Author

J. B. Tenenbaum

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Animal Behavior & Cognition· 3 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Approach:experimental assay· 1 Assay:imaging· 1 Research Focus:synaptic & neural circuits· 1 Organism:zebrafish· 1

Frequent Co-Authors

E. Schulz· 2 shared S. J. Gershman· 2 shared A. D. Bolton· 1 shared D. Czegel· 1 shared D. Duvenaud· 1 shared E. Szathmary· 1 shared F. A. Saad· 1 shared F. Engert· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersAnimal Behavior & Cognition

E. Schulz

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Evolutionary implementation of Bayesian computations

D. Czegel, H. Giaffar, I. Zachar, J. B. Tenenbaum, E. Szathmary

This paper establishes an algorithmic analogy between Bayesian computations and evolutionary dynamics by showing their equivalent dynamical equations and mapping Bayesian concepts to evolutionary concepts.

bioRxiv · Jun 19, 2020 · PreprintEvolutionary Biology

A theory of learning to infer

I. Dasgupta, E. Schulz, J. B. Tenenbaum, S. J. Gershman

The paper proposes a theory explaining systematic deviations in human probabilistic inference by introducing a recognition model that adapts to query distribution.

bioRxiv · Oct 23, 2019Animal Behavior & Cognition

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

A. D. Bolton, M. Haesemeyer, J. Jordi, U. Schaechtle, F. A. Saad, V. K. Mansinghka, J. B. Tenenbaum, F. Engert

This study develops a 3D-imaging setup to analyze larval zebrafish prey capture, revealing a stochastic algorithm that enables predictive behavior and efficient hunting.

bioRxiv · Sep 5, 2019Animal Behavior & Cognition

Compositional Inductive Biases in Function Learning

E. Schulz, J. B. Tenenbaum, D. Duvenaud, M. Speekenbrink, S. J. Gershman

The paper shows humans prefer compositional structures in function learning, finding them more predictable and memorable than non-compositional ones.

Cognitive Psychology · Dec 1, 2017Peer-reviewedAnimal Behavior & Cognition