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This book offers the definitive guide to the theory and practice of disaster robotics. It can serve as an introduction for researchers and technologists, a reference for emergency managers, and a textbook in field robotics. Written by a pioneering researcher in the field who has herself participated in fifteen deployments of robots in disaster response and recovery, the book covers theory and practice, the history of the field, and specific missions. After a broad overview of rescue robotics in the context of emergency informatics, the book provides a chronological summary and formal analysis of the thirty-four documented deployments of robots to disasters that include the 2001 collapse of the World Trade Center, Hurricane Katrina, the 2010 Haiti earthquake, the Deepwater Horizon oil spill, the 2011 Japanese earthquake and tsunami, and numerous mining accidents. It then examines disaster robotics in the typical robot modalities of ground, air, and marine, addressing such topics as robot types, missions and tasks, and selection heuristics for each modality. Finally, the book discusses types of fieldwork, providing practical advice on matters that include collecting data and collaborating with emergency professionals. The field of disaster robotics has lacked a comprehensive overview. This book by a leader in the field, offering a unique combination of the theoretical and the practical, fills the gap.

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.

Behavioural studies investigating the relationship between Executive Functions (EFs) demonstrated evidence that different EFs are correlated with each other, but also that they are partially independent from each other. Neuroimaging studies investigating such an interrelationship with respect to the functional neuroanatomical correlates are sparse and have revealed inconsistent findings. To address this question, we created four tasks derived from the same basic paradigm, one each for updating, inhibition, switching, and dual-tasking. We assessed brain activity through functional magnetic resonance imaging (fMRI) in twenty-nine participants while they performed the four EF tasks plus control tasks. For the analysis, we first determined the neural correlates of each EF by subtracting the respective control tasks from the EF tasks. We tested for unity in EF tasks by calculating the conjunction across these four “EF-minus-control” contrasts. This identified common areas including left lateral frontal cortices [middle and superior frontal gyrus (BA 6)], medial frontal cortices (BA 8) as well as parietal cortices [inferior and superior parietal lobules (BA 39/7)]. We also observed areas activated by two or three EF tasks only, such as frontoparietal areas [e.g., SFG (BA8) right inferior parietal lobule (BA 40), left precuneus (BA 7)], and subcortical regions [bilateral thalamus (BA 50)]. Finally, we found areas uniquely activated for updating [bilateral MFG (BA 8) and left supramarginal gyrus (BA 39)], inhibition (left IFG BA 46), and dual-tasking [left postcentral gyrus (BA 40)]. These results demonstrate that the functional neuroanatomical correlates of the four investigated EFs show unity as well as diversity.

Contingency learning—the fundamental process by which associations are formed between events in our experience is as relevant of conditioning as it is for social interactions, where emotional cues, such as facial expressions, signal complex and reciprocal causal dynamics. This study investigates the functional neuroanatomy underlying contingency perception by with three type of contingent relation (positive, zero, and negative) using sad and happy facial expressions as stimuli in a group of neurotypical participants. Employing a streaming trial paradigm and functional MRI, we examined how these emotional contingencies engage brain regions involved in attention and predictive processing. The behavioural results indicated that participants could distinguish between different contingencies, regardless of the emotional stimuli. However, judgment ratings varied across conditions, with sad expressions eliciting weaker ratings compared to happy expressions, which moderated perceived causality, especially in the uncorrelated and negative contingency tasks. These behavioural findings were primarily linked to increased activation in frontal regions, including the inferior frontal gyrus, middle frontal gyrus, and anterior cingulate cortex. The results highlight the differential cognitive demands and neural responses evoked by emotional expressions and suggestive of the idea that statistical relations that violate social expectations are processed differently than positive relations.

Signals that reduce uncertainty can be valuable because well-informed decision-makers can better align their preferences to opportunities. However, some birds and mammals display an appetite for informative signals that cannot be used to increase returns. We explore the role that reward-predictive stimuli have in fostering such preferences, aiming at distinguishing between two putative underlying mechanisms. The ‘information hypothesis’ proposes that reducing uncertainty is reinforcing per se, somewhat consistently with the concept of curiosity: a motivation to know in the absence of tractable extrinsic benefits. In contrast, the ‘conditioned reinforcement hypothesis’, an associative account, proposes asymmetries in secondarily acquired reinforcement: post-choice stimuli announcing forthcoming rewards (S+) reinforce responses more than stimuli signalling no rewards (S−) inhibit responses. In three treatments, rats faced two equally profitable options delivering food probabilistically after a fixed delay. In the informative option (Info), food or no food was signalled immediately after choice, whereas in the non-informative option (NoInfo) outcomes were uncertain until the delay lapsed. Subjects preferred Info when (1) both outcomes were explicitly signalled by salient auditory cues, (2) only forthcoming food delivery was explicitly signalled, and (3) only the absence of forthcoming reward was explicitly signalled. Acquisition was slower in (3), when food was not explicitly signalled, showing that signals for positive outcomes have a greater influence on the development of preference than signals for negative ones. Our results are consistent with an elaborated conditioned reinforcement account, and with the conjecture that both uncertainty reduction and conditioned reinforcement jointly act to generate preference.

Background Microdosing psychedelics is a phenomenon with claimed cognitive benefits that are relatively untested clinically. Pre-clinically, psychedelics have demonstrated enhancing effects on neuroplasticity, which cannot be measured directly in humans, but may be indexed by non-invasive electroencephalography (EEG) paradigms. This study used a visual long-term potentiation (LTP) EEG paradigm to test the effects of microdosed lysergic acid diethylamide (LSD) on neural plasticity, both acutely while on the drug and cumulatively after microdosing every third day for six weeks. Healthy adult males ( n  = 80) completed the visual LTP paradigm at baseline, 2.5 h following a dose of 10 µg of LSD or inactive placebo, and 6 weeks later after taking 14 repeated microdoses. Visually induced LTP was used as indirect index of neural plasticity. Surface level event-related potential (ERPs) based analyses are presented alongside dynamic causal modelling of the source localised data using a generative thalamocortical model (TCM) of visual cortex to elucidate underlying synaptic circuitry. Results Event-related potential (ERP) analyses of N1b and P2 components did not show evidence of changes in visually induced LTP by LSD either acutely or after 6 weeks of regular dosing. However modelling the complete timecourse of the ERP with the TCM demonstrated changes in laminar connectivity in primary visual cortex. This primarily included changes to self-gain and inhibitory input parameters acutely. Layer 2/3 to layer 5 excitatory connectivity was also different between LSD and placebo groups. After regular dosing only excitatory input from layer 2/3 into layer 5 and inhibitory input into layer 4 were different between groups. Conclusions Without modulation of the ERPs it is difficult to relate the findings to other studies visually inducing LTP. It also indicates the classic peak analysis may not be sensitive enough to demonstrate evidence for changes in LTP plasticity in humans at such low doses. The TCM provides a more sensitive approach to assessing changes to plasticity as differences in plasticity mediated laminar connectivity were found between the LSD and placebo groups. Trial registration:  ANZCTR registration number ACTRN12621000436875; Registered 16/04/2021 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381476 .

The Wiley Handbook on the Cognitive Neuroscience of Learning charts the evolution of associative analysis and the neuroscientific study of behavior as parallel approaches to understanding how the brain learns that both challenge and inform each other. * Covers a broad range of topics while maintaining an overarching integrative approach * Includes contributions from leading authorities in the fields of cognitive neuroscience, associative learning, and behavioral psychology * Extends beyond the psychological study of learning to incorporate coverage of the latest developments in neuroscientific research

Using rules extracted from experience to solve problems in novel situations involves cognitions such as analogical reasoning and language learning and is considered a keystone of humans' unique abilities. Nonprimates, it has been argued, lack such rule transfer. We report that Rattus norvegicus can learn simple rules and apply them to new situations. Rats learned that sequences of stimuli consistent with a rule (such as XYX) were different from other sequences (such as XXY or YXX). When novel stimuli were used to construct sequences that did or did not obey the previously learned rule, rats transferred their learning. Therefore, rats, like humans, can transfer structural knowledge from sequential experiences.