Human Inference in Changing Environments With Temporal Structure

TL;DR

Humans adapt their inference strategies based on the temporal structure of stimuli in changing environments, behaving approximately Bayesian but with variability that is modulated by stimulus statistics, best explained by sampling-based models.

Contribution

This study demonstrates that humans modify their inference processes according to temporal stimulus structure and that their variability aligns with sampling-based Bayesian approximations.

Findings

Humans adapt inference to stimulus temporal structure.

Behavior deviates from optimal Bayesian inference.

Sampling models best explain human inference variability.

Abstract

To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet temporal structure is everywhere in nature, and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion, but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with 'output noise' that corrupts the response-selection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans.