Navigating in a challenging semiarid environment: the use of a route-based mental map by a small-bodied neotropical primate

To increase efficiency in the search for resources, many animals rely on their spatial abilities. Specifically, primates have been reported to use mostly topological and rarely Euclidean maps when navigating in large-scale space. Here, we aimed to investigate if the navigation of wild common marmosets inhabiting a semiarid environment is consistent with a topological representation and how environmental factors affect navigation. We collected 497 h of direct behavioral and GPS information on a group of marmosets using a 2-min instantaneous focal animal sampling technique. We found that our study group reused not only long-route segments (mean of 1007 m) but entire daily routes, a pattern that is not commonly seen in primates. The most frequently reused route segments were the ones closer to feeding sites, distant to resting sites, and in areas sparse in tree vegetation. We also identified a total of 56 clustered direction change points indicating that the group modified their direction of travel. These changes in direction were influenced by their close proximity to resting and feeding sites. Despite our small sample size, the obtained results are important and consistent with the contention that common marmosets navigate using a topological map that seems to benefit these animals in response to the exploitation of clustered exudate trees. Based on our findings, we hypothesize that the Caatinga landscape imposes physical restrictions in our group’s navigation such as gaps in vegetation, small trees and xerophytic plants. This study, based on preliminary evidence, raises the question of whether navigation patterns are an intrinsic characteristic of a species or are ecologically dependent and change according to the environment.

     

Children’s mapping between symbolic and nonsymbolic representations of number

When children learn to count and acquire a symbolic system for representing numbers, they map these symbols onto a preexisting system involving approximate nonsymbolic representations of quantity. Little is known about this mapping process, how it develops, and its role in the performance of formal mathematics. Using a novel task to assess children’s mapping ability, we show that children can map in both directions between symbolic and nonsymbolic numerical representations and that this ability develops between 6 and 8 years of age. Moreover, we reveal that children’s mapping ability is related to their achievement on tests of school mathematics over and above the variance accounted for by standard symbolic and nonsymbolic numerical tasks. These findings support the proposal that underlying nonsymbolic representations play a role in children’s mathematical development.     

Applying automated deduction to natural language understanding

Very few natural language understanding applications employ methods from automated deduction. This is mainly because (i) a high level of interdisciplinary knowledge is required, (ii) there is a huge gap between formal semantic theory and practical implementation, and (iii) statistical rather than symbolic approaches dominate the current trends in natural language processing. Moreover, abduction rather than deduction is generally viewed as a promising way to apply reasoning in natural language understanding. We describe three applications where we show how first-order theorem proving and finite model construction can efficiently be employed in language understanding.The first is a text understanding system building semantic representations of texts, developed in the late 1990s. Theorem provers are here used to signal inconsistent interpretations and to check whether new contributions to the discourse are informative or not. This application shows that it is feasible to use general-purpose theorem provers for first-order logic, and that it pays off to use a battery of different inference engines as in practice they complement each other in terms of performance.The second application is a spoken-dialogue interface to a mobile robot and an automated home. We use the first-order theorem prover spass for checking inconsistencies and newness of information, but the inference tasks are complemented with the finite model builder mace used in parallel to the prover. The model builder is used to check for satisfiability of the input; in addition, the produced finite and minimal models are used to determine the actions that the robot or automated house has to execute. When the semantic representation of the dialogue as well as the number of objects in the context are kept fairly small, response times are acceptable to human users.The third demonstration of successful use of first-order inference engines comes from the task of recognising entailment between two (short) texts. We run a robust parser producing semantic representations for both texts, and use the theorem prover vampire to check whether one text entails the other. For many examples it is hard to compute the appropriate background knowledge in order to produce a proof, and the model builders mace and paradox are used to estimate the likelihood of an entailment.     

压后皮质在空间导航的作用：认知地图与环境的映射

压后皮质在大脑中紧邻胼胝体压部,在空间导航中起到重要作用。但其在空间导航过程中的功能尚未明确,目前存在空间更新、地标领航和参照系转换三种观点。本文通过梳理上述观点,认为它们背后可能存在一致的认知过程,即对认知地图与环境布局进行映射。在基于认知地图的导航过程中,这种映射功能对于解释压后皮质的激活有一定的合理性。未来研究应该注重对压后皮质的功能定位,同时注意空间尺度和熟悉性对任务表现的影响。     

Map-based navigation in mobile robots:: I. A review of localization strategies

For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that must all be solved in an integrated manner to promote survival. This point is illustrated here, within the framework of a review of localization strategies in mobile robots. The allothetic and idiothetic sensors that may be used by these robots to build internal representations of their environment, and the maps in which these representations may be instantiated, are first described. Then map-based navigation systems are categorized according to a three-level hierarchy of localization strategies, which respectively call upon direct position inference, single-hypothesis tracking, and multiple-hypothesis tracking. The advantages and drawbacks of these strategies, notably with respect to the limitations of the sensors on which they rely, are discussed throughout the text.     

Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies

This article reviews map-learning and path-planning strategies within the context of map-based navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, which must be tightly tailored to the specific robot that is used, notably to the capacities and limitations of its sensory-motor equipment, and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot’s way to its goal.     

The CORTEX cognitive robotics architecture: Use cases

CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as “the person is talking and gazing at me”. The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.     

Differences in spatial knowledge acquired from maps and navigation

Models of the spatial knowledge people acquire from maps and navigation and the procedures required for spatial judgments using this knowledge are proposed. From a map, people acquire survey knowledge encoding global spatial relations. This knowledge resides in memory in images that can be scanned and measured like a physical map. From navigation, people acquire procedural knowledge of the routes connecting diverse locations. People combine mental simulation of travel through the environment and informal algebra to compute spatial judgments. An experiment in which subjects learned an environment from navigation or from a map evaluates predictions of these models. With moderate exposure, map learning is superior for judgments of relative location and straight-line distances among objects. Learning from navigation is superior for orienting oneself with respect to unseen objects and estimating route distances. With extensive exposure, the performance superiority of maps over navigation vanishes. These and other results are consonant with the proposed mechanisms.     

Look where you have to go! A field study comparing looking behaviour at urban intersections using a navigation system or a printed route instruction

In this study, experienced navigation system users (N = 20) drove a given unfamiliar route twice: once with the navigation system activated and once with a printed instruction including a route instruction. Quantitative analysis indicated that drivers passed intersections slower when they used the printed instruction than when they used the navigation system. Drivers looked more often and in proportion longer to the side scene when they used the printed instruction and made less and proportionally shorter glances away from the road scene and to the instruction than when they were supported by the navigation system. No difference was found between these two conditions in the total number of glances and the amount and duration of glances to the forward scene. A qualitative analysis provided understanding of the quantitative results: the type of route guidance was identified to influence drivers’ motive for scanning the side road scene. When the navigation system was used the motive was primarily to look for potential hazards and when the printed instruction was used the motive was more focused to look for salient orientation points. The outcomes of the study are discussed in terms of looking motive and the ‘look but failed to see’ phenomenon.     

Rats build and update topological representations through exploration

Although rats are able to build complex spatial representations of their surroundings during exploration, the nature of the encoded information is still a matter for debate. In particular, it is not well established if rats can process the topological structure of the environment in such a way that they are aware of the connections existing between remote places. Here, rats were first exposed for four 5-min trials to a complex environment divided into several sectors that were separated by doors allowing either unrestricted or restricted access to other sectors. In the fifth test trial, we measured the behavior of the animals while they explored the same environment in which, however, they faced changes that either altered or did not alter the topological structure of the environment. In experiment 1, closing previously opened doors prevented the rat from having direct access between corresponding sectors. In experiment 2, opening previously closed doors allowed direct access between sectors that had not been directly accessible. In each experiment, control doors allowed us to discard the mere influence of door manipulation. We compared the rats’ exploratory behavior in response to door manipulations that either strongly altered or did not alter the ability to commute between sectors and found evidence that the animals displayed differential reactions to the two types of door manipulations. This implies that during exploration rats build a precise map of the connectivity of space that can be flexibly updated and used for efficient navigation.     

Verbal cues flexibly transform spatial representations in human memory

ABSTRACT

Humans possess a unique ability to communicate spatially-relevant information, yet the intersection between language and navigation remains largely unexplored. One possibility is that verbal cues accentuate heuristics useful for coding spatial layouts, yet this idea remains largely untested. We test the idea that verbal cues flexibly accentuate the coding of heuristics to remember spatial layouts via spatial boundaries or landmarks. The alternative hypothesis instead conceives of encoding during navigation as a step-wise process involving binding lower-level features, and thus subsequently formed spatial representations should not be modified by verbal cues. Across three experiments, we found that verbal cues significantly affected pointing error patterns at axes that were aligned with the verbally cued heuristic, suggesting that verbal cues influenced the heuristics employed to remember object positions. Further analyses suggested evidence for a hybrid model, in which boundaries were encoded more obligatorily than landmarks, but both were accessed flexibly with verbal instruction. These findings could not be accounted for by a tendency to spend more time facing the instructed component during navigation, ruling out an attentional-encoding mechanism. Our findings argue that verbal cues influence the heuristics employed to code environments, suggesting a mechanism for how humans use language to communicate navigationally-relevant information.     

Building a cognitive map by assembling multiple path integration systems

Path integration and cognitive mapping are two of the most important mechanisms for navigation. Path integration is a primitive navigation system which computes a homing vector based on an animal’s self-motion estimation, while cognitive map is an advanced spatial representation containing richer spatial information about the environment that is persistent and can be used to guide flexible navigation to multiple locations. Most theories of navigation conceptualize them as two distinctive, independent mechanisms, although the path integration system may provide useful information for the integration of cognitive maps. This paper demonstrates a fundamentally different scenario, where a cognitive map is constructed in three simple steps by assembling multiple path integrators and extending their basic features. The fact that a collection of path integration systems can be turned into a cognitive map suggests the possibility that cognitive maps may have evolved directly from the path integration system.     

Conceptual layers and strategies in tour planning

We present an exploratory study of an everyday navigation planning situation, addressing spatial planning strategies as well as cognitive shifts between the visually available map and the conceptualized real-world environment. Participants were asked to plan a diversified holiday route on an island, with the help of a map representing spatial as well as activity information. Following the task proper, they reported in written form about the problem-solving process. Route trajectories were analyzed with respect to their properties, and reports were analyzed with respect to the represented concepts and linguistic patterns. Results reveal that route trajectories tended to be circular rather than random, with relatively few detours or crossing lines. The underlying spatial planning strategies as represented in the written reports resembled earlier findings on the Traveling Salesperson Problem, providing insights into the extent to which this abstract task transfers to a naturalistic scenario. Most crucially, our linguistic analysis provides new results about the representation of conceptual layers when considering the real-world navigation domain of traveling in relation to the actual table-top map planning domain.     

Wayfinding through an unfamiliar environment

Strategies for finding one's way through an unfamiliar environment may be helped by 2D maps, 3D virtual environments, or other navigation aids. The relative effectiveness of aids was investigated. Experiments were conducted in a large, park-like environment. 24 participants (12 men, 12 women; age range = 22-50 years; M=32, SD = 7.4) were divided into three groups of four individuals, who explored a 2D map of a given route prior to navigation, received a silent guided tour by means of an interactive virtual representation, or acquired direct experience of the real route through a silent guided tour. Participants then had to find the same route again on their own. 12 observers were given a "simple" route with only one critical turn, and the other 12 a "complex" route with six critical turns. Compared to three people familiar with the routes, among the naive participants, those who had a direct experience prior to navigation all found their way again on the simple and complex routes. Those who had explored the interactive virtual environment were unable to find their way on the complex route. The relative scale representation in the virtual environment may have given incorrect impressions of relative distances between objects along the itinerary, rendering important landmark information useless.     

Active navigation and orientation-free spatial representations

In this study, we examined the orientation dependency of spatial representations following various learning conditions. We assessed the spatial representations of human participants after they had learned a complex spatial layout via map learning, via navigating within a real environment, or via navigating through a virtual simulation of that environment. Performances were compared between conditions involving (1) multiple- versus single-body orientation, (2) active versus passive learning, and (3) high versus low levels of proprioceptive information. Following learning, the participants were required to produce directional judgments to target landmarks. Results showed that the participants developed orientation-specific spatial representations following map learning and passive learning, as indicated by better performance when tested from the initial learning orientation. These results suggest that neither the number of vantage points nor the level of proprioceptive information experienced are determining factors; rather, it is the active aspect of direct navigation that leads to the development of orientation-free representations.     

Active route learning in virtual environments: disentangling movement control from intention, instruction specificity, and navigation control

Active navigation research examines how physiological and psychological involvement in navigation benefits spatial learning. However, existing conceptualizations of active navigation comprise separable, distinct factors. This research disentangles the contributions of movement control (i.e., self-contained vs. observed movement) as a central factor from learning intention (Experiment 1), instruction specificity and instruction control (Experiment 2), as well as navigation control (Experiment 3) to spatial learning in virtual environments. We tested the effects of these factors on landmark recognition (landmark knowledge), tour-integration and route navigation (route knowledge). Our findings suggest that movement control leads to robust advantages in landmark knowledge as compared to observed movement. Advantages in route knowledge do not depend on learning intention, but on the need to elaborate spatial information. Whenever the necessary level of elaboration is assured for observed movement, too, the development of route knowledge is not inferior to that for self-contained movement.     

Intentional Teaching Facilitates Young Children's Comprehension and Use of a Symbolic Object

Children are exposed to symbolic objects that they have to learn to use very early in life. The authors’ aim was to examine whether it is possible to intentionally teach young children the symbolic function of an object. They employed a search task in which children had to use a map to find a toy. Experiment 1 revealed that with no instruction 3-year-, 10-month-old children were quite successful; 3-year-, 6-month-olds showed a divided performance; and 3-year-, 0-month-olds failed. With this baseline, Experiment 2 compared the performance of 3-year-, 0-month-olds in three different conditions: no-instruction, complete instruction (before the task begins), and teaching (complete instruction plus corrective feedback); only children in the teaching condition succeeded. However, children 6 months younger, 2-year-, 6-month-olds, failed despite teaching that was provided (Study 3). This research shows that at some points in development instruction is not enough; intentional teaching in communicative contexts is the mechanism that boosts symbolic understanding in early childhood.