Navigation Behavior Research Articles

Neuronal activity in the ventral tegmental area during goal-directed navigation recorded by low-curvature microelectrode arrays

Navigating toward destinations with rewards is a common behavior among animals. The ventral tegmental area (VTA) has been shown to be responsible for reward coding and reward cue learning, and its response to other variables, such as kinematics, has also been increasingly studied. These findings suggest a potential relationship between animal navigation behavior and VTA activity. However, the deep location and small volume of the VTA pose significant challenges to the precision of electrode implantation, increasing the uncertainty of measurement results during animal navigation and thus limiting research on the role of the VTA in goal-directed navigation. To address this gap, we innovatively designed and fabricated low-curvature microelectrode arrays (MEAs) via a novel backside dry etching technique to release residual stress. Histological verification confirmed that low-curvature MEAs indeed improved electrode implantation precision. These low-curvature MEAs were subsequently implanted into the VTA of the rats to observe their electrophysiological activity in a freely chosen modified T-maze. The results of the behavioral experiments revealed that the rats could quickly learn the reward probability corresponding to the left and right paths and that VTA neurons were deeply involved in goal-directed navigation. Compared with those in no-reward trials, VTA neurons in reward trials presented a significantly greater firing rate and larger local field potential (LFP) amplitude during the reward-consuming period. Notably, we discovered place fields mapped by VTA neurons, which disappeared or were reconstructed with changes in the path–outcome relationship. These results provide new insights into the VTA and its role in goal-directed navigation. Our designed and fabricated low-curvature microelectrode arrays can serve as a new device for precise deep brain implantation in the future.

Peripersonal space: why so last-second?

A vast range of neurophysiological, neuropsychological and behavioural results in monkeys and humans have shown that the immediate surroundings of the body, also known as peripersonal space (PPS), are processed in a unique way. Three roles have been ascribed to PPS mechanisms: to react to threats, to avoid obstacles and to act on objects. However, in many circumstances, one does not wait for objects or agents to enter PPS to plan these behaviours. Typically, one has more chances to survive if one starts running away from the lion when one sees it in the distance than if it is a few steps away. PPS makes sense in shortsighted creatures but we are not such creatures. The crucial question is thus twofold: (i) why are these adaptive processes triggered only at the last second or even milliseconds? And (ii) what is their exact contribution, especially for defensive and navigational behaviours? Here, we propose that PPS mechanisms correspond to a plan B, useful in unpredictable situations or when other anticipatory mechanisms have failed. Furthermore, we argue that there are energetic, cognitive and behavioural costs to PPS mechanisms, which explain why this plan B is triggered only at the last second. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Exploring Young Adults' Mental Health Help‐Seeking Journey: Preliminary Findings on Resource Navigation Behavior

ABSTRACTYoung adults are among the most vulnerable populations susceptible to mental health concerns, yet they encounter various barriers in accessing resources and obtaining necessary support and treatment. While prior research has predominantly focused on technology utilization in mental health help‐seeking, a more comprehensive understanding of how individuals navigate the broader landscape of available resources is crucial. To address this gap, we conducted in‐depth interviews with 18 young adults to explore their lived experiences and resource navigation during the mental health help‐seeking process. Guided by the Social‐Ecological Framework, our study categorized the resources utilized by young adults into four categories: technological, interpersonal, community‐based, and societal. Through thematic analysis, we report preliminary findings that identify specific characteristics of resources that young adults value and prioritize throughout their help‐seeking journey. These findings provide implications for designing multi‐level mental health resources and interventions to facilitate young adults' help‐seeking. We also discuss methodological considerations to encourage in‐depth conversations with marginalized groups on sensitive topics like mental health, emphasizing the importance of creating a safe, non‐judgmental space for participants to share their experiences openly.

Assessment of an Agent’s Wayfinding of the Urban Environment Through Reinforcement Learning

This simulation study explores wayfinding motivated behavioral patterns in the city through agent-based modelling. Agents were trained using Unity’s ML-Agents toolkit with reinforcement learning. The study uses the Sultan Ahmet Mosque and its surrounding boundary as a model environment for the training of an agent’s wayfinding. Agents are trained to locate the Sultan Ahmet Mosque target. The behaviors of agents trained with two different methods, “Complex” and “Simple” learning, comparing their navigation quests at various difficulty levels featuring respawn points. After the training of the agents, the alternative routes produced while attaining the target during the wayfinding process were analyzed. As an outcome of the analysis, it was observed that the agents were prone to go off-route, navigate to different locations they perceived in the urban space, and then would reach the target. This occurrence is justified as an agent’s curiosity trained through reinforcement learning. This study differs from the literature in a way that it attempts to understand the navigational behavior of agents that were trained with reinforcement learning. Moreover, this research discusses the perception of wayfinding through curiosity and aims to make a comprehension of the perception of the city, which is one of the key ideas in neurourbanism. The study contributes to the literature by showing that wayfinding behaviors acquired from agents’ curiosity-driven explorations and past experiences can be an input for neurourbanism, supporting urban design. It informs urban enhancements that are user-centric and rich in urban perception using the reinforcement learning method.

An adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features

Existing algorithms for trajectory simplification in AIS data processing have issues such as loss of key feature points, high subjectivity in threshold selection, and incomplete consideration of factors. In this paper, an adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features is proposed. The algorithm identifies behavioral feature intervals by extracting vessel behavioral features and constructs an adaptive segmentation threshold function to achieve adaptive segmentation of trajectories and extraction of key feature points. Then, considering the quality, similarity, and spatial variability of trajectory simplification, an adaptive simplification threshold function is constructed to automatically select the best threshold for each sub-trajectory to generate the simplified trajectory. Our experiments demonstrate that the algorithm maintains a high simplification rate while enabling adaptive simplification of the trajectory, and also retains the key feature points and trajectory shapes to the maximum extent. The effectiveness and robustness of the algorithm are verified through comparison with other algorithms, effectively avoiding the distortion of navigational behavior that may be caused by traditional methods. This provides reliable data support for maritime traffic management and decision-making.

Swarm Robotics Navigation Task: A Comparative Study of Reinforcement Learning and Particle Swarm Optimization Methodologies

Automatic design methods focus on generating the collective behavior of swarm robotic systems. These methods enable multiple robots to coordinate and execute complex tasks in their environments autonomously. This research paper investigated two prominent methodologies: particle swarm optimization (PSO) and reinforcement learning (RL). A new comparative study was conducted to analyze the performance of a group of mobile robots through extensive experimentation. The objective was to produce navigational collective behavior through unknown environments. These environments differ in complexity ranging from obstacle-free environments to cluttered ones. The core metrics of the comparison include the time efficiency of individual robots and the overall swarm, flexibility in pathfinding, and the ability to generalize solutions for new environments. The obtained results from the Webots simulator with Python controller suggested that RL excels in environments closely aligned with its training conditions. RL achieved a faster completion time and demonstrated superior coordination among individual robots. However, its performance dips when facing untrained scenarios necessitating computationally expensive retraining or structural complexities to enhance adaptability. Conversely, PSO showed commendable consistency in performance. Despite its slower pace, it exhibited robustness in various challenging settings without reconfiguration.

A spatiotemporal co-occurrence pattern mining algorithm based on ship trajectory data

Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.

Integration of memory systems supporting non-symbolic representations in an architecture for lifelong development of artificial agents

Compared to autonomous agent learning, lifelong agent learning tackles the additional challenge of accumulating skills in a way favourable to long term development. What an agent learns at a given moment can be an element for the future creation of behaviours of greater complexity, whose purpose cannot be anticipated.Beyond its initial low-level sensorimotor development phase, the agent is expected to acquire, in the same manner as skills, values and goals which support the development of complex behaviours beyond the reactive level. To do so, it must have a way to represent and memorize such information.In this article, we identify the properties suitable for a representation system supporting the lifelong development of agents through a review of a wide range of memory systems and related literature. Following this analysis, our second contribution is the proposition and implementation of such a representation system in MIND, a modular architecture for lifelong development. The new variable module acts as a simple memory system which is strongly integrated to the hierarchies of skill modules of MIND, and allows for the progressive structuration of behaviour around persistent non-symbolic representations. Variable modules have many applications for the development and structuration of complex behaviours, but also offer designers and operators explicit models of values and goals facilitating human interaction, control and explainability.We show through experiments two possible uses of variable modules. In the first experiment, skills exchange information by using a variable representing the concept of “target”, which allows the generalization of navigation behaviours. In the second experiment, we show how a non-symbolic representation can be learned and memorized to develop beyond simple reactive behaviour, and keep track of the steps of a process whose state cannot be inferred by observing the environment.

A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion.

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts' tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.

Predictive Prefetching in Client–Server Systems: A Navigational Behavior Modeling Approach

Predictive Prefetching in Client–Server Systems: A Navigational Behavior Modeling Approach

You-talk in young adult couples’ conflict: Family-of-origin roots and adult relational aggression sequelae

The present study examines greater use of the word “you” (i.e., you-talk) during couple’s conflict as linked to conflict behaviors and relational aggression. The way couples navigate relationship conflict is a key risk factor for relational aggression, and investigating conflict microprocesses can inform intervention efforts. In this study, 184 target participants (86 men, 98 women; 58% White, 29% African American, 8% mixed race/ethnicity, 5% other groups) were observed interacting with their parents at age 13 and with romantic partners at ages 20 and 27 to examine origins of you-talk usage and its links to romantic relationship dysfunction. Links were explored in a series of hierarchical linear regressions. Adverse conflict navigation behaviors established in one’s family-of-origin during adolescence (i.e., autonomy-relatedness undermining behavior) predicted target’s use of you-talk during conflict with their romantic partners at age 20. You-talk was concurrently associated with autonomy-relatedness undermining behavior and relational aggression, and you-talk in turn predicted a relative increase in both undermining behavior and relational aggression at age 27. Use of you-talk is discussed as disrupting key features of conflict navigation and having potential developmental origins which may serve as targets in efforts to reduce relational aggression.

Parallel vector memories in the brain of a bee as foundation for flexible navigation

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties

Goal-directed navigation requires continuously integrating uncertain self-motion and landmark cues into an internal sense of location and direction, concurrently planning future paths, and sequentially executing motor actions. Here, we provide a unified account of these processes with a computational model of probabilistic path planning in the framework of optimal feedback control under uncertainty. This model gives rise to diverse human navigational strategies previously believed to be distinct behaviors and predicts quantitatively both the errors and the variability of navigation across numerous experiments. This furthermore explains how sequential egocentric landmark observations form an uncertain allocentric cognitive map, how this internal map is used both in route planning and during execution of movements, and reconciles seemingly contradictory results about cue-integration behavior in navigation. Taken together, the present work provides a parsimonious explanation of how patterns of human goal-directed navigation behavior arise from the continuous and dynamic interactions of spatial uncertainties in perception, cognition, and action.

Retrosplenial inputs drive visual representations in the medial entorhinal cortex

The importance of visual cues for navigation and goal-directed behavior is well established, although the neural mechanisms supporting sensory representations in navigational circuits are largely unknown. Navigation is fundamentally dependent on the medial entorhinal cortex (MEC), which receives direct projections from neocortical visual areas, including the retrosplenial cortex (RSC). Here, we perform high-density recordings of MEC neurons in awake, head-fixed mice presented with simple visual stimuli and assess the dynamics of sensory-evoked activity. We find that a large fraction of neurons exhibit robust responses to visual input. Visually responsive cells are located primarily in layer 3 of the dorsal MEC and can be separated into subgroups based on functional and molecular properties. Furthermore, optogenetic suppression of RSC afferents within the MEC strongly reduces visual responses. Overall, our results demonstrate that the MEC can encode simple visual cues in the environment that may contribute to neural representations of location necessary for accurate navigation.

Hippocampal-dependent navigation in head-fixed mice using a floating real-world environment

Head-fixation of mice enables high-resolution monitoring of neuronal activity coupled with precise control of environmental stimuli. Virtual reality can be used to emulate the visual experience of movement during head fixation, but a low inertia floating real-world environment (mobile homecage, MHC) has the potential to engage more sensory modalities and provide a richer experimental environment for complex behavioral tasks. However, it is not known whether mice react to this adapted environment in a similar manner to real environments, or whether the MHC can be used to implement validated, maze-based behavioral tasks. Here, we show that hippocampal place cell representations are intact in the MHC and that the system allows relatively long (20 min) whole-cell patch clamp recordings from dorsal CA1 pyramidal neurons, revealing sub-threshold membrane potential dynamics. Furthermore, mice learn the location of a liquid reward within an adapted T-maze guided by 2-dimensional spatial navigation cues and relearn the location when spatial contingencies are reversed. Bilateral infusions of scopolamine show that this learning is hippocampus-dependent and requires intact cholinergic signalling. Therefore, we characterize the MHC system as an experimental tool to study sub-threshold membrane potential dynamics that underpin complex navigation behaviors.

Performance and Navigation Behavior of using Teleportation in VR First-Person Shooter Games

Teleportation has been adopted as the preferred mode of navigation in many virtual reality (VR) experiences due to its ability to allow users to easily move beyond the limitations of the tracking space while minimizing the risk of inducing VR sickness. Teleportation instantly translates the user’s viewpoint to a user-selected destination and therefore eliminates any optical flow generation that could cause visual-vestibular conflict. Though teleportation is a discrete navigation method unique to the domain of VR, most VR experiences are modeled after 3D experiences found on desktop/console platforms that use continuous locomotion. How the use of teleportation affects the performance and navigation behavior of its users, especially in competitive first-person shooter environments is unknown, yet it could have a significant effect on gameplay design. We conducted a user study (n=21) that compares teleportation versus continuous locomotion using a VR first-person shooter game with other players being simulated using AI agents. We found significant differences in performance, navigation behavior, and how both locomotion methods are perceived by its users. Specifically, using teleportation, players traveled farther, but during combat were found to be more stationary and as a result got hit more frequently. These differences were profound and carry the potential to impact multiplayer games. We discuss possible strategies to balance gameplay.

The well-worn route revisited: Striatal and hippocampal system contributions to familiar route navigation.

Classic research has shown a division in the neuroanatomical structures that support flexible (e.g., short-cutting) and habitual (e.g., familiar route following) navigational behavior, with hippocampal-caudate systems associated with the former and putamen systems with the latter. There is, however, disagreement about whether the neural structures involved in navigation process particular forms of spatial information, such as associations between constellations of cues forming a cognitive map, versus single landmark-action associations, or alternatively, perform particular reinforcement learning algorithms that allow the use of different spatial strategies, so-called model-based (flexible) or model-free (habitual) forms of learning. We sought to test these theories by asking participants (N = 24) to navigate within a virtual environment through a previously learned, 9-junction route with distinctive landmarks at each junction while undergoing functional magnetic resonance imaging (fMRI). In a series of probe trials, we distinguished knowledge of individual landmark-action associations along the route versus knowledge of the correct sequence of landmark-action associations, either by having absent landmarks, or "out-of-sequence" landmarks. Under a map-based perspective, sequence knowledge would not require hippocampal systems, because there are no constellations of cues available for cognitive map formation. Within a learning-based model, however, responding based on knowledge of sequence would require hippocampal systems because prior context has to be utilized. We found that hippocampal-caudate systems were more active in probes requiring sequence knowledge, supporting the learning-based model. However, we also found greater putamen activation in probes where navigation based purely on sequence memory could be planned, supporting models of putamen function that emphasize its role in action sequencing.

Reinforcement learning with imitative behaviors for humanoid robots navigation: synchronous planning and control

Reinforcement learning with imitative behaviors for humanoid robots navigation: synchronous planning and control

Presenting TaMuNaBe: A taxonomy of museum navigation behaviors.

Presenting TaMuNaBe: A taxonomy of museum navigation behaviors.