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10.14814/phy2.12422

http://scihub22266oqcxt.onion/10.14814/phy2.12422
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C4600370!4600370!26416969
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suck abstract from ncbi


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pmid26416969      Physiol+Rep 2015 ; 3 (9): ä
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  • Human substantia nigra neurons encode decision outcome and are modulated by categorization uncertainty in an auditory categorization task #MMPMID26416969
  • McGovern RA; Chan AK; Mikell CB; Sheehy JP; Ferrera VP; McKhann GM
  • Physiol Rep 2015[Sep]; 3 (9): ä PMID26416969show ga
  • The ability to categorize stimuli ? predator or prey, friend or foe ? is an essential feature of the decision-making process. Underlying that ability is the development of an internally generated category boundary to generate decision outcomes. While classic temporal difference reinforcement models assume midbrain dopaminergic neurons underlie the prediction error required to learn boundary location, these neurons also demonstrate a robust response to nonreward incentive stimuli. More recent models suggest that this may reflect a motivational aspect to performing a task which should be accounted for when modeling dopaminergic neuronal behavior. To clarify the role of substantia nigra dopamine neurons in uncertain perceptual decision making, we investigated their behavior using single neuron extracellular recordings in patients with Parkinson's disease undergoing deep brain stimulation. Subjects underwent a simple auditory categorical decision-making task in which they had to classify a tone as either low- or high-pitched relative to an explicit threshold tone and received feedback but no reward. We demonstrate that the activity of human SN dopaminergic neurons is predictive of perceptual categorical decision outcome and is modulated by uncertainty. Neuronal activity was highest during difficult (uncertain) decisions that resulted in correct responses and lowest during easy decisions that resulted in incorrect responses. This pattern of results is more consistent with a ?motivational? role with regards to perceptual categorization and suggests that dopamine neurons are most active when critical information ? as represented by uncertainty ? is available for learning decision boundaries.
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