Colin Allen

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

Cognitive ethologists are in need of a good theoretical framework for attributing intentional states. Heyes and Dickinson (1990) present criteria that they claim are necessary for an intentional explanation of behavior to be justified. They suggest that questions of intentionality can only be investigated under controlled laboratory conditions and they apply their criteria to laboratory experiments to argue that the common behavior of approaching food is not intentional in most animals. We dispute the details of their argument and interpretation of the laboratory experiments. While criteria such as those suggested have a role to play in comparative studies of cognition, both laboratory and field studies are important for assessing the applicability of intentional explanations across different taxa.

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?