Erratum to: Cognitive mechanisms of 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...

Monkey Errors in Transitive Inference

The ability to make transitive inference, (A > B) ∧ (B > C) ` A > C, occurs in many mammal species, given adequate training. Further, errors made by monkeys are quite similar to those made by 5-year-old children. This paper presents a model that learns transitive inference by acquiring and prioritizing sets of behavior rules linked to contexts. When correctly learned, the resulting system is id...

Primate Errors in Transitive Inference

This paper provides a new model of primate transitive inference learning. We begin from the production-rule model of Harris and McGonigle (1994), which was previously the best model at accounting for the performance of squirrel monkeys (Saimiri sciureus) and human children trained on transitive inference (e.g. given A > B and B > C, then A > C) when presented with three items from the training ...

Hybrid transitive trust mechanisms

Establishing trust amongst agents is of central importance to the development of well-functioning multi-agent systems. For example, the anonymity of transactions on the Internet can lead to inefficiencies; e.g., a seller on eBay failing to ship a good as promised, or a user free-riding on a file-sharing network. Trust (or reputation) mechanisms can help by aggregating and sharing trust informat...

A Process Model of Human Transitive Inference