Brief mindfulness-based meditation enhances the speed of learning following positive prediction errors

Marius Golubickis* (Corresponding Author), Lucy Tan, Parnian Jalalian, Johanna Katariina Falben, Neil Macrae

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Recent research has demonstrated that mindfulness-based meditation facilitates basic aspects of cognition, including memory and attention. Further developing this line of inquiry, here we considered the possibility that similar effects may extend to another core psychological process — instrumental learning. To explore this matter, in combination with a probabilistic selection task, computational modelling (i.e., Reinforcement Drift Diffusion Model analysis) was adopted to establish whether and how brief mindfulness-based meditation influences learning under conditions of uncertainty (i.e., choices based on the perceived likelihood of positive and negative outcomes). Three effects were observed. Compared to performance in the control condition (i.e., no meditation), mindfulness-based meditation: (i) accelerated the rate of learning following positive prediction errors; (ii) elicited a preference for the exploration (vs. exploitation) of choice selections; and (iii) increased response caution. Collectively, these findings elucidate the operations through which brief meditative experiences impact learning and decision-making, with implications for interventions designed to debias aspects of social-cognitive functioning using mindfulness-based meditation.
Original languageEnglish
Pages (from-to)2312–2324
Number of pages13
JournalQuarterly Journal of Experimental Psychology
Volume77
Issue number11
Early online date16 Jan 2024
DOIs
Publication statusPublished - Nov 2024

Data Availability Statement

The data from the present experiment are publicly available at the Open Science Framework website: https://osf.io/xud5c/

Keywords

  • probabilistic learning
  • mindfulness-based meditation
  • prediction errors
  • computational modelling

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