Colin Allen

  Many psychologists and philosophers believe that the close correlation between human language and human concepts makes the attribution of concepts to nonhuman animals highly questionable. I argue for a three-part approach to attributing concepts to animals. The approach goes beyond the usual discrimination tests by seeking evidence for self-monitoring of discrimination errors. Such evidence can be collected without relying on language and, I argue, the capacity for error-detection can only be explained by attributing a kind of internal representation that is reasonably identified as a concept. Thus I hope to have shown that worries about the empirical intractability of concepts in languageless animals are misplaced

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

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

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.

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.

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.

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).

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.