Touching events predict human action segmentation in brain and behavior

Recognizing the actions of others depends on segmentation into meaningful events. After decades of research in this area, it remains still unclear how humans do this and which brain areas support underlying processes. Here we show that a computer vision-based model of touching and untouching events can predict human behavior in segmenting object manipulation actions with high accuracy. Using this computational model and functional Magnetic Resonance Imaging (fMRI), we pinpoint the neural networks underlying this segmentation behavior during an implicit action observation task. Segmentation was announced by a strong increase of visual activity at touching events followed by the engagement of frontal, hippocampal and insula regions, signaling updating expectation at subsequent untouching events. Brain activity and behavior show that touching-untouching motifs are critical features for identifying the key elements of actions including object manipulations.

Is there a prediction network? Meta-analytic evidence for a cortical-subcortical network likely subserving prediction

Predictive coding is an increasingly influential and ambitious concept in neuroscience viewing the brain as a 'hypothesis testing machine' that constantly strives to minimize prediction error, the gap between its predictions and the actual sensory input. Despite the invaluable contribution of this framework to the formulation of brain function, its neuroanatomical foundations have not been fully defined. To address this gap, we conducted activation likelihood estimation (ALE) meta-analysis of 39 neuroimaging studies of three functional domains (action perception, language and music) inherently involving prediction. The ALE analysis revealed a widely distributed brain network encompassing regions within the inferior and middle frontal gyri, anterior insula, premotor cortex, pre-supplementary motor area, temporoparietal junction, striatum, thalamus/subthalamus and the cerebellum. This network is proposed to subserve domain-general prediction and its relevance to motor control, attention, implicit learning and social cognition is discussed in light of the predictive coding scheme. Better understanding of the presented network may help advance treatments of neuropsychiatric conditions related to aberrant prediction processing and promote cognitive enhancement in healthy individuals.