The effects of two Alzheimer's disease related genes and in healthy young adults: An attentional blink study.

Background: Genetic risk factors start to affect the brain and behavior in Alzheimer's disease (AD) before clinical symptoms occur. Although AD is mainly associated with memory deficits, attention and executive dysfunctions can present at the early presymptomatic stages in middle age for those with non-modifiable risks.

Objective: Here, we investigated whether known risk genes for AD already affected attention in young adulthood.

Sensitization and Habituation of Hyper-Excitation to Constant Presentation of Pattern-Glare Stimuli.

Background/objectives: Pattern glare, associated with cortical hyperexcitability, induces visual distortions and discomfort, particularly in individuals susceptible to migraines or epilepsy. While previous research has primarily focused on transient EEG responses to patterned stimuli, this study aims to investigate how continuous presentation of pattern-glare stimuli affects neural adaptation over both fine (seconds) and coarse (entire experiment) temporal scales.

Methods: EEG recordings were obtained from 40 healthy participants exposed to horizontal square-wave gratings at three spatial frequencies presented continuously for three seconds each across multiple trials.

Ecological implications of row width and cultivar selection on rice (Oryza sativa) and barnyardgrass (Echinochloa crus-galli).

Rice (Oryza sativa L.) producers in the Mid-south are experiencing difficulties with herbicide-resistant weeds such as barnyardgrass [Echinochloa crus-galli (L.) P.

Multimodal and Multifactor Branching Time Active Inference.

Active inference is a state-of-the-art framework for modeling the brain that explains a wide range of mechanisms. Recently, two versions of branching time active inference (BTAI) have been developed to handle the exponential (space and time) complexity class that occurs when computing the prior over all possible policies up to the time horizon. However, those two versions of BTAI still suffer from an exponential complexity class with regard to the number of observed and latent variables being modeled.

Deconstructing Deep Active Inference: A Contrarian Information Gatherer.

Active inference is a theory of perception, learning, and decision making that can be applied to neuroscience, robotics, psychology, and machine learning. Recently, intensive research has been taking place to scale up this framework using Monte Carlo tree search and deep learning. The goal of this activity is to solve more complicated tasks using deep active inference.