English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Disentangled behavioural representations

Dezfouli, A., Ashtiani, H., Ghattas, O., Nock, R., Dayan, P., & Ong, C. (2020). Disentangled behavioural representations. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (pp. 2243-2252). Red Hook, NY, USA: Curran.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0004-A121-0 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-11AA-3
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
http://papers.nips.cc/paper/8497-disentangled-behavioural-representations.pdf (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Dezfouli, A, Author
Ashtiani, H, Author
Ghattas, O, Author
Nock, R, Author
Dayan, P1, 2, Author           
Ong, CS, Author           
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects' behavior and then studying differences between them in the associated parameter space. However, these models often fit behavior more poorly than recurrent neural networks (RNNs), which are more flexible and make fewer assumptions about the underlying decision-making processes. Unfortunately, the parameter and latent activity spaces of RNNs are generally high-dimensional and uninterpretable, making it hard to use them to study individual differences. Here, we show how to benefit from the flexibility of RNNs while representing individual differences in a low-dimensional and interpretable space. To achieve this, we propose a novel end-to-end learning framework in which an encoder is trained to map the behavior of subjects into a low-dimensional latent space. These low-dimensional representations are used to generate the parameters of individual RNNs corresponding to the decision-making process of each subject. We introduce terms into the loss function that ensure that the latent dimensions are informative and disentangled, i.e., encouraged to have distinct effects on behavior. This allows them to align with separate facets of individual differences. We illustrate the performance of our framework on synthetic data as well as a dataset including the behavior of patients with psychiatric disorders.

Details

show
hide
Language(s):
 Dates: 2019-122020-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
Place of Event: Vancouver, Canada
Start-/End Date: 2019-12-09 - 2019-12-13

Legal Case

show

Project information

show

Source 1

show
hide
Title: Advances in Neural Information Processing Systems 32
Source Genre: Proceedings
 Creator(s):
Wallach, H, Editor
Larochelle, H, Editor
Beygelzimer , A, Editor
d'Alché-Buc, F, Editor
Fox, E, Editor
Garnett, R, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2243 - 2252 Identifier: ISBN: 978-1-7138-0793-3