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.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

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. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising.

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.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic.

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). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

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. Methodological, engineering, statistical, mathematical, and computer progress now furnishes advanced tools that not only allow quantification of the effects of anesthesia, but also shed light on some aspects of anesthetic mechanisms. In this article, we will review how advanced EEG serves the anesthesiologist in that respect, but will not review other intraoperative utilities that have no direct relationship with consciousness, such as monitoring of brain and spinal cord integrity. We will start with a reminder of anesthestic effects on raw EEG and its time and frequency domain components, as well as a summary of the EEG analysis techniques of use for the anesthesiologist. This will introduce the description of the use of EEG to assess the depth of the hypnotic and anti-nociceptive components of anesthesia, and its clinical utility. The last part will describe the use of EEG for the understanding of mechanisms of anesthesia-induced alteration of consciousness. We will see how, eventually in association with transcranial magnetic stimulation, it allows exploring functional cerebral networks during anesthesia. We will also see how EEG recordings during anesthesia, and their sophisticated analysis, may help corroborate current theories of mental content generation.