AG Heinzelmann, 20.12.2024 Decomposing Motivation

Humans and other animals approach reward and avoid punishment and pay attention to cues predicting these events. Such motivated behavior thus appears to be guided by value, which directs behavior towards or away from positively or negatively valenced outcomes. Moreover, it is facilitated by (top-down) salience, which enhances attention to behaviorally relevant learned cues predicting the occurrence of valenced outcomes. Using human neuroimaging, we recently separated value (ventral striatum, posterior ventromedial prefrontal cortex) from salience (anterior ventromedial cortex, occipital cortex) in the domain of liquid reward and punishment. Moreover, we investigated potential drivers of learned salience: the probability and uncertainty with which valenced and non-valenced outcomes occur. We find that the brain dissociates valenced from non-valenced probability and uncertainty, which indicates that reinforcement matters for the brain, in addition to information provided by probability and uncertainty alone, regardless of valence. Finally, we assessed learning signals (unsigned prediction errors) that may underpin the acquisition of salience. Particularly the insula appears to be central for this function, encoding a subjective salience prediction error, similarly at the time of positively and negatively valenced outcomes. However, it appears to employ domain-specific time constants, leading to stronger salience signals in the aversive than the appetitive domain at the time of cues. These findings explain why previous research associated the insula with both valence-independent salience processing and with preferential encoding of the aversive domain. More generally, the distinction of value and salience appears to provide a useful framework for capturing the neural basis of motivated behavior.

Phillipe Tobler

Dr. Philippe Tobler is a Professor of Neuroeconomics and Social Neuroscience at the University of Zurich in the Department of Economics. His research focuses on the neural mechanisms behind decision-making and reward learning. After completing a PhD on dopamine neurons' role in reward processing, his postdoctoral work used fMRI to explore how the brain processes economic reward factors such as risk, delay, and probability. He currently investigates the neural basis of reward, learning, economic decisions, and social behavior, with a special interest in structures like the dopaminergic midbrain, the striatum, and the prefrontal cortex.

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