Neurocognitive correlates of semantic memory navigation in Parkinson's disease.

Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data.

In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts.

A Context-Dependent Bayesian Account for Causal-Based Categorization.

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.

On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules).

An adaptive linear filter model of procedural category learning.

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm.

How electroencephalography serves the anesthesiologist.

Major clinical endpoints of general anesthesia, such as the alteration of consciousness, are achieved through effects of anesthetic agents on the central nervous system, and, more precisely, on the brain. Historically, clinicians and researchers have always been interested in quantifying and characterizing those effects through recordings of surface brain electrical activity, namely electroencephalography (EEG). Over decades of research, the complex signal has been dissected to extract its core substance, with significant advances in the interpretation of the information it may contain.