TL;DR
This paper introduces Gibbs Sampling with People (GSP), a continuous-sampling method that improves upon traditional MCMCP by enabling more efficient exploration of perceptual spaces through participant-driven stimulus manipulation.
Contribution
The paper generalizes MCMCP to a continuous paradigm called GSP, allowing more efficient and interpretable sampling of perceptual representations with human participants.
Findings
GSP outperforms traditional MCMCP in initial tests.
GSP yields novel insights in musical, emotional, and facial perception domains.
GSP successfully navigates high-dimensional perceptual spaces like StyleGAN latent space.
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…
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