Greg Jensen

This paper reframes transitive inference as a linear classification problem using least-squares estimation, showing it can reproduce key empirical features of TI without explicit assumptions about transitivity.

This study shows that aging rhesus macaques exhibit age-dependent perivascular space burden, mirroring human patterns, and validates MRI-detected structures as perivascular spaces.

This study applies a drift diffusion model to analyze decision-making dynamics in a transitive inference task, showing stable reaction times despite improved accuracy during learning.

This study examines sex-specific gait adaptation in rats with partial dopaminergic lesions, revealing differences in motor recovery trajectories between males and females.

The paper shows that neurons in the dorsal anterior cingulate cortex encode the ability to detect learnable associations in a task involving transitive inference.

The paper examines age-related GABA level changes in cognitively-intact oldest-old adults, finding that GABA decline flattens after age 80, suggesting a link between GABAergic function and successful cognitive aging.

The paper develops a mathematical framework to explain how learning models, including neural networks, can perform transitive inference through statistical principles and representation learning.

This paper develops and tests neural networks that perform transitive inference, showing they generalize and exhibit behaviors similar to humans, while also revealing inconsistencies between human behavior and certain network solutions.

This study integrates 8 datasets to model the lifespan trajectory of cortical GABA levels, finding an early rapid increase, followed by stability and a gradual decline in adulthood and aging.

This study shows that posterior parietal cortex neurons in monkeys represent ordinal ranks of stimuli during serial learning, supporting a virtual workspace model over associative learning.

This study compares transitive inference performance in humans and rhesus macaques after massed training on the last two list items, finding evidence of general task representation in both species.

This paper introduces ARRIS, a method to estimate trial-by-trial firing rate changes during learning using weighted interpolation and reversible-jump MCMC, enabling real-time analysis of neural activity.