{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/disentangled-behavioural-representations","title":"Disentangled behavioural representations","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Amir Dezfouli","Hassan Ashtiani","Omar Ghattas","Richard Nock","Peter Dayan","Cheng Soon Ong"],"abstract":"Individual characteristics in human decision-making are often\n  quantified by fitting a parametric cognitive model to subjects'\n  behavior and then studying differences between them in the associated\n  parameter space.  However, these models often fit behavior more poorly\n  than recurrent neural networks (RNNs), which are more flexible and\n  make fewer assumptions about the underlying decision-making processes.\n  Unfortunately, the parameter and latent activity spaces of RNNs are\n  generally high-dimensional and uninterpretable, making it hard to use\n  them to study individual differences.  Here, we\n  show how to benefit from the flexibility of RNNs while representing\n  individual differences in a low-dimensional and interpretable space.\n  To achieve this, we propose a novel end-to-end learning framework in\n  which an encoder is trained to map the behavior of subjects into a\n  low-dimensional latent space. These low-dimensional representations\n  are used to generate the parameters of individual RNNs corresponding\n  to the decision-making process of each subject.  We introduce terms\n  into the loss function that ensure that the latent dimensions are\n  informative and disentangled, i.e.,\nencouraged to have distinct effects on behavior. This allows them to\nalign with separate facets of\n  individual differences. We illustrate the performance\n   of our framework on synthetic data as well as a dataset including the behavior\n   of patients with psychiatric disorders.","url_abs":"http://papers.nips.cc/paper/8497-disentangled-behavioural-representations","url_pdf":"http://papers.nips.cc/paper/8497-disentangled-behavioural-representations.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"disentangled-behavioural-representations","repo_url":"https://github.com/adezfouli/rnn_hypercoder","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}