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  Gibbs sampling with people

Harrison, P. M. C., Marjieh, R., Adolfi, F., van Rijn, P., Anglada-Tort, M., Tchernichovski, O., et al. (2021). Gibbs sampling with people. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, & H. Lin (Eds.), 34th Conference on Neural Information Processing Systems (NeurIPS 2020) (pp. 10659-10671). Red Hook, NY: Curran Associates. doi:10.17605/OSF.IO/RZK4S.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-AB77-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-DD92-8
Genre: Conference Paper

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 Creators:
Harrison, Peter M. C.1, Author           
Marjieh, Raja1, Author           
Adolfi, Federico2, Author           
van Rijn, Pol2, Author           
Anglada-Tort, Manuel1, Author           
Tchernichovski, Ofer3, Author
Larrouy-Maestri, Pauline2, Author           
Jacoby, Nori1, Author           
Affiliations:
1Research Group Computational Auditory Perception, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_3024247              
2Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421697              
3Hunter College CUNY, The CUNY Graduate Center, ou_persistent22              

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 Abstract: A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications.

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Language(s): eng - English
 Dates: 2020-04-152021
 Publication Status: Issued
 Pages: -
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 Rev. Type: Internal
 Identifiers: DOI: 10.17605/OSF.IO/RZK4S
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Title: Conference on Neural Information Processing Systems ; 34
Place of Event: Online
Start-/End Date: 2020-12-06 - 2020-12-12

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Title: 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Source Genre: Proceedings
 Creator(s):
Larochelle , H. , Editor
Ranzato, M., Editor
Hadsell, R., Editor
Balcan, M.F. , Editor
Lin, H. , Editor
Affiliations:
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Publ. Info: Red Hook, NY : Curran Associates
Pages: - Volume / Issue: 2 Sequence Number: 894 Start / End Page: 10659 - 10671 Identifier: ISBN: 978-1-7138-2954-6

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Title: Advances in neural information processing systems
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Pages: - Volume / Issue: 33 Sequence Number: - Start / End Page: - Identifier: -