22 research outputs found
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Fuzzy Implication Formation in Distributed Associative Memory
An analysis is presented of the emergence of implicational relations within associative memory systems. Implication is first formulated within the framework of Zadeh's theory of approximate reasoning. In this framework, implication is seen to be a fuzzy relation holding between linguistic variables, that is, variables taking linguistic terms (e.g., "young", "very old") as values. The conditional expressions that obtain from this formulation may be naturally cast in terms of vectors and matrices representing the membership functions of the fuzzy sets that, in turn, represent the various linguistic terms and fuzzy relations. The resulting linear algebraic equations are shown to directly correspond to those that specify the operation of certain distributed associative connectionist memory systems. In terms of this correspondence, implication as a fuzzy relation can be seen to arise within the associative memory by means of the operation of standard unsupervised learning procedures. That is, implication emerges as a simple and direct result of experience with instances of events over which the implicational relationship applies. This is illustrated with an example of emergent implication in a natural coarsely coded sensory system. The percepts implied by sensory inputs in this example are seen to exhibit properties that have, in fact, been observed in the system in nature. Thus, the approach appears to have promise for accounting for the induction of implicational structures in cognitive systems
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Direct, Incremental Learning of Fuzzy Propositions
To enable the gradual learning of symbolic representations, a new fuzzy logical operator is developed that supports the expression of negation to degrees. As a result, simple fuzzy propositions become instantiable in a feedforward network having multiplicative nodes and tunable negation links. A backpropagation learning procedure has been straightforwardly developed for such a network and applied to effect the direct, incremental learning of fuzzy propositions in a natural and satisfying manner. Some results of this approach and comparisons to related approaches are discussed as well as directions for further extension
Contextual Validity and the Effects of Low Constraint Sentence Contexts on Lexical Decisions
Subjects made lexical decisions after reading either (a) low-constraint sentence contexts that did not predict the identity or meaning of congruous targets (e.g. “Mary went to her room to look at her XXXX”), or (b) control contexts that were randomly ordered lists of words. The crucial variable was the validity of the contextual information. When the sentence contexts were incongrous with the word targets as often as they were congruous (the “less-valid environment”), the congruous contexts had a slight inhibitory effect on decision latency relative to the baseline condition. In contrast, when the contexts were always congruous with the word targets (“valid environment”), they had a large facilitatory effect on decision latency. These results suggest that (a) the effects of congruous contexts can depend on the validity of the contexts across the entire experimental session, and (b) contextual facilitation may be due in part to sentence level processes