How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner’s hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment.     

Untangling the Theory of Planned Behavior's auxiliary assumptions and theoretical assumptions: Implications for predictive and intervention studies

It has been asserted that the Theory of Planned Behavior (TPB) is better at behavioral prediction than it is behavioral change. However, the performance of a theory depends not only on the theoretical propositions, but the auxiliary assumptions attached to the theory. It is because of such auxiliary assumptions, which are required to bridge the gap between nonobservational terms at the level of theory and the observational terms at the level of the empirical hypothesis, that we believe critiques of the TPB's utility are misguided. We argue that a failure to separate theoretical assumptions from auxiliary assumptions leads to this false assumption. We suggest the importance of distinguishing between the auxiliary assumptions required for prediction studies and the auxiliary assumptions required for intervention studies. We believe that in attaching sound auxiliary assumptions to intervention studies, the TPB is equally effective at facilitating behavior change as it is behavior prediction.     

Identifying Misfitting Achievement Estimates in Performance Assessments: An Illustration Using Rasch and Mokken Scale Analyses

Researchers apply individual person fit analyses as a procedure for checking model-data fit for individual test-takers. When a test-taker misfits, it means that the inferences from their test score regarding what they know and can do may not be accurate. One problem in applying individual person fit procedures in practice is the question of how much misfit it takes to make the test score an untrustworthy estimate of achievement. In this paper, we argue that if a person’s responses generally follow a monotonic pattern, the resulting test score is “good enough” to be interpreted and used. We present an approach that applies statistical procedures from the Rasch and Mokken measurement perspectives to examine individual person fit based on this good enough criterion in real data from a performance assessment. We discuss how these perspectives may facilitate thinking about applying individual person fit procedures in practice.