Modeling the Effects of Attentional Cueing on Meditators

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

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 with performance in the control condition (i.e., no meditation), mindfulness-based meditation (1) accelerated the rate of learning following positive prediction errors; (2) elicited a preference for the exploration (vs. exploitation) of choice selections; and (3) increased response caution. Collectively, these findings elucidate the pathways 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.

Knock yourself out: Brief mindfulness-based meditation eliminates self-prioritization

Recent research has asserted that self-prioritization is an inescapable facet of mental life, but is this viewpoint correct? Acknowledging the flexibility of social-cognitive functioning, here we considered the extent to which mindfulness-based meditation-an intervention known to reduce egocentric responding-attenuates self-bias. Across two experiments (Expt. 1, N = 160; Expt. 2, N = 160), using an object-classification task, participants reported the ownership of previously assigned items (i.e., owned-by-self vs. owned-by-friend) following a 5-minute period of mindfulness-based meditation compared with control meditation (Expt. 1) or no meditation (Expt. 2). The results revealed that mindfulness meditation abolished the emergence of the self-ownership effect during decision-making. An additional computational (i.e., drift diffusion model) analysis indicated that mindfulness meditation eliminated a prestimulus bias toward self-relevant (vs. friend-relevant) responses, increased response caution, and facilitated the rate at which evidence was accumulated from friend-related (vs. self-related) objects. Collectively, these findings elucidate the stimulus and response-related operations through which brief mindfulness-based meditation tempers self-prioritization.

Computational modelling approaches to meditation research: why should we care?

Computational modeling and meditation are not frequently mentioned in the same breath. However, in this article we argue that computational modeling can provide insights into the mechanisms by which meditation produces its effects on cognition. Moreover, computational modeling allows the researcher to make predictions about how effects of meditation will generalize to other contexts such as other tasks, which can be tested in subsequent experiments. In addition, computational theories can help to clarify similarities and differences between meditation practices, which is crucial for mapping out the space of contemplative practices. In short, even though computational modeling has not yet been used extensively, we think this approach can make important contributions to the field of meditation research.