Paul D. Thorn

Individuals often revise their beliefs when confronted with contradicting evidence. Belief revision in the spatial domain can be regarded as variation of initially constructed spatial mental models. Construction and revision usually follow distinct cognitive principles. The present study examines whether principles of revisions which follow constructions under high task demands differ from principles applied after less demanding constructions. We manipulated the task demands for model constructions by means of the continuity with which a spatial model was constructed. We administered tasks with continuous, semi-continuous, and discontinuous conditions as between-subject factor (experiment 1) and as within-subject factor (experiment 2). Construction and revision followed distinct cognitive principles in the changeless conditions of experiment 1. With increased task demands due to switches between different continuity conditions (experiment 2), reasoners adapted the principles they used for model revisions to the principles which they had used during antecedent constructions.

There are numerous formal systems that allow inference of new conditionals based on a conditional knowledge base. Many of these systems have been analysed theoretically and some have been tested against human reasoning in psychological studies, but experiments evaluating the performance of such systems are rare. In this article, we extend the experiments in [19] in order to evaluate the inferential properties of c-representations in comparison to the well-known Systems P and Z. Since it is known that System Z and c-representations mainly differ in the sorts of inheritance inferences they allow, we discuss subclass inheritance and present experimental data for this type of inference in particular

According to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists—a fact that we call the “revenge of ecological rationality”. Nevertheless one can show that a certain MI strategy exists, called “AW”, which is universally long-run optimal, with provably small short-run losses, in comparison to any set of prediction methods that it can use as input. We call this property universal access-optimality. Local and short-run improvements over AW are possible, but only at the cost of forfeiting universal access-optimality. The last part of the paper includes an empirical study of MI strategies in application to an 8-year-long data set from the Monash University Footy Tipping Competition