German Title: Pseudokontingenzen - Regelbasiert und assoziativ

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Abstract

The present work puts forward a rule-based model for judging the direction of a contingency. A set of “alignment rules” (ARs) is defined, all of which bind frequent observations to frequent observations and infrequent observations to infrequent observations. These rules qualify as possible mechanisms behind pseudocontingencies (PCs, Fiedler, Freytag, & Meiser, 2009). Six experiments, involving social and non-social stimuli, are presented that pit the predictions of the rule-based PCs against associative models for contingency judgments (Van Rooy, Van Overwalle, Vanhoomissen, Labiouse, & French, 2003). Results consistently show that participants associate predictors with criteria that are non-contingent but jointly frequent and rare. Crucially, these illusory contingency judgments are shown to persist (a) in attitude ratings after extended observational learning and (b) at asymptote in operant learning. In sum, the results are evidence for the impact of rule-based PCs under conditions that call for associative learning. In a next step, rational arguments (Anderson, 1990) are used to set the AR apart from other rule-based models with similar empirical predictions. Results of two simulations reveal that the AR performs remarkably well under real-life constraints. Under clearly definable conditions, like strongly skewed base rates and small observational samples, the AR performs even better than other models, like ΔP (Allan, 1993) or the Sum-of-Diagonals (SoD, Inhelder & Piaget, 1958). Finally, the AR is claimed to be a natural by-product of the learning history with strong contingencies. Suggestive evidence from a simulation is provided that shows an increased likelihood of jointly skewed base rates, the precondition for ARs, in the presence of strong contingencies. Thus, ARs might develop from a confusion of the learned above chance probability p ( joint-skew | strong-contingency ) with an above chance probability p ( strong-contingency | joint –skew ) that justifies an AR inference. Possible future research on how joint observations and base-rates interact to influence contingency judgments is outlined.

Translation of abstract (English)

The present work puts forward a rule-based model for judging the direction of a contingency. A set of “alignment rules” (ARs) is defined, all of which bind frequent observations to frequent observations and infrequent observations to infrequent observations. These rules qualify as possible mechanisms behind pseudocontingencies (PCs, Fiedler, Freytag, & Meiser, 2009). Six experiments, involving social and non-social stimuli, are presented that pit the predictions of the rule-based PCs against associative models for contingency judgments (Van Rooy, Van Overwalle, Vanhoomissen, Labiouse, & French, 2003). Results consistently show that participants associate predictors with criteria that are non-contingent but jointly frequent and rare. Crucially, these illusory contingency judgments are shown to persist (a) in attitude ratings after extended observational learning and (b) at asymptote in operant learning. In sum, the results are evidence for the impact of rule-based PCs under conditions that call for associative learning. In a next step, rational arguments (Anderson, 1990) are used to set the AR apart from other rule-based models with similar empirical predictions. Results of two simulations reveal that the AR performs remarkably well under real-life constraints. Under clearly definable conditions, like strongly skewed base rates and small observational samples, the AR performs even better than other models, like ΔP (Allan, 1993) or the Sum-of-Diagonals (SoD, Inhelder & Piaget, 1958). Finally, the AR is claimed to be a natural by-product of the learning history with strong contingencies. Suggestive evidence from a simulation is provided that shows an increased likelihood of jointly skewed base rates, the precondition for ARs, in the presence of strong contingencies. Thus, ARs might develop from a confusion of the learned above chance probability p ( joint-skew | strong-contingency ) with an above chance probability p ( strong-contingency | joint –skew ) that justifies an AR inference. Possible future research on how joint observations and base-rates interact to influence contingency judgments is outlined.