Response times seen as decompression times in Boolean concept use.
This paper reports a study of a multi-agent model of working memory (WM) in the context of Boolean concept learning. The model aims to assess the compressibility of information processed in WM. 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. 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). In this previous study, learning time was found to be a function of compression time. To assess the effect of decompression time, this paper presents an extended intra-conceptual study of response times for two- and 3D concepts. Response times are measured in recognition phases. 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. 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 results corroborate the metric of decompression given by the multi-agent model, especially when the model is parameterized following static serial processing of information. Also, this static serial model better fits the patterns of response times than an exemplar-based model.
Bradmetz
Joël
J
Université de Reims, Reims, France.
Mathy
Fabien
F
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001288:Attention; D003118:Color Perception; D003210:Concept Formation; D003657:Decision Making; D003867:Depth Perception; D005260:Female; D005793:Generalization, Psychological; D006801:Humans; D008297:Male; D008570:Memory, Short-Term; D008960:Models, Psychological; D016571:Neural Networks, Computer; D009949:Orientation; D010364:Pattern Recognition, Visual; D011340:Problem Solving; D011601:Psychophysics; D011930:Reaction Time; D012691:Serial Learning; D012858:Size Perception