Ready to help, no matter what you did: Responsibility attribution does not influence compassion in expert Buddhist practitioners.

Within western social psychology and neuroscience, compassion is described as being conditioned by cost-benefit appraisals, such as the attribution of responsibility for the causes of suffering. Buddhist traditions maintain the possibility of cultivating and embodying unconditioned and universal forms of compassion. Whereas a growing body of empirical literature suggests that Buddhist-inspired compassion-based programs foster prosociality and well-being in healthy and clinical populations, there is no evidence that such compassionate disposition toward others can become unconditioned from moral judgment. To address this question, we collected and integrated self-report and behavioral data from expert Buddhist practitioners and trained novices using a previously validated within-subject experiment that manipulates contextual information to influence moral judgment toward suffering others and a newly designed approach-avoidance task. We found that context manipulation impacted responsibility and blame attribution in both groups and that experts' reported willingness to help was higher and less influenced by context, compared to novices. Partial correlation networks highlighted a negative relationship between blame attribution and willingness to help in novices, but not in expert practitioners. Self-reported willingness to help was correlated to reaction times when approaching suffering stimuli. Approach behavior was modulated by context in novice, but not in experts. This study provides initial evidence of a dissociation between moral attributions and prosocial attitude in expert Buddhist practitioners and challenges established evolutionary accounts of compassion in western psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Beyond kindness: a proposal for the flourishing of science and scientists alike.

We argue that many of the crises currently afflicting science can be associated with a present failure of science to sufficiently embody its own values. Here, we propose a response beyond mere crisis resolution based on the observation that an ethical framework of flourishing derived from the Buddhist tradition aligns surprisingly well with the values of science itself. This alignment, we argue, suggests a recasting of science from a competitively managed activity of knowledge production to a collaboratively organized moral practice that puts kindness and sharing at its core. We end by examining how Flourishing Science could be embodied in academic practice, from individual to organizational levels, and how that could help to arrive at a flourishing of scientists and science alike.

No effect of focused attention and open monitoring meditation on EEG auditory mismatch negativity in expert and novice practitioners.

The auditory mismatch negativity (MMN) is a well characterized event-related potential component which has gained recent attention in theoretical models describing the impact of various styles of mindfulness meditation on attentional processes and perceptual inference. Previous findings highlighted a distinct modulation of the MMN amplitude by different meditation practices and degrees of expertise. In the present study, we attempted to replicate results from the recent literature with a data sample that allowed for increased statistical power compared to previous experiments. Relying on traditional frequentist analysis, we found no effect of focused attention and open monitoring meditation on the auditory MMN amplitude compared to a control condition (silent movie) in expert or novice practitioners (all p > 0.17), providing a non-replication of our previous work (Fucci et al. 2018). Using a Bayesian approach, we found strong evidence against an interaction effect on the MMN amplitude between expertise groups and meditation practices (BF01 = 11.0), strong evidence against effects of either meditation practices compared to the control condition (BF01 between 11.9 and 16.1) and moderate evidence against an effect of expertise during meditation (BF01 between 5.3 and 7.9). On the other hand, we replicated previous evidence of increased alpha oscillatory power during meditation practices compared to a control state (p < 0.001). We discuss our null findings in relation to factors that could undermine the replicability of previous research on this subject, namely low statistical power, use of flexible analysis methods and a possible publication bias leading to a misrepresentation of the available evidence.

Training novice practitioners to reliably report their meditation experience using shared phenomenological dimensions.

Empirical descriptions of the phenomenology of meditation states rely on practitioners' ability to provide accurate information on their experience. We present a meditation training protocol that was designed to equip naive participants with a theoretical background and experiential knowledge that would enable them to share their experience. Subsequently, novices carried on with daily practice during several weeks before participating in experiments. Using a neurophenomenological experiment designed to explore two different meditation states (focused attention and open monitoring), we found that self-reported phenomenological ratings (i) were sensitive to meditation states, (ii) reflected meditation dose and fatigue effects, and (iii) correlated with behavioral measures (variability of response time). Each of these effects was better predicted by features of participants' daily practice than by desirable responding. Our results provide evidence that novice practitioners can reliably report their experience along phenomenological dimensions and warrant the future investigation of this training protocol with a longitudinal design.