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Mechanisms of Predictive and Diagnostic Causal Induction

In predictive causal inference, people reason from causes to effects, whereas in diagnostic inference, they reason from effects to causes. Independently of the causal structure of the events, the temporal structure of the information provided to a reasoner may vary (e.g., multiple events followed by a single event vs. a single event followed by multiple events). The authors report 5 experiments in which causal structure and temporal information were varied independently. Inferences were influenced by temporal structure but not by causal structure. The results are relevant to the evaluation of 2 current accounts of causal induction, the Rescorla-Wagner (R. A. Rescorla & A. R. Wagner, 1972) and causal model theories (M. R. Waldmann & K. J. Holyoak, 1992).

Associative and causal reasoning accounts of causal induction : Symmetries and asymmetries in predictive and diagnostic inferences

Associative and causal reasoning accounts are probably the two most influential types of accounts of causal reasoning processes. Only causal reasoning accounts predict certain asymmetries between predictive (i.e., reasoning from causes to effects) and diagnostic (i.e., reasoning from effects to causes) inferences regarding cue-interaction phenomena (e.g., the overshadowing effect). In the experiments reported here, we attempted to delimit the conditions under which these asymmetries occur. The results show that unless participants perceived the relevance of causal information to solving the task, predictive and diagnostic inferences were symmetrical. Specifically, Experiments 1A and 1B showed that implicitly stressing the relevance of causal information by having participants review the instructions favored the presence of asymmetries between predictive and diagnostic situations. In addition, Experiment 2 showed that explicitly stressing the relevance of causal information by stating the importance of the causal role of events after the instructions were given also favored the asymmetry.

Does the type of judgement required modulate cue competition

According to the comparator process hypothesis (Matute, Arcediano, & Miller, 1996), cue competition in the learning of between-events relationships arises if the judgement required involves a comparison between the probability of the outcome given the target cue and the probability of the outcome given the competing cue. Alternatively, other associative accounts (the Rescorla-Wagner model: Rescorla &Wagner, 1972) conceive cue competition as a learning deficit affecting the target cue-outcome association. Consequently, the comparator process hypothesis predicts that cue competition occurs in inference judgements but not in contiguity ones, for only the first type of judgement implicitly involves such a comparison. On the other hand, the Rescorla-Wagner model predicts cue competition in both inference and contiguity judgements, because it establishes no relevant role for the type of judgement in producing cue competition. In Experiments 1 and 2 we manipulated the relative validity of cues and the type of question (inference vs. contiguity) in a predictive learning task. In both experiments we found a cue competition effect, but no interaction between the relative validity of cues and the type of question, suggesting that the Rescorla-Wagner theory suffices to explain cue competition.

Development and validation of an attributional style questionnaire for adolescents.

We describe the development and psychometric characteristics of a new version of the Attributional Style Questionnaire (ASQ; Seligman, Abramson, Semmell, & Von Baeyer, 1979)--a version called the Attributional Style Questionnaire for Adolescents (ASQ-A)--using 3 samples (Ns = 547, 438, and 240) of Spanish secondary school students. In Study 1, the initial pool of 87 items was reduced to 54. Study 2 further analyzed the 54 scale items and revealed that the Internality, Stability, and Globality subscale scores had good reliability, good factorial construct validity, and satisfactory associations with maladaptive mood ratings. In Study 3, the regression analyses showed good and specific predictive validities of ASQ-A subscales for the attributions that the adolescents made about a particular real-life stressful situation. Study 4 showed that over an 8-month period the changes in the Stability and Globality subscales depended on the intensity of stressful life events experienced in this period. Overall, the studies revealed that the new ASQ-A served as an appropriate instrument to assess attributional style in adolescents.

Inter-relaciones entre los niveles computacional y algorítmico

El estudio de los procesos de inferencia causal basada en la observacion de acontecimientos ha ocupado historicamente un lugar destacado en la literatura sobre los procesos cognitivos basicos. El articulo de Perales y cols. viene a ofrecernos una vision teorica actualizada de un aspecto de ese estudio, esencial para entender como se llevan a cabo estos procesos. Nos referimos al analisis computacional (Marr, 1982), es decir, un analisis de que tipo de informacion resulta relevante en el procesamiento causal y por que ese tipo de informacion es relevante. El analisis computacional que realizan los autores esta inspirado, casi totalmente, en consideraciones de tipo normativo o racionales (ver Cheng, 1997, 2000 para una posicion similar). Sin embargo, hay que resenar que la aproximacion racional que realizan de la caracterizacion de las relaciones causales, siguiendo a Cheng, es muy parcial. Esta basada en criterios exclusivamente estadisticos, obviandose una serie de principios al menos igualmente definitorios de causalidad. Por ejemplo, en un analisis racional de la inferencia causal, Anderson (1991) menciona la importancia de la contiguidad espacial y temporal y de la similitud entre causas y efectos. Es importante subrayar que el aprendizaje asociativo es sensible a estos principios. Ademas, la similitud entre causas y efectos puede considerarse un caso particular de un principio mas general necesario para caracterizar relaciones causales: la relacion entre la naturaleza de los acontecimientos. Incluso manteniendo la misma relacion estadistica con un efecto, dos candidatos causales tienen diferente probabilidad de generarlo, dependiendo de su naturaleza (es razonable que un sabor sea mas facilmente causa del dolor de