Inference as a fundamental process in behavior

Highlights

• Inference is a ubiquitous process in cognition and behavior.

• State inference optimizes behavioral flexibility and stability under uncertainty.

• The neural mechanisms underlie different aspects of hidden state inference.

• Inference provides a framework for hierarchically organized decision making.

In the real world, uncertainty is omnipresent due to incomplete or noisy information. This makes inferring the state-of-the-world difficult. Furthermore, the state-of-the-world often changes over time, though with some regularity. This makes learning and decision-making challenging. Organisms have evolved to take advantage of environmental regularities, that allow organisms to acquire a model of the world and perform model-based inference to robustly make decisions and adjust behavior efficiently under uncertainty. Recent research has shed light on many aspects of model-based inference and its neural underpinnings. Here we review recent progress on hidden-state inference, state transition inference, and hierarchical inference processes.

Introduction

In a changing environment, learning, decision-making and cognitive control are critical functions for adaptive behavior under uncertainty. Making decisions involves integrating multiple pieces of information from multiple sources with varying degrees of certainty. When a given decision invariably produces the same outcome, inferring the consequences of choices is straightforward. In the real world, however, uncertainty is common because the information available to a decision-making agent is typically incomplete or hidden by noise.

When facing a novel environment, two scenarios are possible: a) the animal has to learn environmental contingencies from scratch, repeatedly sampling noisy information to form associations between choices and corresponding outcomes, using model-free reinforcement learning strategies to drive decision making [1]; or b) some prior information is available, allowing the animal to infer various environmental features using model-based learning strategies. Because environmental regularities occur, and these can be used to build priors, actively making inferences about the state-of-the-world is often the best solution.

Inference from incomplete information occurs at multiple levels of cognition. At the perceptual level, percepts are formed by combining noisy or incomplete sensory information with prior beliefs (i.e. models), acquired through experience, to infer features of a sensory object. Inference increases processing speed and reduces the energy necessary to make perceptual decisions [2,3]. Moreover, perceptual errors (e.g. hallucinations in mental disease) have been associated with faulty perceptual inference [4].

In higher cognition, animals form beliefs about the world from environmental regularities and use them to infer future outcomes and optimize decision-making. Bayesian-like computations, that combine prior probability distributions with currently available information are used to make inferences, though typically in a (mathematically) suboptimal way [5,6]. Here we review recent progress regarding the neural underpinnings of inference in decision making, focusing on state inference, state-transition inference, and hierarchically organized inference processes.