Examinando por Autor "Ortega Castro, Nerea"

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  • Miniatura
    Ítem
    In search of rationality in human causal learning: evidence of non-normative strategies when combining causes
    (Universidad de Deusto, 2014-12-05) Ortega Castro, Nerea; Vadillo, Miguel A.; Baker, Andrew G.; Facultad de Psicología y Educación; Psicología experimental
    Causal learning models make different assumptions about how people should combine the influence of different potential causes presented in combination. Based on the linear integration rule, some models propose that the causal impact of a compound should equal the linear sum of each of the causes presented in isolation. Other models such as the Power PC theory are based on a different integration rule, the noisy-OR, suggesting that the rational way of computing the causal impact of a compound involves correcting the sum of the causes by subtracting the overlap between them. The present experiments tested which integration rule people use. Four different cover stories were used to ensure that the participants understood the independence of the causes. The experiments used different sets of probabilities and several formats for presenting information. The results of most experiments do not confirm the predictions of the noisy-OR integration rule. Only one experiment (of ten) supports the predictions of the noisy-OR rule. In spite of having mixed evidence, people do not appear to spontaneously use this rule. We discuss the implications of our results and alternative explanations for our pattern of data, including inhibitory mechanisms and an averaging heuristic.
  • Miniatura
    Ítem
    Two heads are better than one, but how much?: evidence that people's use of causal integration rules does not always conform to normative standards
    (Hogrefe Publishing, 2014) Vadillo, Miguel A. ; Ortega Castro, Nerea; Barberia Fernández, Itxaso ; Baker, A.G.
    Many theories of causal learning and causal induction differ in their assumptions about how people combine the causal impact of several causes presented in compound. Some theories propose that when several causes are present, their joint causal impact is equal to the linear sum of the individual impact of each cause. However, some recent theories propose that the causal impact of several causes needs to be combined by means of a noisy-OR integration rule. In other words, the probability of the effect given several causes would be equal to the sum of the probability of the effect given each cause in isolation minus the overlap between those probabilities. In the present series of experiments, participants were given information about the causal impact of several causes and then they were asked what compounds of those causes they would prefer to use if they wanted to produce the effect. The results of these experiments suggest that participants actually use a variety of strategies, including not only the linear and the noisy-OR integration rules, but also averaging the impact of several causes.