Colin Allen

Intentional Communication and Social Play: How and Why Animals Negotiate and Agree to Play

In these papers we mainly consider how analyses of social play in nonhuman animals (hereafter animals) can inform inquiries about the evolution of cognitive mechanisms. Social play is a good behavioral phenotype on which to concentrate for when animals play they typically perform behavior patterns that are used in other contexts (e.g. predation, aggression, or reproduction). Thus, individuals need to be able to tell one another that they do not want to eat, fight with, or mate with the other individual(s), but rather, they want to play with them. In most species (primarily mammals) in which play has been observed, specific actions have evolved that are used to initiate or to maintain play. Furthermore, sequences of play usually differ from nonplay sequences (within species) and self-handicapping has also been observed, in which, for example, dominant individuals allow themselves to be dominated _only_ in the context of play. In our consideration of how play is initiated and maintained, we discuss issues including the evolution of play, the ecology of play, the sorts of information that are shared during play, what cognitive psychologists who study humans can learn from cognitive ethologists who study other animals, and what play can tell us about the emergence of mind in animals. These essays draw on literature from ethology, psychology, and philosophy.

A Conceptual and Computational Model of Moral Decision Making in Human and Artificial Agents

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 agents 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.

Machine morality: bottom-up and top-down approaches for modelling human moral faculties

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.

From encyclopedia to ontology: toward dynamic representation of the discipline of philosophy

The application of digital humanities techniques to philosophy is changing the way scholars approach the discipline. This paper seeks to open a discussion about the difficulties, methods, opportunities, and dangers of creating and utilizing a formal representation of the discipline of philosophy. We review our current project, the Indiana Philosophy Ontology (InPhO) project, which uses a combination of automated methods and expert feedback to create a dynamic computational ontology for the discipline of philosophy. We argue that our distributed, expert-based approach to modeling the discipline carries substantial practical and philosophical benefits over alternatives. We also discuss challenges facing our project (and any other similar project) as well as the future directions for digital philosophy afforded by formal modeling.

How to reason without words: inference as categorization

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.

From cell-surface receptors to higher learning: A whole world of experience

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 interpretations 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. We argue for a position in which development and learning are tightly assimilated to one another. Not learning and development, but learning as part of development. 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.

Evaluating Dynamic Ontologies

Ontology evaluation poses a number of difficult challenges requiring different evaluation methodologies, particularly for a “dynamic ontology” generated by a combination of automatic and semi-automatic methods. We review evaluation methods that focus solely on syntactic (formal) correctness, on the preservation of semantic structure, or on pragmatic utility. We propose two novel methods for dynamic ontology evaluation and describe the use of these methods for evaluating the different taxonomic representations that are generated at different times or with different amounts of expert feedback. These methods are then applied to the Indiana Philosophy Ontology (InPhO), and used to guide the ontology enrichment process.

Consciousness might matter very much

Peter Carruthers argues that phenomenal consciousness might not matter very much either for the purpose of determining which nonhuman animals are appropriate objects of moral sympathy, or for the purpose of explaining for the similarities in behavior of humans and nonhumans. Carruthers bases these claims on his version of a dispositionalist higher-order thought (DHOT) theory of consciousness which allows that much of human behavior is the result of first-order beliefs that need not be conscious, and that prima facie judgments about the importance of consciousness are due to confabulation. We argue briefly against his claim that ‘the moral landscape can remain unchanged’ even if all or nearly all nonhuman animals are taken to be incapable of conscious experience. We then show how a first-order representational (FOR) theory of consciousness might be defended against Carruthers’ criticisms. Finally, we argue that Carruthers’ appeal to confabulation undercuts his own arguments for an evolutionary explanation for consciousness, posing a greater epiphenomenalist threat to his DHOT theory than he concedes.

Visual Attention in Dogs and the Evolution of Non-Verbal Communication

The common history of Homo sapiens and Canis lupus familiaris dates back to between 11,000 and 32,000 years ago, when some wolves (Canis lupus) started living closely with humans. Although we cannot reach back into the past to measure the relative roles of wolves and humans in the ensuing domestication process, it was perhaps the first involving humans and another animal species. Yet its consequences for both species’ history are not completely understood. One of the puzzling aspects yet to be understood about the human–dog dyad is how dogs so readily engage in communication in the context of a social interactions with humans. To be sensitive to the meaning of human speech and gestures, dogs need to attend to various visual and vocal cues, in order to reconstruct the messages from patterns of human behavior that remain stable over time, while also generalizing to unfamiliar, novel contexts. This chapter will discuss this topic in light of some of the recent findings about dogs’ perceptual capacities for social cues. We describe some of the new technologies that are being used to better describe these perceptual processes, and present the results of a preliminary experiment using a portable eye-tracking system to gather data about dogs’ visual attention in a social interaction with humans, ending with a discussion of the possible cognitive mechanisms underlying dogs’ use of human social cues.

LODE: linking digital humanities content to the web of data

Numerous digital libraries projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data, including JCDL. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of our Linked Open Data Enhancer Lode is to work “out of the box”, enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With Lode we enable non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. Lode links and enhances the local RDF repository without reducing the quality of the data. In particular, we support the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high quality linking candidates using state of the art matching algorithms. We hope that the Lode framework will be useful to digital humanities scholars complementing other digital humanities tools.