6/21/2023 0 Comments Daa decompressor![]() ![]() ![]() Gold developed the notion of convergence (identification in the limit) by understanding that the most accurate hypotheses are reached faster when beginning to test the smallest ones (see also Osherson, Stob, & Weinstein, 1986, for a development of Gold theories). In artificial intelligence, a theoretical analysis of inductive reasoning has been introduced by Gold ( 1967). Also, this static serial model better fits the patterns of response times than an exemplar-based model. The results corroborate the metric of decompression given by the multi-agent model, especially when the model is parameterized following static serial processing of information. Three experiments were conducted with 65, 49, and 84 undergraduate students who were given Boolean concept learning tasks in two and three dimensions (also called rule-based classification tasks). The model explains why the time required to compress a sample of examples into a rule is directly linked to the time to decompress this rule when categorizing examples. Response times are measured in recognition phases. To assess the effect of decompression time, this paper presents an extended intra-conceptual study of response times for two- and 3D concepts. In this previous study, learning time was found to be a function of compression time. This model has been successfully applied in measuring learning times for three-dimensional (3D) concepts (Mathy and Bradmetz in Curr Psychol Cognit 22(1):41–82, 2004). Concept complexity is described as a function of communication resources (i.e., the number of agents and the structure of communication between agents) required in WM to learn a target concept. The model aims to assess the compressibility of information processed in WM. This paper reports a study of a multi-agent model of working memory (WM) in the context of Boolean concept learning. ![]()
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