Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts

Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia-from thought disorder to delusions-are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant's verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia

Organisation of foraging in ants

In social insects, foraging is often cooperative, and so requires considerable organisation. In most ants, organisation is a bottom-up process where decisions taken by individuals result in emergent colony level patterns. Individuals base their decisions on their internal state, their past experience, and their environment. By depositing trail pheromones, for example, ants can alter the environment, and thus affect the behaviour of their nestmates. The development of emergent patterns depends on both how individuals affect the environment, and how they react to changes in the environment. Chapters 4 – 9 investigate the role of trail pheromones and route memory in the ant Lasius niger. Route memories can form rapidly and be followed accurately, and when route memories and trail pheromones contradict each other, ants overwhelmingly follow route memories (chapter 4). Route memories and trail pheromones can also interact synergistically, allowing ants to forage faster without sacrificing accuracy (chapter 5). Home range markings also interact with other information sources to affect ant behaviour (chapter 6). Trail pheromones assist experienced ants when facing complex, difficult-to-learn routes (chapter 7). When facing complicated routes, ants deposit more pheromone to assist in navigation and learning (chapter 7). Deposition of trail pheromones is suppressed by ants leaving a marked path (chapter 5), strong pheromone trails (chapter 7) and trail crowding (chapter 8). Colony level ‘decisions’ can be driven by factors other than trail pheromones, such as overcrowding at a food source (chapter 9). Chapter 10 reviews the many roles of trail pheromones in ants. Chapters 11 – 14 focus on the organisation of cooperative food retrieval. Pheidole oxyops workers arrange themselves non-randomly around items to increase transport speeds (chapter 11). Groups of ants will rotate food items to reduce drag (chapter 12). Chapters 13 and 14 encompass the ecology of cooperative transport, and how it has shaped trail pheromone recruitment in P. oxyops and Paratrechina longicornis. Lastly, chapter 15 provide a comprehensive review of cooperative transport in ants and elsewhere

Predicting user behavior using data profiling and hidden Markov model

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data

Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization

We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guests dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign. We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitor paths to get behavioral insights, including the most common flow patterns. On these bases, we build the transition matrix describing, in probability, the room-scale visitor flows. Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in silico. We conclude by employing the simulator to increase the number of daily visitors while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management

Experimental approaches to unravel proximate mechanisms of parasitoid searching and patch leaving behaviour

Animals exploit complex environments in an optimal way, often with limited brain capacities. Interestingly, it is largely unknown, how they do so. This thesis comprises five studies investigating proximate mechanisms modulating the searching behaviour of parasitoid wasps. These organisms serve as excellent organisms for such questions due to their tight link of searching success and fitness. While the first study assumed a simple motor response to serve as a heuristic, yet effective, mechanism, the remaining studies focussed on the role of octopamine [OA] and dopamine [DA]. Both substances being essential in the assessment of reward and aversive stimuli, respectively. Neither the assumed motor response could be met nor did OA or DA reveal any consistent effects with respect to the assessment of rewards and costs. DA slightly impacted the movement pattern. Treatment with OA revealed numerous effects, in total indicating an influence on stress level. Both is in line with studies on other species. Yet, although OA significantly influences searching behaviour, the underlying mechanism is considerably more complex than initially assumed. Last, it was shown that a generalisation on the basis of a few studies and stimuli with respect to the role of OA in the integration of rewards is a simplification

Walking, Crossing Streets and Choosing Pedestrian Routes: A Survey of Recent Insights from the Social/Behavioral Sciences

Walking at first appears to be a relatively simple, mundane behavior that should pose no great puzzle for the diligent researcher in the social and behavioral sciences. The review presented here of recent studies, however, demonstrates that the behavior and experiences of ordinary pedestrians are filled with opportunities for empirical investigation and intricate theory building. But, why bring these studies together for synthesis in this volume? I suggest here that there are, in fact, several reasons that argue in favor of a timely focus on the apparently simple behavior of the pedestrian. First, the deceptive simplicity of the pedestrian experience provides an excellent empirical focus for examination of a wide range of topics prominent in recent work in the emerging field of human-environment studies. Readers unfamiliar with the scope and intensity of research in this interdisciplinary enterprise would do well to consult the pages of Environment and Behavior; Man Environment Systems; Environment and Planning; the annual proceedings of the Environmental Design Research Association (EDRA); and the topical volumes in the new review series entitled Human Behavior and Environment: Advances in Theory and Research, edited by Irwin Altman, Amos Rapoport, and Joachim Wohlwill. Even summary consideration of the many topics that have become the focus of considerable investigation in the last decade reveals that empirical and conceptual work regarding territoriality, crowding, privacy, personal space, sensory overload and deprivation, approach-avoidance, navigation and orientation, mental mapping, search processes, and environmental perception, evaluation, and decision making all bear on various facets of the pedestrian experience. Empirical verification of the viability of these conceptual ideas reveals a void which the study of the pedestrian helps to fill. The inner processes and complexity of pedestrian behavior are far greater, for example, than the outward simplicity suggested by the simple geometrical representation of a pedestrian trip as a line connecting an origin and a destination. The complexity that lies behind this apparent simplicity provides a major challenge for the students of human-environment relations

A swarm intelligence based approach to the mine detection problem

This research focuses on the application of swarm intelligence to the problem of mine detection. Swarm Intelligence concepts have captivated the interests of researchers mainly in collective robotics, optimization problems (traveling salesman problem (TSP), quadratic assignment problem, graph coloring etc.), and communication networks (routing) etc [1]. In the mine detection problem we are faced with sub problems such as searching for the mines over the minefield, defusing them effectively, and assuring that the field is clear of mines within the least possible time. In the problem, we assume that the mines can be diffused by the collective action of the robots for which a model based on ant colonies is given. In the first part of the project we study the ant colony system applied to the mine detection problem. The theoretical aspects such as the ant\u27s behavior (reaction of the ants to various circumstances that it faces), their motion over the minefield, and their process of defusing the mines are investigated. In the second section we highlight a certain formulation that the ants may be given for doing the task effectively. The ants do the task effectively when they are able to assure that the minefield is clear of the mines within the least possible time. A compilation of the results obtained by the various studies is tabulated. In the third and final section we talk about our emulations conducted on the Multi Agent Biorobotics Lab-built groundscout robots, which were used for the demonstration of our swarm intelligence-based algorithms at a practical basis. The various projects thus far conducted were a part of the Multi Agent Biorobotics Lab at Rochester Institute of Technology