Serial pattern complexity: irregularity and hierarchy.

In perception research, various models have been designed for the encoding of, for example, visual patterns, in order to predict the human interpretation of such patterns. Each of these encoding models provides a few coding rules to obtain codes for a pattern, each code expressing regularity and hierarchy in that pattern. Some of these models employ the minimum principle which states that the human interpretation of a pattern is reflected by the simplest code for that pattern, ie the simplest code according to a given complexity metric. In this paper a new complexity metric is proposed. This metric is based on a formal analysis of the concept of regularity. Some conclusions of this analysis are sketched. The new metric does not depend on artifacts of the coding rules. It accounts for the amounts of irregularity and hierarchy as represented in a code of a pattern, such that these two amounts can be added to determine the complexity of a code. An experiment is discussed that shows that the new metric performs significantly better than the metrics used previously. In particular, the new metric predicts more local pattern organizations than the old metrics. This implies that various local pattern organizations do not falsify the minimum principle anymore.     

Modelling monotonic effects of ordinal predictors in Bayesian regression models

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.     

The anatomy of object processing: The role of anteromedial temporal cortex

How objects are represented and processed in the brain remains a key issue in cognitive neuroscience. We have developed a conceptual structure account in which category-specific semantic deficits emerge due to differences in the structure and content of concepts rather than from explicit divisions of conceptual knowledge in separate stores. The primary claim is that concepts associated with particular categories (e.g., animals, tools) differ in the number and type of properties and the extent to which these properties are correlated with each other. In this review, we describe recent neuropsychological and neuroimaging studies in which we have extended our theoretical account by incorporating recent claims about the neuroanatomical basis of feature integration and differentiation that arise from research into hierarchical object processing streams in nonhuman primates and humans. A clear picture has emerged in which the human perirhinal cortex and neighbouring anteromedial temporal structures appear to provide the neural infrastructure for making fine-grained discriminations among objects, suggesting that damage within the perirhinal cortex may underlie the emergence of category-specific semantic deficits in brain-damaged patients.