Flexible use of recent information in causal and predictive judgments.
Associative and statistical theories of causal and predictive learning make opposite predictions for situations in which the most recent information contradicts the information provided by older trials (e.g., acquisition followed by extinction). Associative theories predict that people will rely on the most recent information to best adapt their behavior to the changing environment. Statistical theories predict that people will integrate what they have learned in the two phases. The results of this study showed one or the other effect as a function of response mode (trial by trial vs. global), type of question (contiguity, causality, or predictiveness), and postacquisition instructions. That is, participants are able to give either an integrative judgment, or a judgment that relies on recent information as a function of test demands. The authors concluded that any model must allow for flexible use of information once it has been acquired.
Department of Psychology, Deusto University, Bilbao, Spain. matute@orion.deusto.es
Helena
H
Matute
Sonia
S
Vegas
Pieter-Jan
PJ
De Marez
D001245:Association Learning; D001288:Attention; D003210:Concept Formation; D003937:Diagnosis, Differential; D004342:Drug Hypersensitivity / Q000175:diagnosis / Q000523:psychology; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D007600:Judgment; D011939:Mental Recall; D011336:Probability; D011340:Problem Solving