Neuronal Reward and Decision Signals: From Theories to Data

Abstract

LSchultz W. Neuronal Reward and Decision Signals: From Theories to Data. Physiol Rev 95: 853-951, 2015. Published June 24, 2015 doi:10.1152/physrev.00023.2014.-Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.

Motivated Cognition: Effects of Reward, Emotion, and Other Motivational Factors Across a Variety of Cognitive Domains

“…For instance, how much of what is known about the effects of emotion on memory can be considered domain-general characteristics of motivational salience and valence, rather than domain-specific effects of emotion? Along these lines, it is clear that emotion and reward, among other factors, necessitate unique research approaches (Gershman & Daw, 2017;Mattek et al, 2017;Panksepp et al, 2017;Schultz, 2015), but it is an open question where the boundaries lie between these different facets of motivation. More broadly, while the position of this perspective paper is that these factors can be summarized as 'motivational factors' despite a variety of differences-this is far from conclusive.…”

Memory-reliant Post-error Slowing Is Associated with Successful Learning and Fronto-occipital Activity

“…Reinforcement learning theory (Sutton & Barto, 1998) has been reliably used to explain human and animal behavior in tasks where one learns by trial-and-error associations that lead to maximizing reward (Schultz, 2015). These learning models rely on the measure of a prediction error, that is, how a given reward differs from what is expected.…”

Cross-hemispheric dopamine projections have functional significance

“…The magnitude and frequency of dopamine transients increase in response to drugs of abuse (12,13), which is thought to contribute to their reinforcing properties (14). Although numerous studies have summarized the function of dopamine circuits in reward-based behaviors (15,16) and motor control (17)(18)(19), anatomic descriptions of dopamine projections are conflicting (20)(21)(22)(23). Recent evidence suggests that some dopamine neurons project contralateral to their origin (22,23), contradictory to the uncrossed dopamine system described previously (20,21).…”