Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion.

An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology.

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.

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.