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Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and how they support inductive inferences about unobserved relations. We describe two experiments that test our approach, and show that it provides a better account of human learning and reasoning than an approach developed by Goodman [1]. What is a theory, and what makes one theory better than another? Questions like these are of obvious interest to philosophers of science but are also discussed by psychologists, who have argued that everyday knowledge is organized into rich and complex systems that are similar in many respects to scientific theories. Even young children, for instance, have systematic beliefs about domains including folk physics, folk biology, and folk psychology [2]. Intuitive theories like these play many of the same roles as scientific theories: in particular, both kinds of theories are used to explain and

Everyday knowledge about living things, physical objects and the beliefs and desires of other people appears to be organized into sophisticated systems that are often called intuitive theories. Two long term goals for psychological research are to understand how these theories are mentally represented and how they are acquired. We argue that the language of thought hypothesis can help to address both questions. First, compositional languages can capture the content of intuitive theories. Second, any compositional language will generate an account of theory learning which predicts that theories with short descriptions tend to be preferred. We describe a computational framework that captures both ideas, and compare its predictions to behavioral data from a simple theory learning task. Any comprehensive account of human knowledge must acknowledge

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g., 1987), it is argued that some universal principles may be attainable in cognitive science. Here we propose two examples: The simplicity principle (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale-invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. We illustrate how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath & Chater, in press), and briefly mention some extensions to models of identification and categorization. We also consider the scope and limitations of universal laws in cognitive science.

Towards a rational theory of human information acquisition

In this paper, we will try to outline a few major questions that the design of artificial cognitive systems should tackle. The argumentation will be led from a philosophical point of view, reviewing some of the latest epistemological positions, namely from the 20th century. The notion of the thing-in-itself, i.e. the substantial core of an object, will be central in the discussion of abstraction in cognitive systems. A second important point is intentionality (in the philosophical sense): Doing something is always guided or biased by a task that is currently pursued. Last but not least the old question of symbol anchoring is to be posed. A preliminary system design for abstracting knowledge is shown that forms a possible implementation for tackling these aspects for the domain of robotic vision. 1. Why start from philosophy? It has become obvious in the past years that the study of cognitive systems, in which epigenetic robotics takes an important part, not only profits from but even requires an interdisciplinary approach. Philosophy has an outstanding role in the history of sciences, often termed as mother of all sciences“. Its unique approach can provide researchers with new perspectives and simultaneously retaining the big picture. The usual suspects of disciplines involved in the study of cognitive systems, namely biology/neuroscience, developmental sciences, psychology and artificial intelligence, have evolved from philosophy when sciences started to split into more detailed directions. Nevertheless, their roots are philosophical. Second, at least since the ground-breaking article