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The experience of pain is something that most people are extremely familiar with. However, once we begin to examine the subject from an ethical point of view, and particularly when we examine so-called marginal cases such as nonhuman animals, we are quickly confronted with difficult questions. This thesis, through an examination of a particular feature of moral language and a description of recent research on pain, provides an analysis of how pain fits into ethical theory. It is argued that universalizability is an important feature of ethical systems and provides a basis for claiming that an agent is acting inconsistently if he or she evaluates similar situations differently. Though the additional features prescriptivity and overridingness provide an important connection between moral judgment and action in Hare’s two-level utilitarianism, it is argued that they ultimately lead to claims incompatible with lived moral experience. Arguments by Parfit and Sidgwick are discussed which tie acting morally to acting consistently, and it is concluded that selfinterest theory is not a tenable position. After the features of moral judgment are discussed, the necessary features of a moral subject are examined. It is concluded that sentience, or the ability to feel pleasure or pain, is a sufficient condition for being a moral subject. Arguments are examined that attempt to show which animals likely consciously experience pain. Difficulties for these arguments are discussed and an original argument is presented that at least partially addresses these difficulties. It is concluded that from an ethical perspective our current practices such as factory farming are probably not justified. It appears especially likely that our treatment of other mammals is unethical, but the answers are not as clear with other animals. However, all of the conclusions are tentative, as no doubt future scientific investigation will shed more light on our knowledge.

Thesis (M.S.)--Villanova University, 2009. / Psychology Dept. Includes bibliographical references.

Recent studies have advanced our knowledge of factors that could affect problem solving performance, and also of the positive effects of problem solving ability on fitness measures (the ‘what’ of problem solving). However, a missing linkage exists between this ‘what’ and the corresponding ‘how’. Such linkage requires the understanding of how these factors contribute to problem solving. Therefore, the central aim of this thesis is to examine this ‘how’. The roles of learning and behavioural flexibility in the context of problem solving are shown across the experiments, primarily with laboratory and free-ranging grey squirrels and to a lesser extent with wild red squirrels. Under a recurring change, laboratory grey squirrels showed a rapid decrease in the number of errors they made per reversal phase in a serial spatial reversal learning task. Such efficiency is achieved by a gradual tactic change, from sequential to integrative tactics, with increased experience. It also involves support from cognitive mechanisms such as attention and inhibitory control. In a puzzle box task, wild grey squirrels showed that they were better problem solvers than the wild red squirrels. However, red squirrels that solved the puzzle box were more efficient than the grey solvers. Detailed analysis of the results showed that learning and flexibility play independent roles in problem solving. Each process is associated with particular traits that to increase efficiency. For grey squirrels, behavioural selectivity (effective behaviours) and persistence increased with increased experience. Flexibility, however, showed minimal positive effect for them, given that it decreased behavioural selectivity. In contrast, flexibility primarily provided a positive effect for red squirrels’ solving efficiency. These results showed that the two species appear to use both similar and different cognitive processes in solving the task. The discussion gathers the results and explores how learning and flexibility, along with other behavioural traits, vary in their contributions to problem solving performance. As learning and flexibility are definitely not limited in problem solving, the discussion also addresses how these two processes might be involved a construct of general intelligence (‘g’) in animals, and how they are relevant to wilder ecological aspects.

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Humans show such strong sensitivity to whether resources are distributed fairly that they sacrifice personal gain to avoid distributional inequity. This inequity aversion plays an important role in guiding human social decision-making and appears to be ubiquitous across human populations. However, we currently do not understand whether or how inequity aversion evolved over the course of human evolution or how it develops in children. / Human Evolutionary Biology

In North America, the grizzly bear (Ursus arcos) is one of many species increasingly threatened by the consequences of human-wildlife conflict, with human-bear encounters on the rise due to increased human activity near or in bear habitat. As a result, a growing number of bears are subjected to management measures such as translocations in which animals are moved to areas with lower risk of human conflict, although these measures are not always successful. Previous research has attempted to understand factors associated with translocation success, but new methods are needed to address the continuous and complex nature of issues related to how animals move and learn about their surroundings as well as how they adapt to novel environments. The objective of my MSc thesis is to develop and employ a novel agent-based computer simulation model to analyze how grizzly bears learn and respond following a translocation event. This modelling effort attempts to capture how bears make decisions based on multiple factors, and represent how grizzly bears interact with their environment and make movement decisions based on learned behaviours. First, an agent-based movement model was developed for female grizzly bears using GPS-location data for bears within a region in west-central Alberta, Canada. The model, which incorporates multi-scale decision-making and machine learning, generated movement patterns similar to those observed in radio-collared females in the study area. Home range sizes and movement metrics produced by the model were consistent with those observed in female grizzly bears in the area. The model was then used to simulate translocation events in which bears with varying “exploration” propensities were translocated to habitats with familiar or novel landscape characteristics. In general, bears translocated to habitats with similar landscape features to their original habitat were more likely to use high-quality habitat than bears moved to areas with very different landscape features. However, while increased exploration led to greater use of high-quality habitat in the long run, exploratory behaviour was found to be mostly detrimental during the first years following a translocation, the period considered critical for translocation success. Model results were found to be scale-dependent with results varying both in time and space, highlighting the need for a multi-scale approach to animal movement studies. The findings presented here also emphasize the need to account for behavioural traits of wildlife and habitat characteristics of the capture and release sites when selecting suitable translocation locations. This work highlights the potential for agent-based modelling as a tool to study animal movement as a continuous and complex process and evaluate conservation policies. / Graduate / 2021-08-24

Traditionally, the concept of cognition has been tied to the brain or the nervous system. Recent work in various noncomputational cognitive sciences has enlarged the category of “cognitive phenomena” to include the organism and its environment, distributed cognition across networks of actors, and basic cellular functions. The meaning, scope, and limits of ‘cognition’ are no longer clear or well-defined. In order to properly delimit the purview of the cognitive sciences, there is a strong need for a clarification of the definition of cognition. This paper will consider the outer bounds of that definition. Not all cognitive behaviors of a given organism are amenable to an analysis at the organismic or organism-environment level. In some cases, emergent cognition in collective biological and human social systems arises that is irreducible to the sum cognitions of their constituent entities. The group and social systems under consideration are more extensive and inclusive than those considered in studies of distributed cognition to date. The implications for this ultimately expand the purview of the cognitive sciences and bring back a renewed relevance for anthropology and introduce sociology on the traditional six-pronged interdisciplinary wheel of the cognitive sciences.