Inferring perceptual decision making parameters from behavior in production and reproduction tasks

TL;DR

This paper introduces a Bayesian inference method to estimate perceptual uncertainty, response variability, and effort-related cost functions from production and reproduction task data, enhancing understanding of decision-making processes.

Contribution

It presents a novel hybrid inference approach combining MCMC sampling with neural network-based amortized inference for parameter recovery in psychophysical tasks.

Findings

Successfully recovers parameters from synthetic data

Applies method to experimental data demonstrating practical utility

Addresses unidentifiability issues in experimental design

Abstract

Bayesian models of behavior have provided computational level explanations in a range of psychophysical tasks. One fundamental experimental paradigm is the production or reproduction task, in which subjects are instructed to generate an action that either reproduces a previously sensed stimulus magnitude or achieves a target response. This type of task therefore distinguishes itself from other psychophysical tasks in that the responses are on a continuum and effort plays an important role with increasing response magnitude. Based on Bayesian decision theory we present an inference method to recover perceptual uncertainty, response variability, and the cost function underlying human responses. Crucially, the cost function is parameterized such that effort is explicitly included. We present a hybrid inference method employing MCMC sampling utilizing appropriate proposal distributions and an inner loop utilizing amortized inference with a neural network that approximates the mode of the optimal response distribution. We show how this model can be utilized to avoid unidentifiability of experimental designs and that parameters can be recovered through validation on synthetic and application to experimental data. Our approach will enable behavioral scientists to perform Bayesian inference of decision making parameters in production and reproduction tasks.