Colin Allen

I argue in opposition to Sam Rakover that the current lack of fully adequate theories of the subjective and qualitative aspects of mind does not justify the adoption of what he calls “methodological dualism” (Rakover, this issue). Scientific understanding of consciousness requires the continuation of attempts to explain it in terms of the neural mechanisms that support it. It would be premature to adopt a methodological stance that could foreclose on the possibility of more reductionistic approaches. The effects of such a stance would be especially pernicious insofar as methodological dualism lacks rigorous methods for testing specific models.

Fish farming is one of the fastest growing sectors of agriculture, attracting considerable attention to the question of whether existing farming regulations and animal welfare laws are adequate to deal with the expanding role of fish in feeding humans. The role of fish as model organisms in scientific research is also expanding -- a majority of research biology departments now keep zebrafish for the purposes of genome biology, and they are used widely used for basic neuroscience research. However, due to their diversity and distance from mammalian biology, fish pose difficult questions for the application of legal and ethical principles of animal welfare. This paper reviews the developing legal and scientific context in which such questions must be answered.

As the above quote clearly highlights, it is the responsibility of researchers and research supervisors to be certain that their research staff and students assistants are very familiar with all of the ethical principles and current standards relevant to the research they are conducting. Indeed, they must take an active role in being certain that their research staff and students complete appropriate training in these ethical principles and standards, and how they apply them to the research context in which they are working. This is especially important in areas in which there may be physical harm such as chronic pain research. During the past decade, there has been a great increase in research of chronic pain, with breakthroughs in better understanding its etiology, assessment, and treatment (1,2). Obviously, much of this research was conducted using humans and animals as subjects. As a consequence, there were a number of ethical issues that investigators have to be cognizant of when conducting their studies. In this chapter, we will discuss such ethical issues in three major areas: (i) laboratory research with human subjects; (ii) laboratory research with animals; and (iii) translating these laboratory research ndings to ‘‘real world’’ applications in the clinical treatment arena

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches

Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent’s selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we will elucidate a process whereby an agent can work through an ethical problem to reach a solution that takes account of ethically relevant factors.

The close kinship between humans, chimpanzees, gorillas, and orangutans is a central theme among participants in the debate about human treatment of the other apes. Empathy is probably the single most important determinant of actual human moral behavior, including the treatment of nonhuman animals. Given the applied nature of questions about the treatment of captive apes, it is entirely appropriate that the close relationship between us should be highlighted. But the role that relatedness should play in ethical theory is less clear. To the extent that legal and regulatory challenges to keeping apes in captivity are likely to be based on principles of theory, it is important to understand what roles evolutionary theory can play in deriving such principles. The development of ethically correct policies for captivity of animals will depend on taking into account both species-specific and individual differences in the ways that individuals perceive and conceptualize the spaces in which they live, and the choices with which they are presented. A fully evolutionary approach to cognition, a cognitive ethology, that is not just limited to the great apes or to primates is the best hope we have for understanding such perceptions and conceptions.

Ethicists have commonly appealed to science to bolster their arguments for elevating the moral status of nonhuman animals. I describe a framework within which I take many ethicists to be making such appeals. I focus on an apparent gap in this framework between those properties of animals that are part of the scientific consensus, and those to which ethicists typically appeal in their arguments. I will describe two different ways of diminishing the appearance of the gap, and argue that both of them present challenges to ethicists seeking a firm scientific basis for their claims about the moral status of animals. I argue that more clarity about the role of appeals to science by applied ethicists leads to questions about the effectiveness of such appeals, and that these questions might best be pursued empirically.

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.

People can be taught to manipulate symbols according to formal mathematical and logical rules. Cognitive scientists have traditionally viewed this capacity—the capacity for symbolic reasoning—as grounded in the ability to internally represent numbers, logical relationships, and mathematical rules in an abstract, amodal fashion. We present an alternative view, portraying symbolic reasoning as a special kind of embodied reasoning in which arithmetic and logical formulae, externally represented as notations, serve as targets for powerful perceptual and sensorimotor systems. Although symbolic reasoning often conforms to abstract mathematical principles, it is typically implemented by perceptual and sensorimotor engagement with concrete environmental structures.

In this paper, we approach the idea of group cognition from the perspective of the “extended mind” thesis, as a special case of the more general claim that systems larger than the individual human, but containing that human, are capable of cognition (Clark, 2008; Clark & Chalmers, 1998). Instead of deliberating about “the mark of the cognitive” (Adams & Aizawa, 2008), our discussion of group cognition is tied to particular cognitive capacities. We review recent studies of group problem-solving and group memory which reveal that specific cognitive capacities that are commonly ascribed to individuals are also aptly ascribed at the level of groups. These case studies show how dense interactions among people within a group lead to both similarity-inducing and differentiating dynamics that affect the group's ability to solve problems. This supports our claim that groups have organization-dependent cognitive capacities that go beyond the simple aggregation of the cognitive capacities of individuals. Group cognition is thus an emergent phenomenon in the sense of Wimsatt (1986). We further argue that anybody who rejects our strategy for showing that cognitive properties can be instantiated at multiple levels in the organizational hierarchy on a priori grounds is a “demergentist,” and thus incurs the burden of proof for explaining why cognitive properties are “stuck” at a certain level of organizational structure. Finally, we show that our analysis of group cognition escapes the “coupling-constitution” charge that has been leveled against the extended mind thesis (Adams & Aizawa, 2008).

The demise of behaviorism has made ethologists more willing to ascribe mental states to animals. However, a methodology that can avoid the charge of excessive anthropomorphism is needed. We describe a series of experiments that could help determine whether the behavior of nonhuman animals towards dead conspecifics is concept mediated. These experiments form the basis of a general point. The behavior of some animals is clearly guided by complex mental processes. The techniques developed by comparative psychologists and behavioral ecologists are able to provide us with the tools to critically evaluate hypotheses concerning the continuity between human minds and animal minds.

A principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. Intermediate goals of artificial morality are directed at building into AI systems sensitivity to the values, ethics, and legality of activities. The development of an effective foundation for the field of artificial morality involves exploring the technological and philosophical issues involved in making computers into explicit moral reasoners. The goal of this paper is to discuss strategies for implementing artificial morality and the differing criteria for success that are appropriate to different strategies.

Few areas of scientific investigation have spawned more alternative approaches than animal behavior: comparative psychology, ethology, behavioral ecology, sociobiology, behavioral endocrinology, behavioral neuroscience, neuroethology, behavioral genetics, cognitive ethology, developmental psychobiology---the list goes on. Add in the behavioral sciences focused on the human animal, and you can continue the list with ethnography, biological anthropology, political science, sociology, psychology (cognitive, social, developmental, evolutionary, etc.), and even that dismal science, economics. Clearly, no reasonable-length chapter can do justice to such a varied collection. We have opted therefore to focus on three of these subdisciplines and to provide a somewhat historical tour of them, mentioning along the way the philosophical points that are of particular interest to us, but allowing the development of these points to be limited only by the imaginations of our readers. For readers seeking a more-traditional historical survey, see Dewsbury (1984a, b) and Burghardt (1985a). Our chosen brief is to write about comparative psychology, ethology, and cognitive ethology, although other approaches, especially neuroscience, will be mentioned where appropriate. These sciences are philosophically significant because they are enmeshed in ancient philosophical questions about the nature of mind and purposeful action and about the differences between humans and other animals. These sciences are also clustered because of their attention to mechanistic explanations of individual animal behavior as opposed to attempting to capture regularities at a population level, such as the game-theoretic strategic models popular among behavioral ecologists.

Daniel Dennett and Stephen Stich have independently, but similarly, argued that the contents of mental states cannot be specified precisely enough for the purposes of scientific prediction and explanation. Dennett takes this to support his view that the proper role for mentalistic terms in science is heuristic. Stich takes it to support his view that cognitive science should be done without reference to mental content at all. I defend a realist understanding of mental content against these attacks by Dennett and Stich. I argue that they both mistake the difficulty of making content ascriptions precise for the impossibility of doing so.

Which nonhuman animals experience conscious pain?1 This question is central to the debate about animal welfare, as well as being of basic interest to scientists and philosophers of mind. Nociception—the capacity to sense noxious stimuli—is one of the most primitive sensory capacities. Neurons functionally specialized for nociception have been described in invertebrates such as the leech Hirudo medicinalis and the marine snail Aplysia californica (Walters 1996). Is all nociception accompanied by conscious pain, even in relatively primitive animals such as Aplysia, or is it the case, as some philosophers continue to maintain, that conscious experiences are the exclu- sive province of human beings? What philosophical and scientific resources are presently available for assessing claims lying between these extremes?