Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/63363
Title: Aplicacion de un modelo de covariacion al analisis de las ejecuciones de sujetos humanos en condiciones de entrenamiento y de trasferencia en una tarea de discriminacion condicional
Author: Martinez, H.
Gonzalez, A.
Ortiz, G.
Carrillo, K.
Issue Date: 1998
Abstract: In a previous study, according to the requeriments of a model of covariation, children and college students showed performance highly efficient in the learning of tasks of conditional discrimination. These results were limited since not collected data on transfer. The purpose of the present study was to explore the necessary conditions to maintain the efficiency of performance in training during different tests of transfer. For this, college students received one of the two training sequence in conditional discrimination of first order, using geometric figures of colors like stimuli presented in a computer. Each sequence consisted of two conditions of training (similarity/oddity or, oddity/similarity) and three tests of transfer. Subjects in the conditions of training were exposed to precise instruction's task and continuous feedback on their performance. In the tests of transfer, feedback was removed and the instructions were vague. Each session of training and transfer consisted of 36 trials of typical arrangements of matching-to-sample (one stimulus as sample and three as comparison). To advance of a training to another, all of the subjects had to fulfill a criterion of performance of two consecutive sessions with 34 correct responses or complete four training sessions as maximum. The results successfully replicated the effective performances during training. However, subjects did not transfer their previous level of performance in the tests. These results are interpreted in terms of the covariation model proposed and suggest that the properties of the used stimuli, as well as the vague instructions could be relevant variables for the transfer of relational learning.
URI: http://hdl.handle.net/20.500.12104/63363
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