Colin Allen

Seven chimpanzees in twenty-seven experiments run over the course of five years at his University of Louisiana laboratory in New Iberia, Louisiana, are at the heart of Daniel Povinelli’s case that chimpanzee thinking about the physical world is not at all like that of humans. Chimps, according to Povinelli and his coauthors James Reaux, Laura Theall, and Steve Giambrone, are phenomenally quick at learning to associate visible features of tools with specific uses of those tools, but they appear to lack cognitive access to forces and other invisible causal features of those tools. Povinelli’s chimps appar- ently rely on a trial-and-error strategy to learn whether a particular tool is suitable for a particular task, and and having mastered one task they appear unable to generalize to other tasks on the basis of tool properties that are not directly visible. Thus, for instance, Povinelli’s research subjects did not immediately recognize that a tool that had been demonstrated to be non- rigid would be unsuitable for dragging a piece of food towards them. When presented with a choice between a rigid, T-shaped “rake” that they had used many times previously and a rake with non-rigid arms, Povinelli and Reaux found, over the course of eight trials, that their chimps chose the non-rigid rake as frequently as they chose the rigid one (experiment 9, chapter 7).

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.

It is a truism among ethologists that one must not forget that animals perceive and represent the world differently from humans. Sometimes this caution is phrased in terms of von Uexküll’s Umwelt concept. Yet it seems possible (perhaps even unavoidable) to adopt a common ontological framework when comparing different species of mind. For some purposes it seems sufficient to ­anchor comparative cognition in common-sense categories; bats echolocate insects (or a subset of them) after all. But for other purposes it seems necessary to find out more about how organisms organize their perceptions into biologically significant and perhaps cognitively meaningful states. Complex animals have high bandwidth sensory channels that feed into large nerve networks with very complex dynamics. Even for relatively simple animals belonging to species believed to have a small, fixed number of neurons, the odds are very much against any two animals of the same species, let alone different species, having exactly the same couplings to the environment, the same dimensionality in their nervous systems, or the same dynamics. Given such diversity (which von Uexküll himself recognized), how should we think about shared representation, shared meaning, and cognitive similarity between individuals and species?

Our goal in this paper is to provide enough of an account of the origins of cognitive ethology and the controversy surrounding it to help ethicists to gauge for themselves how to balance skepticism and credulity about animal minds when communicating with scientists. We believe that ethicists’ arguments would benefit from better understanding of the historical roots of ongoing controversies. It is not appropriate to treat some widely reported results in animal cognition as if their interpretations are a matter of scientific consensus. It is especially important to understand why loose references to “cognitive ethology” by philosophers can signal ignorance of the field to scientists who are more deeply immersed in the relevant literature. Understanding the variety of approaches to cognitive phenomena in animals is essential if such capacities are to form the foundation of scientifically-informed ethical reasoning about animals.

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 intentionality, 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.

In this paper we1 assess the potential for research on nonhuman animals to address questions about the phenomenology of painful experiences. Nociception, the basic capacity for sensing noxious stimuli, is widespread in the animal kingdom. Even rel- atively primitive animals such as leeches and sea slugs possess nociceptors, neurons that are functionally specialized for sensing noxious stimuli (Walters 1996). Vertebrate spinal cords play a sophisticated role in processing and modulating nociceptive signals, providing direct control of some motor responses to noxious stimuli, and a basic capacity for Pavlovian and instrumental conditioning (Grau et al. 1990; Grau 2002). Higher brain systems provide additional layers of association, top-down control, and cognition. In humans, at least, these higher brain systems also give rise to the conscious experiences that are characteristic of pain. What can be said about the experiences of other animals who possess nervous systems that are similar but not identical to humans?

Wittgenstein famously ended his Tractatus Logico-Philosophicus (Wittgenstein 1922) by writing: "Whereof one cannot speak, one must pass over in silence." (Wovon man nicht sprechen kann, darüber muss man schweigen.) In that earliest work, Wittgenstein gives no clue about whether this aphorism applied to animal minds, or whether he would have included philosophical discussions about animal minds as among those displaying "the most fundamental confusions (of which the whole of philosophy is full)" (1922, TLP 3.324), but given his later writings on language and thought, it seems a plausible hypothesis. Years later he wrote in the Philosophical Investigations (1968, p. 223) another aphoristic statement: "If a lion could speak, we would not understand him." (Wenn der Löwe sprechen könnte, wir könnten ihn nicht verstehen.) Decades of philosophical discussion about what Wittgenstein meant by these remarks illustrate another point: when philosophers speak, we do not fully understand them.

Discussions of the functions of biological traits generally take the notion of a trait for granted. Defining this notion is a non-trivial problem. Different approaches to function place different constraints on adequate accounts of the notion of a trait. Accounts of function based on engineering-style analyses allow trait boundaries to be a matter of human interest. Accounts of function based on natural selection have typically been taken to require trait boundaries that are objectively real. After canvassing problems raised by each approach, I conclude with some facts that satisfactory notions of trait must respect.

In the last decade it has become en vogue for cognitive comparative psychologists to study animal behavior in an ‘integrated’ fashion to account for both the ‘innate’ and the ‘acquired’. We will argue that these studies, instead of really integrating the concepts of ‘nature’ and ‘nurture’, rather cement this old dichotomy. They combine empty nativist interpretation of behavior systems with blatantly environmentalist explanations of learning. We identify the main culprit as the failure to take development seriously. While in some areas of biology interest in the relationship between behavior and development has surged through topics such as extragenetic inheritance, niche construction, and phenotypic plasticity, this has gone almost completely unnoticed in the study of animal behavior in comparative psychology, and is frequently ignored in ethology too. The main aims of this paper are to clarify the relationship between the concepts of learning, experience, and development, and to investigate whether and how all three concepts can be usefully deployed in the study of animal behavior. This will require the full integration of the psychological study of behavior into biology, and of the idea of learning into a wider concept of experience. We lay out how, in a systems view of development, learning may just appear as one among many processes in which experience influences behavior. This new synthesis should help to overcome the age-old dualism between innate and acquired. It thereby opens up the possibility of developing scientifically more fruitful distinctions.

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

The Indiana Philosophy Ontology (InPhO) project is presented as one of the first social-semantic web endeavors which aims to bootstrap feedback from users unskilled in ontology design into a precise representation of a specific domain. Our approach combines statistical text processing methods with expert feedback and logic programming approaches to create a dynamic semantic representation of the discipline of philosophy. We describe the basic principles and initial experimental results of our system.

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