Two decades of pharmacologic research on the human capacity to implicitly acquire knowledge as well as cognitive skills and procedures have yielded surprisingly few conclusive insights. We review the empirical literature of the neuropharmacology of implicit learning. We evaluate the findings in the context of relevant computational models related to neurotransmittors such as dopamine, serotonin, acetylcholine and noradrenalin. These include models for reinforcement learning, sequence production, and categorization. We conclude, based on the reviewed literature, that one can predict improved implicit acquisition by moderately elevated dopamine levels and impaired implicit acquisition by moderately decreased dopamine levels. These effects are most prominent in the dorsal striatum. This is supported by a range of behavioral tasks in the empirical literature. Similar predictions can be made for serotonin, although there is yet a lack of support in the literature for serotonin involvement in classical implicit learning tasks. There is currently a lack of evidence for a role of the noradrenergic and cholinergic systems in implicit and related forms of learning. GABA modulators, including benzodiazepines, seem to affect implicit learning in a complex manner and further research is needed. Finally, we identify allosteric AMPA receptors modulators as a potentially interesting target for future investigation of the neuropharmacology of procedural and implicit learning.
Keywords: Implicit learning, procedural learning, neurotransmittors, neuromodulators, dopamine, serotonin, acetylcholine, noradrenalin, GABA, glutamate, Neuropharmacology, NMDA, ampakines, frontal cortex, basal ganglia, medial temporal lobe, amnesic patients, skill learning, probabilistic learning, NEURO-ANATOMY, nucleus accumbens, ventral striatum, prefrontal cortex, nigrostriatal DA system, euphoria, psychosis, phrenia, dorsal striatum, Parkinson's disease, tubero-infudibular DA system, substantia nigra, aminergic brainstem, cerebellum, hippo-campus, mesencephalo-tegmental complex, Alzheimer's disease, computational model, Sejnowskij's model, Frank's model, cholinergic systems, cortico-cortical pathways, antipsychotic olanzapine, Haloperidol, antagonist, D2-antagonist, D-amphetamine, L-dopa, Citalopram, tryptophan, BENZODIAZEPINES
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