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 I argue that selection of the best theory of content is not a matter for mere philosophical reflection on the consequences of each theory for our intuitive judgments about content. Rather, the theories must be judged in a different way that is based on the putative roles of content attribution in the behavioural sciences. The ultimate test of any theory of content will be the success of the sciences that adopt it. Furthermore, alternative semantic theories may be seen as complementary rather than antagonistic. Different approaches to content specification may well be compatible, reflecting different but perhaps equally legitimate explanatory goals.

Teleological terms such as "function" and "design" appear frequently in the biological sciences. Examples of teleological claims include: A (biological) function of stotting by antelopes is to communicate to predators that they have been detected. Eagles' wings are (naturally) designed for soaring. Teleological notions were commonly associated with the pre-Darwinian view that the biological realm provides evidence of conscious design by a supernatural creator. Even after creationist viewpoints were rejected by most biologists there remained various grounds for concern about the role of teleology in biology, including whether such terms are: 1. vitalistic (positing some special "life-force"); 2. requiring backwards causation (because future outcomes explain present traits); 3. incompatible with mechanistic explanation (because of 1 and 2); 4. mentalistic (attributing the action of mind where there is none); 5. empirically untestable (for all the above reasons). Opinions divide over whether Darwin's theory of evolution provides a means of eliminating teleology from biology, or whether it provides a naturalistic account of the role of teleological notions in the science. Many contemporary biologists and philosophers of biology believe that teleological notions are a distinctive and ineliminable feature of biological explanations but that it is possible to provide a naturalistic account of their role that avoids the concerns above. Terminological issues sometimes serve to obscure some widely-accepted distinctions.

Psychology, according to a standard dictionary definition, is the science of mind and behavior. For a major part of the twentieth century, (nonhuman) animal psychology was on a behavioristic track that explicitly denied the possibility of a science of animal mind. While many comparative psychologists remain wedded to behavioristic methods, they have more recently adopted a cognitive, information-processing approach that does not adhere to the strictures of stimulus-response explanations of animal behavior. Cognitive ethologists are typically willing to go much further than comparative psychologists by adopting folk-psychological terms to explain the behavior of nonhuman animals. This different attitudes of many scientists presupposes a distinction between cognitive and mental state attributions that is not commonly articulated. This paper seeks to understand that distinction.

Primatologists generally agree that monkeys lack higher-order intentional capacities related to theory of mind. Yet the discovery of the so-called "mirror neurons" in monkeys suggests to many neuroscientists that they have the rudiments of intentional understanding. Given a standard philosophical view about intentional understanding, which requires higher-order intentionahty, a paradox arises. Different ways of resolving the paradox are assessed, using evidence from neural, cognitive, and behavioral studies of humans and monkeys. A decisive resolution to the paradox requires substantial additional empirical work and perhaps a rejection of the standard philosophical view.

Social play is naturally characterized in intentional terms. An evolutionary account of social play could help scientists to understand the evolution of cognition and intentionality. Alexander Rosenberg (1990) has argued that if play is characterized intentionally or functionally, it is not a behavioral phenotype suitable for evolutionary explanation. If he is right, his arguments would threaten many projects in cognitive ethology. We argue that Rosenberg's arguments are unsound and that intentionally and functionally characterized phenotypes are a proper domain for ethological investigation.

The idea that reasoning is a singular accomplishment of the human species has an ancient pedigree.Yet this idea remains as controversial as it is ancient. Those who would deny reasoning to nonhuman animals typically hold a language-based conception of inference which places it beyond the reach of languageless creatures. Others reject such an anthropocentric conception of reasoning on the basis of similar performance by humans and animals in some reasoning tasks, such as transitive inference. Here, building on the modal similarity theory of Vigo [J Exp Theor Artif Intell, 2008 (in press)], we offer an account in which reasoning depends on a core suite of subsymbolic processes for similarity assessment, discrimination, and categorization. We argue that premise-based inference operates through these subsymbolic processes, even in humans. Given the robust discrimination and categorization abilities of some species of nonhuman animals, we believe that they should also be regarded as capable of simple forms of inference. Finally, we explain how this account of reasoning applies to the kinds of transitive inferences that many nonhuman animals display

In this paper we argue that there is much to learn about “wild justice” and the evolutionary origins of morality – behaving fairly – by studying social play behavior in group-living mammals. Because of its relatively wide distribution among the mammals, ethological investigation of play, informed by interdisciplinary cooperation, can provide a comparative perspective on the evolution of ethical behavior that is broader than is provided by the usual focus on primate sociality. Careful analysis of social play reveals rules of engagement that guide animals in their social encounters. Because of its significance in development, play may provide a foundation of fairness for other forms of cooperation that are advantageous to group living. Questions about the evolutionary roots of cooperation, fairness, trust, forgiveness, and morality are best answered by attention to the details of what animals do when they engage in social play – how they negotiate agreements to cooperate, to forgive, to behave fairly, and to develop trust. We consider questions such as why play fairly? Why did play evolve as it has? Does “being fair” mean being more fit? Do individual variations in play influence an individual’s reproductive fitness? Can we use information about the foundations of moral behavior in animals to help us understand ourselves? We conclude that there is likely to be strong selection for cooperative fair play because there are mutual benefits when individuals adopt this strategy and group stability may also be fostered. Numerous mechanisms have evolved to facilitate the initiation and maintenance of social play, to keep others engaged, so that agreeing to play fairly and the resulting benefits of doing so can be readily achieved.

If fast and frugal heuristics are as good as they seem to be, who needs logic and probability theory? Fast and frugal heuristics depend for their success on reliable structure in the environment. In passive environments, there is relatively little change in structure as a consequence of individual choices. But in social interactions with competing agents, the environment may be structured by agents capable of exploiting logical and probabilistic weaknesses in competitors' heuristics. Aspirations toward the ideal of a demon reasoner may consequently be adaptive for direct competition with such agents.

As arti® cial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an arti® cial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing arti® cial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a `moral Turing Test ’ is considered and the computational di culties accompanying the diŒerent types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people and other sentient beings are great. The development of machines with enough intelligence to assess the eŒects of their actions on sentient beings and act accordingly may ultimately be the most important task faced by the designers of arti® cially intelligent automata.

Cognitive ethology is the comparative study of animal cognition from an evolutionary perspective. As a sub-discipline of biology it shares interest in questions concerning the immediate causes and development of behavior. As a part of ethology it is also concerned with questions about the function and evolution of behavior. I examine some recent work in cognitive ethology, and I argue that the notions of mental content and representation are important to enable researchers to answer questions and state generalizations about the function and volution of behavior.