Chen, H.-T. | bioRxiv Science

bioRxiv2020

The Impact of Hand Movement Velocity on Cognitive Conflict Processing in a 3D Object Selection Task

Detecting and correcting incorrect body movements is an essential part of everyday interaction with ones environment. The human brain provides a monitoring system that constantly controls and adjusts our actions according to our surroundings. However, when our brains predictions about a planned action do not match the sensory inputs resulting from that action, cognitive conflict occurs. Much is known about cognitive conflict in 1D/2D environments; however, less is known about the role of movement characteristics associated with cognitive conflict in 3D environment. Hence, we devised an object selection task in a virtual reality (VR) environment to test how the velocity of hand movements impacts human brain responses. From a series of analyses of EEG recordings synchronized with motion capture, we found that the velocity of the participants hand movements modulated the brains response to proprioceptive feedback during the task and induced a prediction error negativity (PEN). Additionally, the PEN originates in the anterior cingulate cortex and is itself modulated by the ballistic phase of the hands movement. These findings suggest that velocity is an essential component of integrating hand movements with visual and proprioceptive information during interactions with real and virtual objects.

neuroscience↗

bioRxiv2020

Between-subject prediction reveals a shared representational geometry in the rodent hippocampus

The rodent hippocampus constructs statistically independent representations across environments ("global remapping") and assigns individual neuron firing fields to locations within an environment in an apparently random fashion, processes thought to contribute to the role of the hippocampus in episodic memory. This random mapping implies that it should be challenging to predict hippocampal encoding of a given experience in one subject based on the encoding of that same experience in another subject. Contrary to this prediction, we find that by constructing a common representational space across rats in which neural activity is aligned using geometric operations (rotation, reflection, and translation; "hyperalignment"), we can predict data of "right" trials (R) on a T-maze in a target rat based on 1) the "left" trials (L) of the target rat, and 2) the relationship between L and R trials from a different source rat. These cross-subject predictions relied on ensemble activity patterns including both firing rate and field location, and outperformed a number of control mappings, such as those based on permuted data that broke the relationship between L and R activity for individual neurons, and those based solely on within-subject prediction. This work constitutes proof-of-principle for successful cross-subject prediction of ensemble activity patterns in the hippocampus, and provides new insights in understanding how different experiences are structured, enabling further work identifying what aspects of experience encoding are shared vs. unique to an individual.

neuroscience↗