Aprendizaje de relaciones de contingencia y causalidad: Una aproximación a las tendencias teóricas actuales

Psicológica (Impact Factor: 0.3). 01/1999; 20(3):163-193.

ABSTRACT En las dos últimas décadas el estudio de la causalidad desde la perspectiva de la Psicología del Aprendizaje ha experimentado un auge notable. Sin embargo, el crecimiento del conjunto de datos experimentales no ha ido acompañado de un desarrollo teórico que permitiese integrarlos. Cualquier marco teórico que pretenda ser explicativo debe dar cuenta de estos datos, así como su relación con los tipos de tarea utilizados, a menudo de forma indiferenciada. Esta revisión pretende ofrecer una visión organizada del corpus experimental desarrollado dentro del campo del aprendizaje causal humano y esbozar algunas líneas posibles para la integración teórica. El análisis de las similitudes e incompatibilidades entre distintas aproximaciones (hasta la fecha necesariamente parciales) podrá llevar en el futuro a modelos de organización serial que abarquen las distintas etapas del procesamiento en este tipo de aprendizaje. Palabras clave: Aprendizaje de causalidad, contingencia, covariación, modelos asociativos, modelos de reglas.

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    ABSTRACT: In two experiments, positive, negative, and zero response-outcome contingencies were responded to and rated by college students under a free-operant procedure. In Experiment 1, outcomes were either neutral or were associated with point gain. In Experiment 2, subjects were administered different outcome treatments: neutral outcomes, outcomes associated with money gain, or outcomes associated with money loss. In both experiments, subjects' judgments of response-outcome contingency and their operant responses were each strong linear functions of ΔP, the difference between the probability of an outcome given a response and the probability of an outcome given no response. Appetitive and aversive outcomes produced opposite and symmetrical response patterns. In Experiment 1, no differences in ratings occurred with neutral or appetitive outcomes; however, in Experiment 2, more potent appetitve outcomes led to somewhat more extreme ratings than either neutral or aversive outcomes. Increasing outcome probability produced only a slight bias in ratings of noncontingent problems in Experiment 1 and no bias in Experiment 2. Contrary to predictions derived from an analysis of superstitious behavior, increasing outcome probability in noncontingent problems decreased operant responding when outcomes were appetitive and increased operant responding when outcomes were aversive. Trend analyses revealed that Δ P was superior to several other metrics in predicting subjects' estimates of contingency and the behavioral effects of contingency. Operant responding was in closer accord with matching predictions than with maximizing predictions.
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    ABSTRACT: Most studies of human contingency judgment have been based on the assumption that frequency information about one predictor is assessed in isolation of information about other predictors. Recent evidence, however, suggests that the judged predictive strength of one cue is influenced by the predictive strengths of other copresent cues. Two experiments demonstrate that stimuli with the same outcome contingencies may nonetheless have different predictive strengths as the result of cue interaction. The first experiment, in which a within-subject design was used, provides a demonstration of blocking. A stimulus presented in compound with a strong predictor was rated as less predictive than another stimulus that was presented in compound with a nonpredictive cue. In the second experiment, cue interactions in conditioned inhibition were examined. A stimulus gained negative predictive strength as the result of compound presentations with a positive predictor when the outcome was not presented. This negative predictor was compared with an otherwise analogous stimulus that was not presented in compound with a positive predictor. These results support the use of animal-conditioning models as accounts of human contingency learning.
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