English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

It has been proposed that, when processing a stream of events, humans dividetheir experiences in terms of inferred latent causes (LCs) to supportcontext-dependent learning. However, when shared structure is present acrosscontexts, it is still unclear how the "splitting" of LCs and learning of sharedstructure can be simultaneously achieved. Here, we present the Latent CauseNetwork (LCNet), a neural network model of LC inference. Through learning, itnaturally stores structure that is shared across tasks in the network weights.Additionally, it represents context-specific structure using a context module,controlled by a Bayesian nonparametric inference algorithm, which assigns aunique context vector for each inferred LC. Across three simulations, we foundthat LCNet could 1) extract shared structure across LCs in a function learningtask while avoiding catastrophic interference, 2) capture human data oncurriculum effects in schema learning, and 3) infer the underlying eventstructure when processing naturalistic videos of daily events. Overall, theseresults demonstrate a computationally feasible approach to reconciling sharedstructure and context-specific structure in a model of LCs that is scalablefrom laboratory experiment settings to naturalistic settings.

Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causes