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Πέμπτη 2 Νοεμβρίου 2017

The application of reward learning in the real world: Changes in the reward positivity amplitude reflect learning in a medical education context

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Publication date: Available online 27 October 2017
Source:International Journal of Psychophysiology
Author(s): Chad C. Williams, Kent G. Hecker, Michael K. Paget, Sylvain P. Coderre, Kelly W. Burak, Bruce Wright, Olave E. Krigolson
Evidence ranging from behavioural adaptations to neurocognitive theories has made significant advances into our understanding of feedback-based learning. For instance, over the past twenty years research using electroencephalography has demonstrated that the amplitude of a component of the human event-related brain potential – the reward positivity – appears to change with learning in a manner predicted by reinforcement learning theory (Holroyd and Coles, 2002; Sutton and Barto, 1998). However, while the reward positivity (also known as the feedback related negativity) is well studied, whether the component reflects an underlying learning process or whether it is simply sensitive to feedback evaluation is still unclear. Here, we sought to provide support that the reward positivity is reflective of an underlying learning process and further we hoped to demonstrate this in a real-world medical education context. In the present study, students with no medical training viewed a series of patient cards that contained ten physiological readings relevant for diagnosing liver and biliary disease types, selected the most appropriate diagnostic classification, and received feedback as to whether their decisions were correct or incorrect. Our behavioural results revealed that our participants were able to learn to diagnose liver and biliary disease types. Importantly, we found that the amplitude of the reward positivity diminished in a concomitant manner with the aforementioned behavioural improvements. In sum, our data support theoretical predictions (e.g., Holroyd and Coles, 2002), suggest that the reward positivity is an index of a neural learning system, and further validate that this same system is involved in learning across a wide range of contexts.



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