Chemical neuromodulation of higher cognitive function

The goal of the Motivational and Cognitive Control lab is to understand the mental processes, computations, and neural mechanisms that enable and motivate us to act in accordance with our goals. We approach this question by studying effects of challenging the major ascending chemical neuromodulators, such as dopamine, noradrenaline and serotonin. These different systems have diffuse ramifications throughout cortical and subcortical regions that enable them to exert a global influence on brain function. However, they are now also well established to modulate dissociable functions, in part reflecting their innervation of distinct target regions. To study these effects, we combine psychopharmacology, functional MRI, neurochemical PET and computational cognitive modelling in human volunteers with and without neuropsychiatric disorders.

Cognitive Control and Working Memory


Cognitive flexibility versus stability: Role of dopamine (and noradrenaline)

The brain catecholamines dopamine (and noradrenaline) play important roles in complex cognitive functions such as working memory. This somewhat ill-defined term generally refers to the ‘on-line’ stabilization of task-relevant representations, but often also implies flexible updating of those representations in response to novel information. A dynamic task-dependent balance between these opponent functions is critically important for a wide range of cognitive abilities such as reasoning, language comprehension, planning, and spatial processing and has been associated most commonly with circuits connecting the prefrontal cortex with the striatum. One major goal of our group is to unravel the roles of the catecholamines in cognitive flexibility and in its tradeoff with cognitive stability.


Reinforcement Learning and Decision Making


Reversal learning and Compulsivity: Role of Dopamine

Brain dopamine is probably best known for its implication in reinforcement learning. We study the contribution of dopamine to not only reward, but also punishment learning, in the context of reversal learning paradigms, as models of compulsivity. Indeed dopamine-related compulsivity, as seen in addiction or in medicated Parkinson’s disease, has often been attributed to dopamine-induced increases in the weight on the benefits versus the costs of actions. In the lab, we combine psychopharmacology with computational reinforcement learning modeling, genetics, electrophysiology and/or neuroimaging (EEG, PET and fMRI) to increase our understanding of the paradoxical relationship between dopamine and compulsivity.


Motivational Control


Motivation of Cognitive Control: Role of Dopamine

Our third line of research represents an integration of our first and second lines of work: Dopamine’s dual roles in cognitive control and in value computation lead to the obvious next question whether value-based decisions about whether or not to exert cognitive control also depend on dopamine transmission.

An answer to this question would begin to address why people so often fail to exert cognitive control. Resource allocation accounts have shifted attention from capacity limitation to motivation. According to these accounts, cognitive control comes not only with benefits, but also with a cost based on which people (learn to) decide to avoid exerting control. However, the nature of this cost of cognitive control is unclear. In ongoing work, we are studying the origins of both the cost and benefits of cognitive control: What makes some people cognition avoidant, but others cognition seeking?


Motivational Biases of Behaviour: Role of Serotonin versus Dopamine

When we see a threat, we tend to hold back. When we see a reward, we have a strong urge to approach. Reward or punishment cues bias action, eliciting appetitive activation and/or aversive inhibition, respectively. Such motivational biases of behavior are often considered to reflect cue-based, ‘Pavlovian’ effects, which arise as hardwired responses to learned stimulus-associated outcome predictions. We study the role of catecholamine as well as serotonin transmission in these motivational action biases, which we have shown can in fact arise also from biases in instrumental learning (Swart et al., 2019). Specifically we have tested the motivational opponency hypothesis, according to which serotonin and dopamine play key roles in linking so-called Pavlovian aversive and appetitive predictions with behavioral inhibition and activation, respectively. Moreover, we study the degree to which such motivational biases can transfer to more cognitive learning systems (Piray et al. 2019).



A fundamental cognitive motivation is the drive to seek information: curiosity. What makes us curious? To what degree is information seeking behavior independent from our basic drive to maximize reward and minimize punishment? Together with Floris de Lange, we study non-instrumental curiosity, a form of curiosity that, when relieved, is not associated with primary reward. We use both self-report and willingness-to-wait measures to assess participants’ curiosity. So far we have shown that curiosity is a function of both the uncertainty and the expected value of an outcome.


The efficiency and flexibility with which we infer (or generate) meaning during language comprehension (or production) is remarkable. How does our brain do it? In a new line of research, embedded within the Language in Interaction consortium, we will treat linguistic inference as a multi-step, sequential choice problem, such as those that we have faced in other cognitive domains (e.g. chess, foraging and spatial navigation). Specifically, we anticipate to make unique progress in unraveling the mechanisms of fast, flexible linguistic inference by leveraging recent major advances in our understanding of the representations and computations necessary for sequential model-based action planning. This approach will also lead us to revise current dual-system dogma’s in non-linguistic domains, that have commonly over-focused on the contrast between a cognitive (flexible, but slow) and a habitual (fast, but inflexible) system: The current quest will encourage the integration of so-called ‘cognitive habits’ and their associated cognitive map-related neural mechanisms into theoretical models of both linguistic and nonlinguistic inference.


Knowledge of the cognitive functions of the major neurotransmitters significantly advances the field of computational psychiatry and neurology, which aim to bridge the gap between neuroscience, neurology and psychiatry by elucidating the cognitive computations underlying (mal)adaptive behaviors. Indeed, many of the major neuropsychiatric and neurological disorders are cognitive in nature. Definition of neuropsychiatric abnormality in terms of its cognitive mechanism is increasingly recognized to be important for diagnosis, prognosis and optimizing treatment development. We contribute to this, either within the group, or in collaborative efforts, by studying motivational and cognitive control in Parkinson’s disease, gambling addiction, eating disorder, ADHD, depression, anxiety, psychopathy, gambling addiction and schizophrenia.


Computational Psychiatry and Neurology