Learning signatures of decision making from many individuals playing the same game

Michael J. Mendelson, Mehdi Azabou, Suma Jacob, Nicola Grissom, David Darrow, Becket Ebitz, Alexander Herman, Eva L. Dyer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.

Original languageEnglish (US)
Title of host publication11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781665462921
StatePublished - 2023
Event11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Baltimore, United States
Duration: Apr 25 2023Apr 27 2023

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


Conference11th International IEEE/EMBS Conference on Neural Engineering, NER 2023
Country/TerritoryUnited States

Bibliographical note

Funding Information:
*Both first authors (mmendelson3@gatech.edu, mazabou@gatech.edu) contributed equally. † Both senior authors (evadyer@gatech.edu, herma686@umn.edu) contributed equally. This work was supported by NIH awards 1R01EB029852-01, R21HM127607, and K23MH050909, NSF award IIS-2039741, and the McKnight Foundation.

Publisher Copyright:
© 2023 IEEE.


  • Human decision making
  • behavior analysis
  • contrastive learning
  • multi-arm bandit task
  • self-supervised learning


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