Publications

Abstract

Marr and Poggio’s levels of description are one of the most well-known theoretical constructs of twentieth century cognitive science. It entails that behavior can and should be considered at three different levels: computation, algorithm, and implementation. In this contribution focus is on the computational level of description, the level that describes the “why” of cognition. I argue that the computational level should be taken as a starting point in devising experiments in cognitive (neuro)science. Instead, the starting point in empirical practice often is a focus on the stimulus or on some capacity of the cognitive system. The “why” of cognition tends to be ignored when designing research, and is not considered in subsequent inference from experimental results. The overall aim of this manuscript is to show how re-appreciation of the computational level of description as a starting point for experiments can lead to more informative experimentation.

Abstract

Brain processing depends on the interactions between neuronal groups. Those interactions are governed by the pattern of anatomical connections and by yet unknown mechanisms that modulate the effective strength of a given connection. We found that the mutual influence among neuronal groups depends on the phase relation between rhythmic activities within the groups. Phase relations supporting interactions between the groups preceded those interactions by a few milliseconds, consistent with a mechanistic role. These effects were specific in time, frequency, and space, and we therefore propose that the pattern of synchronization flexibly determines the pattern of neuronal interactions.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

Abstract

Review: User friendliness of the first dictionary of Sign Language of the Netherlands can be improved

Abstract

(Needed: staff for residential care home with good eye-hand coordination. Measuring NGT-skills.)

Abstract

(The Corpus NGT and the daily practice of language teaching)

Abstract

(The Corpus NGT and teacher NGT/interpreter NGT training)