Human inference in changing environments with temporal structure.

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.

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

“…In the laboratory, their behavior is often at best qualitatively Bayesian but quantitatively suboptimal. For example, although they adjust their effective learning rate to changes, the base value of their learning rate and their dynamic adjustments may depart from the optimal values (Nassar et al, 2010(Nassar et al, , 2012Prat-Carrabin et al, 2021).…”

“…They may also not update their prediction on every trial, unlike the optimal solution (Gallistel et al, 2014;Khaw et al, 2017). Finally, there is substantial interindividual variability which does not exist in the optimal solution (Khaw et al, 2021;Nassar et al, 2010Nassar et al, , 2012Prat-Carrabin et al, 2021). In the future, these suboptimalities could be explored using our networks by making them suboptimal in three ways (among others): by stopping training before quasi-optimal performance is reached (Caucheteux & King, 2021;Orhan & Ma, 2017), by constraining the size of the network or its weights (with hard constraints or with regularization penalties) (Mastrogiuseppe & Ostojic, 2017;Sussillo et al, 2015), or by altering the network in a certain way, such as pruning some of the units or some of the connections (Blalock et al, 2020;Chechik et al, 1999;LeCun et al, 1990;Srivastava et al, 2014), or introducing random noise into the activity (Findling et al, 2021;Findling & Wyart, 2020;Legenstein & Maass, 2014).…”

Resource-Rational Account of Sequential Effects in Human Prediction

“…Finally, the deviations from perfect inference, in the precision-cost model, originate in the constraints faced by the brain when performing computation with probability distributions. In spite of the success of the Bayesian framework, we note that human performance in various inference tasks is often suboptimal [30, 55, 97, 98]. Our approach suggests that the deviations from optimality in these tasks may be explained by the cognitive constraints at play in the inference carried out by humans.…”

“…In spite of the success of the Bayesian framework, we note that human performance in various inference tasks is often suboptimal [30,55,97,98]. Our approach suggests that the deviations from optimality in these tasks may be explained by the cognitive constraints at play in the inference carried out by humans.…”

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

“…behavior is often at best qualitatively Bayesian but quantitatively suboptimal. For example, although they adjust their effective learning rate to changes, the base value of their learning rate and their dynamic adjustments may depart from the optimal values (Nassar et al, 2010(Nassar et al, , 2012Prat-Carrabin et al, 2021). They may also not update their prediction on every trial, unlike the optimal solution (Gallistel et al, 2014;Khaw et al, 2017).…”