  Transitive Inference (TI) is a cognitive task that assesses an organism's
ability to infer novel relations between items based on previously acquired
knowledge. TI is known for exhibiting various behavioral and neural signatures,
such as the Serial Position Effect (SPE), Symbolic Distance Effect (SDE), and
the brain's capacity to maintain and merge separate ranking models. We propose
a novel framework that casts TI as a probabilistic preference learning task,
using one-parameter Mallows models. We present a series of simulations that
highlight the effectiveness of our novel approach. We show that the Mallows
ranking model natively reproduces SDE and SPE. Furthermore, extending the model
using Bayesian selection showcases its capacity to generate and merge ranking
hypotheses as pairs with connecting symbols are encountered. Finally, we employ
neural networks to replicate Mallows models, demonstrating how this framework
aligns with observed prefrontal neural activity during TI. Our innovative
approach sheds new light on the nature of TI, emphasizing the potential of
probabilistic preference learning for unraveling its underlying neural
mechanisms.
