Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognit...

When logic fails: implicit transitive inference in humans.

Transitive inference (TI) in animals (e.g., choosing A over C on the basis of knowing that A is better than B and B is better than C) has been interpreted by some as reflecting a declarative logical inference process. We invert this anthropomorphic interpretation by providing evidence that humans can exhibit TI-like behavior on the basis of simpler associative mechanisms that underlie many theo...

A Process Model of Human Transitive Inference

A Hippocampal - like Neural Network Model Solvesthe Transitive Inference

Lifted Inference on Transitive Relations

Lifted inference algorithms are able to boost efficiency through exploiting symmetries of the underling firstorder probabilistic models. Models with transitive relations (e.g., if X and Y are friends and so are Y and Z, then X and Z will likely be friends) are essential in social network analysis. With n elements in a transitive relation model, the computational complexity of exact propositiona...