Thesis advisor: Gorica Petrovich / Novel foods and novel environments both impact consumption, but their interaction is poorly understood, especially how this interaction varies across habituation and by sex. Prior studies found that placement in a novel context suppressed consumption of a novel food across habituation in a two-choice paradigm with familiar food, and there were neural correlates in the amygdala of consumption under novelty during the first exposure. The current study extended these findings using a paradigm with only a novel food. We placed adult male and female rats in a novel or familiar environment and measured their consumption of a novel, palatable food across four habituation sessions and a final test session. We collected brain tissue after the test session to measure Fos induction with immunohistochemistry during the final exposure to novelty. Fos induction was measured in the central nucleus of the amygdala and the nuclei of the basolateral complex. We found that placement in a novel context suppressed consumption of a novel food at every time point. During the test, Fos induction was elevated in groups tested in the novel context in the medial part of the central nucleus and all nuclei of the basolateral complex except the anterior part of the basolateral nucleus despite the test being the fifth exposure to the novel stimuli. Parts of the central nucleus and nuclei of the basolateral complex showed sex-specific elevations in Fos induction in females regardless of the testing context. Correlations of Fos induction across regions showed that novel context tested groups had similarly elevated Fos induction throughout the central nucleus and basolateral complex, unlike their familiar context tested counterparts. Females had more correlations of Fos induction than males regardless of testing context. These results demonstrated that habituation to eating a novel food is prolonged in a novel environment compared to a familiar environment. Notably, Fos induction remained high in the novel context groups after multiple exposures to novelty. These behavioral and neural findings demonstrate that unfamiliar environments remain salient throughout the process of habituation. / Thesis (MA) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology and Neuroscience.

The ability to suppress a prepotent response is a crucial component of cognition that begins to develop during infancy and peeks during preschool. As part of understanding how one develops inhibitory control, learning about what conditions may help or hurt task performance is of great interest. The purpose of this project was to study the effects of familiarity and novelty on inhibitory control. Thirty-five preschoolers between two and five years of age were tested in four different versions of the Dimensional Change Card Sort (DCCS). Performance was no different among standard, 2D-familiar, and 3D-familiar conditions. When comparing novel with the standard condition, children performed worse (37% and 68% respectively). Findings support the attentional inertia hypothesis. An exploratory analysis on temperament was also investigated. Children who scored higher in effortful control performed better in the 2D-familiar condition. / Master of Science

As human populations increase and city borders grow, many animals have to modify their foraging behaviours to exploit evolutionarily novel urban food sources that could aid their survival. Neophobia, the fear of novelty, can lead to missed opportunities in these cases. Novelty is therefore expected to elicit different responses in urban and rural populations, a difference that has been frequently studied, but with mixed results. The main objective of my thesis was to study the novelty response of wild black-capped chickadees (Poecile atricapillus) in ecologically relevant conditions while controlling for individual characteristics and potential differences in foraging group size. I predicted that urban black-capped chickadees would be more likely to initially contact novelty than rural chickadees, and that subordinates and juveniles would be more likely to first contact novelty than dominants and adults, respectively. I ran replicated experiments using three novelty types (object, colour, or food) on six sites, during which I registered feeder choice of 71 tagged individuals. I found that urban chickadees showed less neophobia than their rural counterparts, the latter initially contacting the familiar feeder before approaching the novel feeder, while the former were equally likely to contact any feeder. There was no significant effect of an individual’s dominance, age or sex on its first choice of feeder, nor was there an effect of novelty type. Overall, my results suggest that urban chickadees exhibit less neophobia than their rural counterparts, because they have generally learned to tolerate novelty in their habitat and/or they have adapted to live in an environment that rewards low neophobia.

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great demand of introducing more intelligent anomaly detection methods with distinct characteristics to cope with various needs. To this end, this thesis starts with presenting a thorough review of existing anomaly detection strategies and methods. The advantageous and disadvantageous of the strategies and methods are elaborated. Afterward, four distinctive anomaly detection methods, especially for time series, are proposed in this work aiming at resolving specific needs of anomaly detection under different scenarios, e.g., enhanced accuracy, interpretable results, and self-evolving models. Experiments are presented and analysed to offer a better understanding of the performance of the methods and their distinct features. To be more specific, the abstracts of the key contents in this thesis are listed as follows: 1) Support Vector Data Description (SVDD) is investigated as a primary method to fulfill accurate anomaly detection. The applicability of SVDD over noisy time series datasets is carefully examined and it is demonstrated that relaxing the decision boundary of SVDD always results in better accuracy in network time series anomaly detection. Theoretical analysis of the parameter utilised in the model is also presented to ensure the validity of the relaxation of the decision boundary. 2) To support a clear explanation of the detected time series anomalies, i.e., anomaly interpretation, the periodic pattern of time series data is considered as the contextual information to be integrated into SVDD for anomaly detection. The formulation of SVDD with contextual information maintains multiple discriminants which help in distinguishing the root causes of the anomalies. 3) In an attempt to further analyse a dataset for anomaly detection and interpretation, Convex Hull Data Description (CHDD) is developed for realising one-class classification together with data clustering. CHDD approximates the convex hull of a given dataset with the extreme points which constitute a dictionary of data representatives. According to the dictionary, CHDD is capable of representing and clustering all the normal data instances so that anomaly detection is realised with certain interpretation. 4) Besides better anomaly detection accuracy and interpretability, better solutions for anomaly detection over streaming data with evolving patterns are also researched. Under the framework of Reinforcement Learning (RL), a time series anomaly detector that is consistently trained to cope with the evolving patterns is designed. Due to the fact that the anomaly detector is trained with labeled time series, it avoids the cumbersome work of threshold setting and the uncertain definitions of anomalies in time series anomaly detection tasks.

The ‘synaptic tagging-and-capture’ (STC) theory of cellular memory consolidation holds that memory persistence can be altered by prior or subsequent patterns of neural activity (Redondo & Morris 2011). The aim of this thesis was to develop a realistic model of everyday memory for mice and use the optogenetic toolbox to investigate the neuromodulatory circuitry that modulates persistence of everyday spatial memories. The task involved learning a win-stay rule with the daily goal of finding the location of food in the event arena. Using the developed task, it was confirmed that unrelated novel experiences can facilitate the persistence of spatial memory in a manner sensitive to pharmacological blockade of hippocampal dopamine D1/D5 receptors. Further analysis focused on identifying the specific neuromodulatory systems that mediate this effect. An influential model called the ‘hippocampus- VTA loop’ (Lisman & Grace 2005) points to the critical role of dopaminergic neurons in the ventral tegmental area (VTA), but recent evidence also implicates locus coeruleus (LC) as a potential source of dopamine in the hippocampus (Smith & Greene 2012). In order to identify the dopaminergic structure(s) that may mediate the novelty effect on memory persistence, single unit activity of optogenetically identified catecholaminergic (CAergic) neurons in mouse VTA and LC was recorded in a novelty exploration paradigm. Using tyrosine hydroxylase-Cre knock-in mice and a Cre-dependent adeno-associated viral vectors, CAergic neurons in VTA and LC were selectively tagged with channelrhodopsin-2 (ChR2). Conditional ChR2 expression made it possible to reliably identify CAergic neurons during unit recording sessions in freely moving animals. The main conclusion of the study is that that CAergic neurons in both VTA and LC selectively increase their firing rates in novel environments, relative to both a familiar environment and a home cage baseline. When normalised to their average baseline firing rates, LC neurons are more strongly activated by novelty than VTA neurons. In the final experiment outlined in this thesis, another cohort of Th-Cre mice, in which ChR2 was expressed in CAergic neurons of both VTA and LC using a Cre-dependent adeno-associated virus, was trained on the everyday appetitive spatial memory task. ChR2-mediated photoactivation of CAergic neurons in LC but not in VTA 30 min after encoding, substituting for novelty, was successful in enhancing the persistence of memory. Paradoxically, the effect of optogenetic LC activation was blocked by hippocampal microinfusion of dopamine D1/D5 receptor antagonist but not β-adrenergic receptor antagonist. Results of experiments described in this thesis support the principle of STC theory and collectively indicate that dopamine released from hippocampal terminals of LC neurons mediates the novelty effect on memory persistence. Importantly, they also point to a more general of role of LC in gating of entry to long-term memory.

By means of a peculiar magic, insurance preserves the quantified value of capital through destructive, contingent events. The principal subjects of this project, global reinsurers, stand at the end of a long line of loss claims, holding capital together as forces threaten to tear it apart. The apocalyptic imaginaries of climate change portend events that will be increasingly destructive to capital, and insurers counter with new products and narratives. In examining reinsurers and the catastrophes they protect against, this project questions how novelty emerges from the eternal return of the same. I show how power is inscribed in the landscape, maintained through the ritual of daily reproduction, and protected from looming outliers to build a long inheritance. Using Walter Benjamin's meditations on violence, I then explore the swerves and breaks that might make the world otherwise.

As a new topic in text mining, novelty detection is a natural extension of information retrieval systems, or search engines. Aiming at refining raw search results by filtering out old news and saving only the novel messages, it saves modern people from the nightmare of information overload. One of the difficulties in novelty detection is the inherent ambiguity of language, which is the carrier of information. Among the sources of ambiguity, synonymy proves to be a notable factor. To address this issue, previous studies mainly employed WordNet, a lexical database which can be perceived as a thesaurus. Rather than borrowing a dictionary, we proposed a statistical approach employing Latent Semantic Indexing (LSI) to learn semantic relationship automatically with the help of language resources. To apply LSI which involves matrix factorization, an immediate problem is that the dataset in novelty detection is dynamic and changing constantly. As an imitation of real-world scenario, texts are ranked in chronological order and examined one by one. Each text is only compared with those having appeared earlier, while later ones remain unknown. As a result, the data matrix starts as a one-row vector representing the first report, and has a new row added at the bottom every time we read a new document. Such a changing dataset makes it hard to employ matrix methods directly. Although LSI has long been acknowledged as an effective text mining method when considering semantic structure, it has never been used in novelty detection, nor have other statistical treatments. We tried to change this situation by introducing external text source to build the latent semantic space, onto which the incoming news vectors were projected. We used the Reuters-21578 dataset and the TREC data as sources of latent semantic information. Topics were divided into years and types in order to take the differences between them into account. Results showed that LSI, though very effective in traditional information retrieval tasks, had only a slight improvement to the performances for some data types. The extent of improvement depended on the similarity between news data and external information. A probing into the co-occurrence matrix attributed such a limited performance to the unique features of microblogs. Their short sentence lengths and restricted dictionary made it very hard to recover and exploit latent semantic information via traditional data structure.

With the ever-increasing number of people accessing the Internet and the recent explosion of e-commerce world wide, there are considerable implications for the tourism industry. Tourism suppliers are investing in the Internet via web pages, advertising and e-commerce, but what role does the Internet actually play in tourism? Before more money is placed into this new 'e-economy', it is important to study the effectiveness of the Internet as a marketing tool in tourism. In order to better address the concerns described above, this research accomplishes several tasks. First, the significance of researching Internet use within the tourism context is established. Specifically, theories and concepts from postmodernism, post-industrialism and post-structuralism are drawn upon as they frame this study. Second, this research explores motivation and decision making within tourism and how the Internet is used during stages of travel preparation, planning and activities. Third, this research explores tourist preferences for novelty and familiarity in three dimensions; travel services, social contact and destination choices, and examines how these are associated with Internet use. The general structure of tourism markets in relation to Internet use as well as novelty and familiarity preferences are also discussed. Three case studies are undertaken to examine these matters: winter tourists, summer tourists and cruise tourists. Novelty-seekers were found to be the most frequent group of Internet users, and also were the most likely to consult a wider variety of information sources when making travel-related decisions. Results also indicate that Internet use for travel varies according to seasonality and destination choices rather than primary activity.

With the ever-increasing number of people accessing the Internet and the recent explosion of e-commerce world wide, there are considerable implications for the tourism industry. Tourism suppliers are investing in the Internet via web pages, advertising and e-commerce, but what role does the Internet actually play in tourism? Before more money is placed into this new 'e-economy', it is important to study the effectiveness of the Internet as a marketing tool in tourism. In order to better address the concerns described above, this research accomplishes several tasks. First, the significance of researching Internet use within the tourism context is established. Specifically, theories and concepts from postmodernism, post-industrialism and post-structuralism are drawn upon as they frame this study. Second, this research explores motivation and decision making within tourism and how the Internet is used during stages of travel preparation, planning and activities. Third, this research explores tourist preferences for novelty and familiarity in three dimensions; travel services, social contact and destination choices, and examines how these are associated with Internet use. The general structure of tourism markets in relation to Internet use as well as novelty and familiarity preferences are also discussed. Three case studies are undertaken to examine these matters: winter tourists, summer tourists and cruise tourists. Novelty-seekers were found to be the most frequent group of Internet users, and also were the most likely to consult a wider variety of information sources when making travel-related decisions. Results also indicate that Internet use for travel varies according to seasonality and destination choices rather than primary activity.

As a new topic in text mining, novelty detection is a natural extension of information retrieval systems, or search engines. Aiming at refining raw search results by filtering out old news and saving only the novel messages, it saves modern people from the nightmare of information overload. One of the difficulties in novelty detection is the inherent ambiguity of language, which is the carrier of information. Among the sources of ambiguity, synonymy proves to be a notable factor. To address this issue, previous studies mainly employed WordNet, a lexical database which can be perceived as a thesaurus. Rather than borrowing a dictionary, we proposed a statistical approach employing Latent Semantic Indexing (LSI) to learn semantic relationship automatically with the help of language resources. To apply LSI which involves matrix factorization, an immediate problem is that the dataset in novelty detection is dynamic and changing constantly. As an imitation of real-world scenario, texts are ranked in chronological order and examined one by one. Each text is only compared with those having appeared earlier, while later ones remain unknown. As a result, the data matrix starts as a one-row vector representing the first report, and has a new row added at the bottom every time we read a new document. Such a changing dataset makes it hard to employ matrix methods directly. Although LSI has long been acknowledged as an effective text mining method when considering semantic structure, it has never been used in novelty detection, nor have other statistical treatments. We tried to change this situation by introducing external text source to build the latent semantic space, onto which the incoming news vectors were projected. We used the Reuters-21578 dataset and the TREC data as sources of latent semantic information. Topics were divided into years and types in order to take the differences between them into account. Results showed that LSI, though very effective in traditional information retrieval tasks, had only a slight improvement to the performances for some data types. The extent of improvement depended on the similarity between news data and external information. A probing into the co-occurrence matrix attributed such a limited performance to the unique features of microblogs. Their short sentence lengths and restricted dictionary made it very hard to recover and exploit latent semantic information via traditional data structure.