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Psychology
Cognitive Psychology

An ontology for comparative cognition: A functional approach

Profile image of Michael FerkinMichael FerkinProfile image of Stan FranklinStan Franklin

2006, Comparative Cognition & Behavior Reviews

Abstract
sparkles

AI

Comparative cognition investigates the cognitive processes of non-human animals, but the field faces challenges in conceptual clarity and terminology consistency. This paper proposes a comprehensive ontology to define key concepts and their relationships in comparative cognition, aiming to standardize terminology and facilitate communication among researchers. By introducing a trial framework for concepts such as perception, memory, and consciousness, the document seeks to enhance the understanding of animal cognition and stimulate further discussion and research in the field.

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Animal ecologists have successfully applied agent-based models to many different problems. Often, these focus on issues concerning collective behaviors, environmental interactions, or the evolution of traits. In these cases, patterns of interest can usually be investigated by constructing the appropriate multiagent system, and then varying or evolving model parameters. In recent years, however, the study of animal behavior has increasingly expanded to include the study of animal cognition. In this field, the question is not just how or why a particular behavior is performed, but also what its 'mental underpinnings' are. In this paper, we argue that agent-based models are uniquely suited to explore questions concerning animal cognition, as the experimenter has direct access to agents' internal representations, control over their evolutionary history, and a perfect record of their previous learning experience. To make this possible, a new modeling paradigm must be developed, where agents' reasoning processes are explicitly simulated, and can evolve over time. We propose that this be done in the form of "if-then" rules, where only the form is specified, not the content. This should allow qualitatively different reasoning processes to emerge, which may be more or less "cognitive" in nature. In this paper, we illustrate the potential of such an approach with a prototype model. Agents must evolve explicit rule sets to forage for food, and to escape predators. It is shown that even in this relatively simple setup, different strategies emerge, as well as unexpected outcomes.

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Animal cognition
rosa rugani

The main topics in the study of animal cognition are reviewed with special reference to direct links to human, and in particular developmental, cognitive sciences. The material is organized with regard to the general idea that biological organisms would be endowed with a small set of separable systems of core knowledge, a prominent hypothesis in the current developmental cognitive sciences. Core knowledge systems would serve to represent inanimate physical objects and their mechanical interactions (natural physics); numbers with their relationships of ordering, addition, and subtraction (natural mathematics); places in the spatial layout with their geometric relationships (natural geometry); and animate psychological objects (agents) with their goal-directed actions (natural psychology). Some advanced forms of animal cognition, such as episodic-like representations and planning for the future, are also discussed.

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Learning and decision-making in artificial animals
Claes Strannegård

Journal of artificial general intelligence, 2018

A computational model for artificial animals (animats) interacting with real or artificial ecosystems is presented. All animats use the same mechanisms for learning and decisionmaking. Each animat has its own set of needs and its own memory structure that undergoes continuous development and constitutes the basis for decision-making. The decision-making mechanism aims at keeping the needs of the animat as satisfied as possible for as long as possible. Reward and punishment are defined in terms of changes to the level of need satisfaction. The learning mechanisms are driven by prediction error relating to reward and punishment and are of two kinds: multi-objective local Q-learning and structural learning that alter the architecture of the memory structures by adding and removing nodes. The animat model has the following key properties: (1) autonomy: it operates in a fully automatic fashion, without any need for interaction with human engineers. In particular, it does not depend on human engineers to provide goals, tasks, or seed knowledge. Still, it can operate either with or without human interaction; (2) generality: it uses the same learning and decision-making mechanisms in all environments, e.g. desert environments and forest environments and for all animats, e.g. frog animats and bee animats; and (3) adequacy: it is able to learn basic forms of animal skills such as eating, drinking, locomotion, and navigation. Eight experiments are presented. The results obtained indicate that (i) dynamic memory structures are strictly more powerful than static; (ii) it is possible to use a fixed generic design to model basic cognitive processes of a wide range of animals and environments; and (iii) the animat framework enables a uniform and gradual approach to AGI, by successively taking on more challenging problems in the form of broader and more complex classes of environments.

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Cognition in animals: Learning as program assembly
John Staddon

Cognition, 1981

Learning most tasks has two components. First, an approximate solution must be found: the student laboriously solves the first algebra problem, the rat stumbles on the lever. Second, after an intervening period when the organism does something else, a residue must remain of what occurred on first exposure so that there are savings, i.e., the organism does a bit better the seccmd time than it did the first time. Learning is compounded of these two effects: initial often ill-directed stabs at solving the problem, which will be repeated in reduced form on subsequent exposures, plus the retention from occasion to occasion of an accumulating core of reliable knowledge. The two major areas of animal learning are divided along these general lines. Operant conditioning, to the extent that it is concerned with learning at all, is interested in the initial "shaping" of behavior by the condition: ,f reinforcement. There is much less interest in how the changes wrought in one experimental session carry over to the next. Classical conditioning, on the other hand, is primarily concerned with the accumulation of "strength" by a conditioned stinmlus from one experimental session to the next. Although classical and operant conditioners disagree on many points they agree in one respect: on the importance of stimuli and responses. Both lines ofwork have their theoretical roots in the refle.u-not the reflex of Sherrington a subtle concept subordinate to his main theme of integration, but a simpler idea used as a metaphor for cause. In recent years, dissatisfaction with this primitive kind of theory has led to the appearance of "cognitive" views. These take several forms: updated versions of Tolmanian cognitive maps (e.g., Menzel, 1978), experimental exploration of the perceptual categories of animals (e.g., Herrnstein and de Villiers, 1980), more or 1~s~ explicit informationprocessing accounts (e.g.,Wagner, 1978),through accounts of animal behavior as a rational process paralleiing human verbal reasoning (e.g., Seligman and Johnston, 1973). Any account involving the word "memory" or referrip:-to constructs at all'removed from measurable stimuli and responses tends also to label itself "cognitive" (cf., several chapters in the book edited by Hulse, Fowler and Honig, 1978. *I thank Richard Herrnstein, Stewart Hulse and Evalyn Segal for comments on an earlier version of this piece. Research supported by grants from the National Science Foundation to Duke University. Reprint requests should be sent to J.

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Related topics

  • Animal Cognition
  • Software Agent
  • Distributed Agents
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