The Neurobiology of Confidence: From Beliefs to Neurons

Abstract

How confident are you? As humans, aware of our subjective sense of confidence, we can readily answer. Knowing your level of confidence helps to optimize both routine decisions such as whether to go back and check if the front door was locked and momentous ones like finding a partner for life. Yet the inherently subjective nature of confidence has limited investigations by neurobiologists. Here, we provide an overview of recent advances in this field and lay out a conceptual framework that lets us translate psychological questions about subjective confidence into the language of neuroscience. We show how statistical notions of confidence provide a bridge between our subjective sense of confidence and confidence-guided behaviors in nonhuman animals, thus enabling the study of the underlying neurobiology. We discuss confidence as a core cognitive process that enables organisms to optimize behavior such as learning or resource allocation and that serves as the basis of metacognitive reasoning. These approaches place confidence on a solid footing and pave the way for a mechanistic understanding of how the brain implements confidence-based algorithms to guide behavior.

Summary: Order and Disorder in Brains and Behavior

“…In human studies, Daphna Shohamy presented behavioral strategies to separate episodic and reinforcement learning, Daniel Wolpert discussed statistical approaches to motor control, and Daniela Schiller described parametric tracking of social value. These sophisticated approaches are increasingly matched in nonhuman primates, such as studies of social value by Michael Platt, adolescent behavior in mice (Delevich et al 2019), and quantitative rodent behaviors presented by Anne Churchland, Bo Li, Kay Tye, and myself (Ott et al 2019).…”

Total recall: episodic memory retrieval, choice, and memory confidence in the rat

“…The first step, corresponding to a longstanding challenge in the study of memory confidence, was to identify an appropriate memory discriminability axis, or decision confidence variable 44 . In studies of perceptual confidence the relevant decision variable is typically defined by external task parameters (e.g., motion coherence; odor concentrations) where a simple monotonic relationship between the task parameter and task difficulty can be demonstrated 17 . Alternatively, in the context of value-based decisions, the decision variable is often inferred using a modelbased approach that posits a concrete computational model to explain choice behavior 45 .…”

“…A statistical framework that formally defines confidence and its signatures 14,16,17 has established a correspondence between statistical confidence in perceptions and the subjective sense of human confidence 18 , and enabled the identification of behavioral and neural confidence markers in species including macaques 10,19 , pre-verbal infants 20 , and rats 21,22 .…”

“…Overall, studies of confidence have focused primarily on information perceived from the external world ( e.g., motion detection, odor discrimination), reporting confidence-related behaviors across multiple species including dolphins 7 , non-human primates 8–12 , honey bees 13 , and rats 14,15 . A statistical framework that formally defines confidence and its signatures 14,16,17 has established a correspondence between statistical confidence in perceptions and the subjective sense of human confidence 18 , and enabled the identification of behavioral and neural confidence markers in species including macaques 10,19 , pre-verbal infants 20 , and rats 21,22 .…”

Confidence-controlled Hebbian learning efficiently extracts category membership from stimuli encoded in view of a categorization task

“…In order to elucidate the neural basis of confidence, researchers have focused on simplest experimental protocols, allowing to combine experimental and theoretical approaches (see e.g. [Ott et al, 2019]). Within the framework of drift-diffusion models and taking a Bayesian viewpoint, Drugowitsch et al [2019] have shown that the optimal learning rate for categorization tasks should depend on the confidence in one's decision, where confidence is defined as the probability of having answered correctly.…”