A central question about cognition is how, faced with a situation, one explores possible ways of understanding and responding lo it In particular, how do concepts initially considered irrelevant, or not even considered at all, become relevant in response to pressures evoked by the understanding process itself We describe a model of concepts and high-level perception in which concepts consist of a central region surrounded by a dynamic nondeterministic "halo of potential associations, in which relevance and degree of association change as processing proceeds. As the representation of a situation is built, associations arise and are considered in a probabilistic fashion according to a parallel terraced scan, in which many routes toward understanding the situation are tested in parallel, each at a rate and to a depth reflecting ongoing evaluations of its promise. We describe a computer program that implements this model in the context of analogy-making, and illustrate, using screen dumps from a run, how the program's ability to flexibly bring in appropriate concepts for a given situation emerges from the mechanisms we are proposing.

Empirical psychological experimentation (very briefly reviewed here) has provided evidence of top-down conceptual constraints on letter perception. The role hypothesis suggests that these conceptual constraints take the form of structural subcomponents (roles) and relations between subcomponents (r-roles). In this paper, we present a fully-implemented computer model based on the role hypothesis of letter recognition. The emergent model of letter perception discussed below offers a cogent explanation of human letter-perception data — especially with regard to error-making. The model goes beyond simple categorization by parsing a letter-form into its constituent parts. As it runs, the model dynamically builds (and destroys) a context-sensitive internal representation of the letter that it is perceiving. The representation emerges as by-product of a parallel exploration of possible categories. The model is able to successfully recognize (i.e., conceptually parse) many diverse letters at the extremes of their categories.

Tabletop is a computer model of analogy-making that has a nondeterministic parallel architecture. It is based on the premise that analogy-making is a by-product of high-level perception, and it operates in a restricted version of an everyday domain: that of place-settings on a table. The domain's simplicity helps clarify the tight link between perception and analogy-making. In each problem, a table configuration is given; the user, hypothetically seated at the table, points at some object. The program responds by doing "the same thing", as determined from the opposite side of the table. Being nondeterministic, Tabletop acts differently when run repeatedly on any problem. Thus to understand how diverse pressures affect the program, one must compile statistics of many runs on many problems. Tabletop was tested on several families of interrelated problems, and a performance landscape was built up, representing its "likes" and "dislikes". Through qualitative comparisons of this landscape with human preferences, one can assess the psychological realism of Tabletop's "uste".

This paper describes Tabletop, a computer progam that models human analogy-making in a micro-world consisting of a small table covered with ordinary table objects. We argue for the necessity, even in this simple domain, of an architecture that builds its own representations by means of a continual interaction between an associative network of fixed concepts (the Slipnet) and simple low-level perceptual agents (codelets), that relies on local processing and (simulated) parallelism, and that is fundamentally stochastic. Several problems solved by the Tabletop program are used to illustrate these principles.