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Bayesian models of behavior have provided computational level explanations ina range of psychophysical tasks. One fundamental experimental paradigm is theproduction or reproduction task, in which subjects are instructed to generatean action that either reproduces a previously sensed stimulus magnitude orachieves a target response. This type of task therefore distinguishes itselffrom other psychophysical tasks in that the responses are on a continuum andeffort plays an important role with increasing response magnitude. Based onBayesian decision theory we present an inference method to recover perceptualuncertainty, response variability, and the cost function underlying humanresponses. Crucially, the cost function is parameterized such that effort isexplicitly included. We present a hybrid inference method employing MCMCsampling utilizing appropriate proposal distributions and an inner looputilizing amortized inference with a neural network that approximates the modeof the optimal response distribution. We show how this model can be utilized toavoid unidentifiability of experimental designs and that parameters can berecovered through validation on synthetic and application to experimental data.Our approach will enable behavioral scientists to perform Bayesian inference ofdecision making parameters in production and reproduction tasks.

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