Performance on the Visual Oddity Task.- Spatial Modeling and Spatial Reasoning.- Modelling Scenes Using the Activity within Them.- Pareto-Optimality of Cognitively Preferred Polygonal Hulls for Dot Patterns.- Qualitative Reasoning about Convex Relations.

Creating and utilizing symbolic representations of spatial knowledge using mobile robots

Abstract: A map is a description of an environment allowing an agent—a human, or in our case a mobile robot—to plan and perform effective actions. From a single location, an agent's sensors can not observe the whole structure of a complex, large environment. For this reason, the agent must build a map from observations gathered over time and space. We distinguish between large-scale space, with spatial structure larger than the agent's sensory horizon, and small-scale space, with structure within the sensory horizon. We propose a factored approach to mobile robot map-building that handles qualitatively different types of uncertainty by combining the strengths of topological and metrical approaches. Our framework is based on a computational model of the human cognitive map; thus it allows robust navigation and communication within several different spatial ontologies. Our approach factors the mapping problem into natural sub-goals: building a metrical representation for local small-scale spaces; finding a topological map that represents the qualitative structure of large-scale space; and (when necessary) constructing a metrical representation for large-scale space using the skeleton provided by the topological map. The core contributions of this thesis are a formal description of the Hybrid Spatial Semantic Hierarchy (HSSH), a framework for both small-scale and large-scale representations of space, and an implementation of the HSSH that allows a robot to ground the large-scale concepts of place and path in a metrical model of the local surround. Given metrical models of the robot's local surround, we argue that places at decision points in the world can be grounded by the use of a primitive called a gateway. Gateways separate different regions in space and have a natural description at intersections and in doorways. We provide an algorithmic definition of gateways, a theory of how they contribute to the description of paths and places, and practical uses of gateways in spatial mapping and learning.

CAKES-NEGO: Causal knowledge-based expert system for B2B negotiation

Abstract: As the advent of the Internet, B2B negotiation process on the Internet has been given attention from both researchers and practitioners. Therefore, B2B ecommerce decision making will be a challenge for organizations in the foreseeable future. Some literature shows that important issues to reduce uncertainty in the development of long-term relationships among B2B commerce partners. In this sense, this paper proposes a new negotiation support system to incorporate causal relationships of negotiation terms in the process of B2B negotiation, on the basis of a cognitive map. The proposed a CAKES-NEGO (CAusal Knowledge-driven Expert System) suggests that causal relationships of negotiation terms could be explicitly represented by using the cognitive map as knowledge representation vehicle as well as inference engine. Cognitive maps can illustrate causal relationships among the factors describing a given object and/or problem, and it can also describe experts' tacit knowledge about a certain object. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCM, a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. The objectives of this paper are to (1) suggest a fuzzy cognitive mapping based expert system that support decision process of decision makers and (2) apply it to the illustrative examples, which are B2B negotiation problems, to show the validity of our proposed system. In addition, statistical tests proved that the proposed negotiation mechanism could improve decision performance significantly in B2B negotiations.

Appropriation of Information Systems: Using Cognitive Mapping for Eliciting Users' Sensemaking

Abstract: This paper explores the use of cognitive mapping for eliciting users' sensemaking during information system (IS) appropriation. Despite the potential usefulness of sensemaking, few studies in IS research use it as a theoretical lens to address IS appropriation. A possible reason for this may be that sensemaking does not easily lend itself to be used in practice. We introduce cognitive mapping as a way to elicit users' sensemaking and illustrate its value by reporting on findings from an empirical study of the introduction of an Electronic Patient Record (EPR) system. The contribution of the paper is threefold: first, our findings demonstrate cognitive mapping's use for eliciting users' sensemaking during IS appropriation. Second, our findings illustrate how cognitive mapping can be used as a dynamic approach facilitating collective negotiation of meaning. Third, we contribute with a thorough discussion of the epistemological and methodological assumptions underlying cognitive mapping to ensure its validity and trustworthiness.

Ontological Cognitive Map

Abstract: A cognitive map model provides a graphical representation of an influence network between concepts. One drawback of this model is that large cognitive maps are difficult to exploit and understand.This paper introduces an ontological cognitive map model that enables the designer to organize concepts in an ontology. On one hand, this model provides an ontological influence mechanism that shows the influence from any concept of the ontology to any other according to the map. The map is then easier to exploit. On the other hand, the ontology is used as a scale for providing a synthetical view of a map. The map is then easier to understand.

Cognitive approach to decision-making in ill-structured situation control and the problem of risks

Abstract: The basic directions of cognitive approach development in the field of formal methods of searching and making decisions in the control of complex and ill-structured situations are briefly reviewed. The problem of risks for the results validity that arise due to the human factor in the cognitive approach is considered and two kinds of risks which we can pertinently treat as cognitive risks are exposed. On the example of a real cognitive map of a complex and ill-structured situation the analysis of some cognitive risks concerned with causal influence transitivity is carried out. Some explanatory mechanisms and criteria for the early detection of such risks are proposed. The issues important for further development of the cognitive approach to decision-making in the ill-structured situation control and especially of causal mapping techniques are highlighted.

The formation, generative power, and evolution of toponyms: Grounding a spatial vocabulary in a cognitive map

Abstract: We present a series of studies investigating the formation, generative power, and evolution of toponyms (i.e. topographic names). The domain chosen for this project is the spatial concepts related to places in an environment, one of the key sets of concepts to be grounded in autonomous agents. Concepts for places cannot be directly perceived as they require knowledge of relationships between locations in space, with representations inferred from ambiguous sensory data acquired through exploration. A generative toponymic language game has been developed to allow the agents to interact, forming concepts for locations and spatial relations. The studies demonstrate how a grounded generative toponymic language can form and evolve in a population of agents interacting through language games. Initially, terms are grounded in simple spatial concepts directly experienced by the agents. A generative process then enables the agents to learn about and refer to locations beyond their direct experience, enabling concepts and toponyms to co-evolve. The significance of this research is the demonstration of grounding for both experienced and novel concepts, using a generative process, applied to spatial locations.

Bridging the Gap: Learning Sensorimotor-Linked Population Codes for Planning and Motor Control

Abstract: Humans and animals are able to flexibly learn internal, cognitive maps of their environments and are able to use these maps to approach goals efficiently, reliably, and flexibly. Recent neuroscientific evidence suggests that such maps are formed in the hippocampus by means of interconnected place, view, and head direction cell encodings. This paper presents a neural learning architecture that develops an interconnected population code of place cells during random exploration. Connections develop dependent on the experienced sensorimotor contingencies. The learned spatial representation enables the agent to flexibly plan shortest paths to any goal location within the explored environment by means of dynamic programming. It approaches activated goal locations by means of closed loop control. While the algorithm currently relies on the Markov property, it is able to connect the network over radical sensory changes as long as they are close in sensorimotor distance. Moreover, the agent is able to flexibly adjust its behavior dependent on current constraints without further learning. This paper introduces the algorithm and evaluates its robustness and consequent behavioral flexibility.

Coalition Formation of Cognitive Agents.

Abstract: Part of the knowledge and set of beliefs of a cognitive agent are its mental models of the world and of other agents. A mental model reflects the cultural context and past experiences of an adaptive agent. In interacting with other agents, we claim that a causal mental model, i.e. a cognitive map, which might be different for each agent, adapts and changes causing coalitions to emerge. Possible actions are reflected through the respective cognitive maps of the agents to determine whether a favorable steady state will emerge from a coalition. The problems addressed are which coalitions will be more likely to form and how to adapt the cognitive maps of the agents in order to reduce conflicts. The claim is that a group mental model can emerge from individual mental models through the learning and adaptation of cognitive maps. An algorithm based on Particle Swarm Optimization (PSO) for evolving cognitive maps for coalition formation is introduced and experiments on randomly generated cognitive maps are presented. We conclude with possible uses of this adaptive cognitive modeling approach to understand other cultures, predict coalitions/chaos and shifts of allegiance, and induce group formation through avatars in a virtual world.

Incident Response Probabilistic Cognitive Maps

Abstract: Security incident response is a major activity to guarantee the protection of information systems. We develop in this paper a methodology for automatically determining responses to security incidents based on a new category of cognitive maps, referred to as incident response probabilistic cognitive map. The main issue addressed in this paper is the development of a methodology using mathematical tools for reasoning about the constructed cognitive map to identify the attack, to reduce its complexity, and to search for the decision responding to the detected attack. The main features have been highlighted showing the complexity of the incident response team role.