The simulation of verbal learning behavior

Feigenbaum, Edward A.

doi:10.1145/1460690.1460704

Cited by 138 publications

(98 citation statements)

References 8 publications

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“…The order of use of these discriminating properties and subobjects is important and was extensively studied with the NOTICING ORDER mechanism of the EPAM II model (Feigenbaum, 1959;Feigenbaum, 1961). The noticing order is important because it encodes much of the learner's strategy in the familiarization of new material, and the learner's adaptation to the locus of differences that could be used for discrimination learning.…”

Section: Discriminationmentioning

confidence: 99%

EPAM‐like Models of Recognition and Learning*

1984

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A description is provided of EPAM‐III, a theory in the form of a computer program for simulating human verbal learning, along with a summary of the empirical evidence for its validity. Criticisms leveled against the theory in a recent paper by Barsalou and Bower are shown to derive largely from their misconception that EPAM‐III employed a binary, rather than n‐ary branching discrimination net. It is shown that Barsalou and Bower also failed to understand how the recursive structure of EPAM‐III eliminates the need to duplicate test nodes that are used to recognize subobjects, and how the possibility of redundant recognition paths controls the sensitivity of EPAM to noticing order. EPAM is also compared briefly with other theories of human discrimination and discrimination learning, including PANDEMONIUM‐like systems and dataflow nets.

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Section: Discriminationmentioning

confidence: 99%

EPAM‐like Models of Recognition and Learning*

1984

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“…He devised the following criteria for testing the presence of intuition. As an illustration, Simon cited a program named EPAM (Elementary Perceiver and Memorizer) [107,108]. When a stimulus was presented to EPAM, the program applied a sequence of tests to it, using the outcomes of the tests to sort it down along a discrimination net until it was differentiated from alternative stimuli.…”

Section: Simulation Of Gestalt Phenomenonmentioning

confidence: 99%

Deciphering the Enigma of Human Creativity: Can a Digital Computer Think?

Hong¹

2013

J Comput Sci Syst Biol

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The objective of the present article is a) to explain humans' high creativity in non-mystic and unambiguous terms, b) to evaluate the performance of problem-solving computer programs and c) to make suggestions about future designs of heuristics. Unlike many previous attempts in the past century, we sought inspiration from two sources that had been neglected or excluded from considerations by experts: artificial intelligence and introspections of a number of highly creative individuals, who confessed that they had a penchant for visual thinking. Simonton's chance-configuration model was refurbished accordingly. It is now possible for the refurbished model to explain a number of outstanding puzzles that had eluded our predecessors: a) what intuition is, b) why creators had no idea about their source of inspiration even after the fact, c) a peculiar event happening at the discovery time, known as the "aha" phenomenon, d) a type of accidental discoveries known as serendipity. Moreover, the elusive concept of abduction advanced by philosopher Charles Peirce is actually visual thinking in disguise. Blessed with this new understanding, we could evaluate the performance of a number of problem-solving computer programs from a cognitive point of view. It turned out that the common thread that links human creativity and computerbased creative problem solving is heuristic searching. Recognizing that a digital computer must perform heuristic searching in a digital environment, which is not the most user-friendly environment to do so, we made suggestions about how to circumvent the restrictions without sacrificing the principles in future designs of heuristics.

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“…7 We have experimented with both discrimination networks (Feigenbaum, 1963) and hash tables for indexing. The exact indexing method is not crucial in most domains; there are rarely a large number of instances under a given generalization, since subgeneralizations tend to be formed as the number of instances grows.…”

Section: Unimem's Representation Of Instances and Conceptsmentioning

confidence: 99%

“…GBM can be viewed as a large discrimination net (Feigenbaum, 1963), so UNIMEM starts with its most general node and carries out a controlled depth-first search to find the most specific generalization(s) that legitimately describe the new instance. When the search begins, none of the input features have been matched to a generalization.…”

Section: Searching the Generalization Hierarchymentioning

confidence: 99%

Untitled

Lebowitz

1987

Machine Learning

104

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Abstract. Learning by observation involves automatic creation of categories that summarize experience. In this paper we present UNIMEM, an artificial intelligence system that learns by observation. UNIMEM is a robust program that can be run on many domains with real-world problem characteristics such as uncertainty, incompleteness, and large numbers of examples. We give an overview of the program that illustrates several key elements, including the automatic creation of non-disjoint concept hierarchies that are evaluated over time. We then describe several experiments that we have carried out with UNIMEM, including tests on different domains (universities, Congressional voting records, and terrorist events) and an examination of the effect of varying UNIMEM's parameters on the resulting concept hierarchies. Finally we discuss future directions for our work with the program. IntroductionLearning from observation is a task that is important in domains where examples are not pre-classified, but where one still wishes to detect general rules and intelligently organize examples. In this paper we discuss UNIMEM, a system that learns from observation by noticing regularities among examples and organizing them into a generalization hierarchy. We view UNIMEM both as implementing an algorithm for concept formation and as a prototype intelligent information system that can incorporate large amounts of data into memory and retrieve appropriate information in response to user queries. UNIMEM is not intended to be a psychological model per se, since it deals with a task more data-intensive than people are likely to perform. However, in developing the program we have made use of techniques derived by observing human behavior.The task of UNIMEM is to take a series of examples (or instances) that are expressed as collections of features and build up a generaliza-104 M. LEBOWITZ tion hierarchy of concepts. For example, UNIMEM might use information about a collection of universities to inductively determine the concepts of Ivy League universities, European technical universities, and so forth, and determine which examples are described by which concepts. The point of creating such concept descriptions is that they allow a performance element using the output of the program to make inferences about new examples based on partial information.Successful learning from real-world input must deal with several constraints. The key features that characterize the operation of UNIMEM are:• It learns by observation] it is not explicitly told how examples should be grouped into categories;• It is incremental] output must be available after processing each example; it cannot wait for all the input;• It must handle examples in large numbers (currently hundreds, eventually more);• Its generalizations are pragmatic] they need not perfectly describe all the instances they cover.1Although certain learning systems have dealt with tasks having some of these characteristics, little work has been concerned with all of them. However, all seem to characte...

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The simulation of verbal learning behavior

Cited by 138 publications

References 8 publications

EPAM‐like Models of Recognition and Learning*

EPAM‐like Models of Recognition and Learning*

Deciphering the Enigma of Human Creativity: Can a Digital Computer Think?

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