A novel machine learning model called Temporal Autoencoders for Causal Inference (TACI) accurately detects changing cause-and-effect relationships in complex, time-varying systems like weather patterns and brain activity. By analyzing both synthetic and real data, TACI captures dynamic interactions and quantifies shifts in strength or direction over time. Tested on long-term weather data and brain imaging in monkeys, TACI successfully pinpointed when causal connections emerged, weakened, or reversed.

A study using a computational model reveals factors that influence whether debunking efforts about disputed election results will persuade people to change their beliefs. The model shows that debunking is more likely to succeed when individuals are less certain of their original beliefs and perceive the authority as unbiased and motivated by accuracy.

Scientists developed a computer simulation that models neuron growth in the brain, which could support advancements in neurodegenerative disease research. The simulation accurately replicated real neuron growth patterns in the hippocampus, a brain region key to memory. Built using BioDynaMo software, the model uses Approximate Bayesian Computation to closely match real-life neuron data, improving its precision.