Meditation experience predicts negative reinforcement learning and is associated with attenuated FRN amplitude

Abstract

Focused attention meditation (FAM) practices are cognitive control exercises where meditators learn to maintain focus and attention in the face of distracting stimuli. Previous studies have shown that FAM is both activating and causing plastic changes to the mesolimbic dopamine system and some of its target structures, particularly the anterior cingulate cortex (ACC) and striatum. Feedback-based learning also depends on these systems and is known to be modulated by tonic dopamine levels. Capitalizing on previous findings that FAM practices seem to cause dopamine release, the present study shows that FAM experience predicts learning from negative feedback on a probabilistic selection task. Furthermore, meditators exhibited attenuated feedback-related negativity (FRN) as compared with nonmeditators and this effect scales with meditation experience. Given that reinforcement learning and FRN are modulated by dopamine levels, a possible explanation for our findings is that FAM practice causes persistent increases in tonic dopamine levels which scale with amount of practice, thus altering feedback processing.

Short-term Focused Attention Meditation Restricts the Retrieval of Stimulus-Response Bindings to Relevant Information

“…Second, an experienced Buddhist meditator has shown specific increased activity in the anterior cingulate cortex (ACC) and in the striatum during FA compared with other meditative techniques (Hagerty et al 2013). Third, Knytl and Opitz (2019) have found that FA meditators, as compared with nonmeditators, exhibited attenuated feedback-related negativity, an electrophysiological component known to be affected by dopamine levels in the striatum and ACC. Hence, the studies outlined above imply a crucial activation of the nigro-striatal dopamine pathway when practicing FA meditation.…”

Increased Anxiety is Associated with Better Learning from Negative Feedback

“…The ERPs were pre-processed as described in Knytl and Opitz (2019) using BrainAnalyzer2 (Brain Products GmbH, Gilching, Germany). Data pre-processing consisted of a digital band-pass filter from 0.5 Hz to 30 Hz (−8 dB cut-off) to eliminate low-frequency signal drifts and high-frequency artifacts.…”