Discounting and Augmentation in Causal Conditional Reasoning: Causal Models or Shallow Encoding?

ABSTRACT: Recent research comparing mental models theory and causal Bayes nets for their ability to account for discounting and augmentation inferences in causal conditional reasoning had some limitations. One of the experiments used an ordinal scale and multiple items and analysed the data by subjects and items. This procedure can create a variety of problems that can be resolved by using an appropriate cumulative link function mixed models approach in which items are treated as random effects. Experiment 1 replicated this earlier experiment and analysed the results using appropriate data analytic techniques. Although successfully replicating earlier research, the pattern of results could be explained by a much simpler "shallow encoding" hypothesis. Experiment 2 introduced a manipulation to critica

SUBMITTER: Hall S 

PROVIDER: S-EPMC5193512 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature