Thesis advisor: Michael McDannald / Uncertainty is a pervasive facet of life, and responding appropriately and proportionally to uncertain threats is critical for adaptive behavior. Aversive prediction errors are signals that allow for appropriate fear responses, especially in the face of uncertainty, and provide a critical updating mechanism to adapt to change. Positive prediction errors (+PE) are generated when an actual outcome of an event is worse than the predicted outcome and increase fear upon future encounters with the related predictive cue. Negative prediction errors (-PE) are generated when the predicted outcome is worse than the actual outcome and decrease fear upon future encounters with the related predictive cue. While some regions have been offered as the neural source of positive and negative prediction errors, no causal evidence has been able to identify their sources of generation. The objective of this dissertation was to causally identify the neural basis of aversive prediction error signaling. Using precise neural manipulations paired with a robust behavioral fear discrimination task, I present causal evidence for vlPAG generation of +PEs and for a ventrolateral periaqueductal grey (vlPAG) to medial central amygdala (CeM) pathway to carry out +PE fear updating. Further, I demonstrate that while dorsal raphe serotonergic neurons are not the source of -PE generation, they appear to receive and utilize this signal. Understanding the neural network responsible for aversive prediction error signaling will not only inform understanding of the neurological basis of fear but also may provide insights into disorders, such as PTSD and anxiety disorders, that are characterized by excessive/inappropriate fear responses. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / The past several years has marked one of the most heightened periods of fed cattle basis volatility since the installment of live cattle futures contracts. Understanding basis, the difference between local cash price and the futures contract price, is imperative when making marketing and procurement decisions. In the face of increased volatility, the ability to produce accurate basis expectations is no simple task. The purpose of these analyses was to develop econometric models to determine the greatest influencers of fed cattle basis, to test the presence of structural changes in the determinants of fed cattle basis, and to compare out-of-sample forecasting performance. This study analyzed in-sample econometric models using monthly data from January 2003 through September 2016, then compared the results of the competing models. Using the same time period, we then identified the presence of structural breaks in the data. Furthermore, this study analyzed the out-of-sample forecasting performance for January 2012 through September 2016. The out-of-sample results were then compared to in-sample estimations and historical average basis models. The in-sample estimations indicated the important factors that influence fed cattle basis. The results indicate that there are multiple structural breaks present in the determinants of fed cattle basis examined during this study. We can robustly conclude that there was a market structural break present in the fourth quarter of 2013 and within the 2005-2006 time period. The results indicate that the out-of-sample regression estimations were outperformed by historical average models and did not improve our ability to accurately forecast basis. Overall, a 3 or 4 year historical average model should be preferred over econometric estimations when forecasting fed cattle basis.

Cocaine use has contributed to 5 million individuals falling into the cycle of addiction. Prior research in cocaine dependence mainly focused on rewards. Losses also play a critical role in cocaine dependence as dependent individuals fail to avoid social, health, and economic losses even when they acknowledge them. However, dependent individuals are extremely adept at escaping negative states like withdrawal. To further understand whether cocaine use may contribute to dysfunctions in aversive learning, this paper uses fMRI and an aversive learning task to examine cocaine dependent individuals abstinent from cocaine use (C-) and using as usual (C+). Specifically of interest is the neural signal representing actual loss compared to the expected loss, better known as prediction error (δ), which individuals use to update future expectations. When abstinent (C-), dependent individuals exhibited higher positive prediction error (δ+) signal in their striatum than when they were using as usual. Furthermore, their striatal δ+ signal enhancements from drug abstinence were predicted by higher positive learning rate (α+) enhancements. However, no relationships were found between drug abstinence enhancements to negative learning rates (α±-) and negative prediction error (δ-) striatal signals. Abstinent (C-) individuals' striatal δ+ signal was predicted by longer drug use history, signifying possible relief learning adaptations with time. Lastly, craving measures, especially the desire to use cocaine and positive effects of cocaine, also positively correlated with C- individuals' striatal δ+ signal. This suggests possible relief learning adaptations in response to higher craving and withdrawal symptoms. Taken together, enhanced striatal δ+ signal when abstinent and adaptations in relief learning provide evidence in supporting dependent individuals' lack of aversive learning ability while using as usual and enhanced relief learning ability for the purpose of avoiding negative situations such as withdrawal, suggesting a neurocomputational mechanism that pushes the dependent individual to maintains dependence. / Master of Science

The reward positivity is a component of the human event-related brain potential (ERP) elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error that is differentially sensitive to the valence of the outcomes, namely, larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments (a shock to the finger) also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). Accordingly, these investigators argued that this ERP component reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, I adapted the task paradigm by Talmi and colleagues to a more standard guessing task often used to investigate the reward positivity. Participants navigated a virtual T-maze and received feedback on each trial under two conditions. In a reward condition the feedback indicated that they would either receive a monetary reward or not for their performance on that trial. In a punishment condition the feedback indicated that they would receive a small shock or not at the end of the trial. I found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. / Graduate / 0633 / 0317 / heydari@uvic.ca

This thesis focuses on probabilistic associative learning. One of the classic effects in this field is the stimulus associability effect for which I derive a statistically optimal inference model and a corresponding approximation that addresses a number of problems with the original account of Mackintosh. My proposed account of associability - a variable learning rate depending on a relative informativeness of stimuli - also accounts of the classic blocking effect \cite{kamin1969predictability} without the need for Prediction Error [PE] computation. Given that blocking was the main impetus for placing PE at the centre of learning theories, I critically re-evaluate other evidence for PE in learning, particularly the recent neuroimaging evidence. I conclude that the brain data are not as clear cut as often presumed. The main shortcoming of the evidence implicating PE in learning is that probabilistic associative learning is mostly described as a transition from one state of belief to another, yet those beliefs are typically observed only after multiple learning episodes and in a very coarse manner. To address this problem, I develop an experimental paradigm and accompanying statistical methods that allow one to infer the beliefs at any given point in time. However, even with the rich data provided by this new paradigm, the blocking effect still cannot provide conclusive evidence for the role of PE in learning. I solve this problem by deriving a novel conceptualisation of learning as a flow in probability space. This allows me to derive two novel effects that can unambiguously distinguish learning that is driven by PE from learning not driven by PE. I call these effectsgeneralized blocking and false blocking, given their inspiration by the original paradigm of Kamin (1969). These two effects can be generalized to the entirety of probability space, rather than just the two specific points provided by the paradigms used by Mackintosh and Kamin, and therefore offer greater sensitivity to differences in learning mechanisms. In particular, I demonstrate that these effects are necessary consequences of PE-driven learning, but not learning based on the relative informativeness of stimuli. Lastly I develop an online experiment to acquire data on the new paradigm from a large number (approximately 2000) of participants recruited via social media. The results of model fitting, together with statistical tests of generalized blocking and false blocking, provide strong evidence against a PE-driven account of learning, instead favouring the relative informativeness account derived at the start of the thesis.

Reversible watermarking techniques extract the watermark and recover the original image losslessly from the watermarked image. They have been applied to those sensitive fields, such as the medicine and the military. Since an embedding pixel value may exceed the limitation of pixel value during the embedding process, most of the reversible watermarking methods require a location-map to record those pixels for recovering cover images. Although the location-map can be compressed by a lossless compression algorithm, and then embed into the watermarked image, this lowers embedding capacity and increases the complexity of watermarking during the procedures of embedding and extraction. In this thesis, we propose a reversible location-map free of watermarking algorithm. This algorithm first exploits the sorting and the correlation of neighboring pixels to increase the embedding capacity. Next we find thresholds from the predicted values. If the predicted value of an embedding pixel is within the thresholds, we can ensure that the pixel has no underflow or overflow problem during embedding process. Therefore, we can recover the cover image without any distortion. The experimental results reveal that the performance of our proposed method outperforms that proposed by FUJIYOSHI et al. For example, the embedding capacity obtained by the proposed method is higher than that obtained by FUJIYOSHI et al. about 60%, and the PSNR of our scheme is higher than FUJIYOSHI et al. about 5 dB.

The present thesis examined the behavioural and brain mechanisms of associative change in the rat during Pavlovian fear conditioning as measured by freezing. The first series of experiments (Chapter 3) used compound test designs to study how learning is distributed among excitatory and neutral conditional stimuli (CSs). More was learned about a neutral CSB than an excitatory CSA when trained in isolation, indicating that fear learning is negatively accelerated. CSA blocked fear learning to CSB when trained in compound. Unblocking of CSB occurred if the AB compound signalled an increase in unconditional stimulus (US) intensity or number. Assessments of associative change during blocking showed that more was learned about CSB than CSA. Such assessments during unblocking revealed that more was learned about CSB than CSA following an increase in US intensity but not US number. These US manipulations had no differential effects on single-cue learning. The results show that variations in US intensity or number produce unblocking of fear learning, but for each there is a different profile of associative change and a potentially different mechanism. The second series of experiments (Chapter 4) demonstrated that these stimulus selection effects are mediated, at least in part, by nucleus accumbens shell (AcbSh). AcbSh lesions augmented overshadowing during compound conditioning and promoted learning about CSA at the expense of CSB during blocking designs. Lesioned rats could learn normally about the novel CSB if it was rendered more informative regarding shock in Stage II. These results identify an important role for AcbSh and ventral striatum in distributing attention and learning among competing predictors of danger.

A Linear mixed-effects model (LME) is one of the possible tools for longitudinal or group--dependent data. This thesis deals with evaluating of prediction error in LME. Firstly, it is derived the mean square error of prediction (MSEP) by direct calculation. Then the covariance penalty method and crossvalidation is presented for evaluation of MSEP in LME. Further, it is shown how Akaike information criterion (AIC) can be used in mixed-effects models. Because of the model's properties two types of AIC are distinguished - marginal and conditional one. Subsequently, the procedures of AIC's calculation and its basic asymptotic properties are described. Finally, the thesis contains simulation study of behaviour of marginal and conditional AIC with the goal to choose the right variance structure of random effects. It turns out that the marginal criterion tends to select models with smaller number of random effects than conditional criterion.

Chronic nicotine use has been linked to increased sensitivity to nondrug rewards as well as improvement in mood among individuals with depression, and these effects have been hypothesized to be mediated through alternations in striatal dopamine activity. Similarly, chronic nicotine use is hypothesized to influence the mechanisms by which healthy and depressed individuals learn about rewards in their environment. However, the specific behavioral and neural mechanisms by which nicotine influences the learning process is poorly understood. Here, we use a probabilistic learning task, functional magnetic resonance imaging and neurocomputational analyses, to show that chronic smoking is associated with higher reward sensitivity, along with lower learning rate and striatal prediction error signal. Further, we show that these effects do not differ between individuals with and without major depressive disorder (MDD). In addition, a negative correlation between reward sensitivity and striatal prediction error signal was found among smokers, consistent with the suggestion that enhanced tonic dopamine associated with increased reward sensitivity leads to an attenuation of phasic dopamine activity necessary for updating of reward value during learning. / Master of Science

The Generalized Unsafety Theory of Stress proposes that physiological markers of generalized stress impair learning of safe cues in stressful environments. Based on this model, chronic problems inhibiting physiological arousal lead to a heightened perception of threat, which involves experiencing anxiety symptoms without any obvious precipitating stressful or traumatic event. This investigation aims to determine the impact of stressor- versus context-related emotional learning on generalized unsafety, using a Pavlovian threat-conditioning paradigm. The difference in learning threatening cues ([CS+] paired with an aversive stimulus) compared to safety cues ([CS-] not paired with an aversive stimulus) was used as a proxy measure of generalized unsafety, as conceptualized by the GUTS model. This difference is expected to be moderated by individual differences in tonic cardiac regulation (i.e. heart rate variability). Lastly, a temporal-differences learning model was used to predict skin-conductance learning during stressor, stressor context and general contexts to determine which best predicts Pavlovian learning. TD learning is expected to better predict skin-conductance in individuals with higher fear inhibition in comparison to those with low fear inhibition. / Doctor of Philosophy / This study examined the claims of a theory about how human bodies respond to stress and what this tells us about how anxiety develops in and affects the mind and body. The theory is named the Generalized Unsafety Theory of Stress (GUTS) and two main hypotheses were tested in this study: 1) the theory suggests that a person's feeling of safety is affected by the variation in their heart rate at rest, and 2) that a person's feeling of safety could be observed most accurately by their body's defense responses when they are experiencing a threatening situation that is objectively safe. Individuals experiencing anxiety often report being aware that they are safe, yet their heart rate remains elevated and palms remain sweaty. Most studies that have examined the body's defense response have focused almost solely on reactions to a threat by looking at the reactions of one or more organs that make up the body's defense-response systems (e.g., heart). Results of this study confirmed the unique GUTS perspective. Specifically, the heart rate's variation at rest affects the defense response (sweaty hands) during threatening and objectively safe contexts, which in turn, predicts a person's feeling of safety. These results confirm that there are measurable biological constraints that change the way people learn about and react to their environments, which is very important for understanding the development and maintenance of anxiety physiology and behavior. The way a person learns to associate emotional responses to certain cues in their environment, particularly threat and safety cues, can be measured as defense responses in the body in response to a series of trials. Exploratory analyses examined human threat learning in comparison with mathematically-generated learning in order to better model the processes whereby anxiety develops based on learning of threat and safety cues.