Advanced perceptual systems are faced with the problem of securing a principled relationship between the world and its internal representation. I propose a unified approach to visual representation, based on Shepard's (1968) notion of second-order isomorphism. According to the proposed theory, a shape is represented internally by the responses of a few tuned modules, each of which is broadly selective for some reference shape, whose similarity to the stimulus it measures. The result is a philosophically appealing, computationally feasible, biologically credible, and formally veridical representation of a distal shape space. This approach supports representation of and discrimination among shapes radically different from the reference ones, while bypassing the need for the computationally problematic decomposition into parts; it also addresses the needs of shape categorization, and can be used to derive a range of models of perceived similarity. Representation is Representation of Sim...

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This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain’s free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models

The intent of this paper is the presentation of an associative interpretation of the process of creative thinking. The explanation is not directed to any specific field of application such as art or science but attempts to delineate processes that underlie all creative thought. The discussion will take the following form, (a) First, we will define creative thinking in associative terms and indicate three ways in which creative solutions may be achieved—serendipity, similarity, and mediation, (b) This definition will allow us to deduce those individual difference variables which will facilitate creative performance, (c) Consideration of the definition of the creative process has suggested an operational statement of the definition in the form of a test. The test will be briefly described along with some preliminary research results. (d) The paper will conclude with a discussion of predictions regarding the influence of certain experimentally manipulable variables upon the creative process. Creative individuals and the processes by which they manifest their creativity have excited a good deal of

SOCIAL ACTIVITY WORK ACTIVITY OBJECT MENTAL OBJECT ABSTRACT OBJECT REPRESENTATIONAL OBJECT INTANGIBLE OBJECT ENERGY ENTROPY FORCE PHYSICAL OBJECT MATERIAL PLACE SEPARABLE ENTITY SOCIAL OBJECT ORGANIZATION GEOPOLITICAL ENTITY SOCIAL ROLE PROPERTY ATTRIBUTE LITERAL ATTRIBUTE SCALAR ATTRIBUTE RELATION OBJECT RELATION INVERSE EVENT STATE RELATION INVERSE EVENT RELATION INVERSE EVENT OBJECT RELATION EVENT STATE RELATION EVENT RELATION EVENT OBJECT RELATION Figure 2: Upper Level of ¯Kosmos Ontology. Links represent the "is a subclass of" relation. Hyphenation was removed from original concept names for better reading. 3.1 ¯Kosmos ¯Kosmos 15 is an ontology developed for machine translation of business and financial texts between English, Spanish and other languages. The ontology contains 4790 annotated concepts which are publicly accessible as HTML hypertext and Lisp code. Ontological entries in ¯Kosmos are linked to dictionaries in several languages. Thus, the onto...

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

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Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization.

In this report we review a large body of literature describing how experience affects recognition. Both neurophysiology and psychophysics provide clear evidence for the development of recognition over time. In particular, we show how perceptual learning in recognition tasks can be directly linked to learning in feature tuned inferotemporal lobe neurons in the primate brain. The environment as we experience it, is so structured that potentially very different images appearing in close temporal succession are likely to be views of the same object. We argue that this temporal structure forms the basis of a tendency (a prior in the sense of Bayesian Statistics) of the human visual system to associate images of objects together over short periods of time.

This paper is concerned with establishing broadly-based system-theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful. A general formulation is first given in which two order relations are postulated on a class of models: a constant one of complexity; and a variable one of approximation induced by an observed behaviour. An admissible model is such that any less complex model is a worse approximation. The general problem of identification is that of finding the admissible subspace of models induced by a given behaviour. It is proved under very general assumptions that, if deterministic models are required then nearly all behaviours require models of nearly maximum complexity. A general theory of approximation between models and behaviour is then developed based on subjective probability concepts and semantic information theory The role of structural constraints such as causality, locality, finite memory, etc., are then discussed as rules of the game. These concepts and results are applied to the specific problem or stochastic automaton, or grammar, inference. Computational results are given to demonstrate that the theory is complete and fully operational. Finally the formulation of identification proposed in this paper is analysed in terms of Klir’s epistemological hierarchy and both are discussed in terms of the rich philosophical literature on the acquisition of knowledge. 1