Publications

Abstract

Scene-selective regions (SSRs), including the parahippocampal place area (PPA), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS), are among the most widely characterized functional regions in the human brain. However, previous studies have mostly focused on the commonality within each SSR, providing little information on different aspects of their variability. In a large group of healthy adults (N = 202), we used functional magnetic resonance imaging to investigate different aspects of topographical and functional variability within SSRs, including interindividual, interhemispheric, and sex differences. First, the PPA, RSC, and TOS were delineated manually for each individual. We then demonstrated that SSRs showed substantial interindividual variability in both spatial topography and functional selectivity. We further identified consistent interhemispheric differences in the spatial topography of all three SSRs, but distinct interhemispheric differences in scene selectivity. Moreover, we found that all three SSRs showed stronger scene selectivity in men than in women. In summary, our work thoroughly characterized the interindividual, interhemispheric, and sex variability of the SSRs and invites future work on the origin and functional significance of these variabilities. Additionally, we constructed the first probabilistic atlases for the SSRs, which provide the detailed anatomical reference for further investigations of the scene network.

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

Psycholinguistic research has typically portrayed speech production as a relatively automatic process. This is because when errors are made, they occur as seldom as one in every thousand words we utter. However, it has long been recognised that we need some form of control over what we are currently saying and what we plan to say. This capacity to both monitor our inner speech and self-correct our speech output has often been assumed to be a property of the language comprehension system. More recently, it has been demonstrated that speech production benefits from interfacing with more general cognitive processes such as selective attention, short-term memory (STM) and online response monitoring to resolve potential conflict and successfully produce the output of a verbal plan. The conditions and levels of representation according to which these more general planning, monitoring and control processes are engaged during speech production remain poorly understood. Moreover, there remains a paucity of information about their neural substrates, despite some of the first evidence of more general monitoring having come from electrophysiological studies of error related negativities (ERNs). While aphasic speech errors continue to be a rich source of information, there has been comparatively little research focus on instances of speech repair. The purpose of this Frontiers Research Topic is to provide a forum for researchers to contribute investigations employing behavioural, neuropsychological, electrophysiological, neuroimaging and virtual lesioning techniques. In addition, while the focus of the research topic is on novel findings, we welcome submission of computational simulations, review articles and methods papers.

Abstract

This paper reports on an exploration of the ways in which multiple entities are expressed in Turkish Sign Language (TİD). The (descriptive and quantitative) analyses provided are based on a corpus of both spontaneous data and specifically elicited data, in order to provide as comprehensive an account as possible. We have found several devices in TİD for expression of multiple entities, in particular localization, spatial plural predicate inflection, and a specific form used to express multiple entities that are side by side in the same configuration (not reported for any other sign language to date), as well as numerals and quantifiers. In contrast to some other signed languages, TİD does not appear to have a productive system of plural reduplication. We argue that none of the devices encountered in the TİD data is a genuine plural marking device and that the plural interpretation of multiple entity localizations and plural predicate inflections is a by-product of the use of space to indicate the existence or the involvement in an event of multiple entities.