Tagged: Confidence

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain

Abstract Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

Neuro-Robotic Technologies and Social Interactions

The current bandwidth for understanding cognitive and emotional context of a person is much more limited between robots and humans than among humans. Advances in human sensing technologies over the past two decades hold promise for providing online and unique information sources that can lead to deeper insights into human cognitive and emotional state than are currently attainable. However, blind application of the human sensing technologies alone is not a solution. Here, we focus on the integration of neuroscience with robotic technologies for improving social interactions. We discuss the issue of uncertainty in human state detection and the need to develop approaches to estimate and integrate knowledge of that uncertainty. We illustrate this by discussing two application areas and the potential neuro-robotic technologies that could be developed within them