Visually guided movements can show surprising accuracy even when the perceived three-dimensional (3D) shape of the target is distorted. One explanation of this paradox is that an evolutionarily specialized "vision-for-action" system provides accurate shape estimates by relying selectively on stereo information and ignoring less reliable sources of shape information like texture and shading. However, the key support for this hypothesis has come from studies that analyze average behavior across many visuomotor interactions where available sensory feedback reinforces stereo information. The present study, which carefully accounts for the effects of feedback, shows that visuomotor interactions with slanted surfaces are actually planned using the same cue-combination function as slant perception and that apparent dissociations can arise due to two distinct supervised learning processes: sensorimotor adaptation and cue reweighting. In two experiments, we show that when a distorted slant cue biases perception (e.g., surfaces appear flattened by a fixed amount), sensorimotor adaptation rapidly adjusts the planned grip orientation to compensate for this constant error. However, when the distorted slant cue is unreliable, leading to variable errors across a set of objects (i.e., some slants are overestimated, others underestimated), then relative cue weights are gradually adjusted to reduce the misleading effect of the unreliable cue, consistent with previous perceptual studies of cue reweighting. The speed and flexibility of these two forms of learning provide an alternative explanation of why perception and action are sometimes found to be dissociated in experiments where some 3D shape cues are consistent with sensory feedback while others are faulty. When interacting with three-dimensional (3D) objects, sensory feedback is available that could improve future performance via supervised learning. Here we confirm that natural visuomotor interactions lead to sensorimotor adaptation and cue reweighting, two distinct learning processes uniquely suited to resolve errors caused by biased and noisy 3D shape cues. These findings explain why perception and action are often found to be dissociated in experiments where some cues are consistent with sensory feedback while others are faulty.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1152/jn.00718.2019 | DOI Listing |
Nat Commun
December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
View Article and Find Full Text PDFFront Hum Neurosci
December 2024
Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia.
The COVID-19 pandemic has highlighted the prevalence of fatigue, reduced interpersonal interaction, and heightened stress in work environments. The intersection of neuroscience and architecture underscores how intricate spatial perceptions are shaped by multisensory stimuli, profoundly influencing workers' wellbeing. In this study, EEG and VR technologies, specifically the , were employed to gather data on perception and cognition.
View Article and Find Full Text PDFBio Protoc
December 2024
Department of Biology, Texas A&M University, College Station, TX, USA.
larvae exhibit rolling motor behavior as an escape response to avoid predators and painful stimuli. We introduce an accessible method for applying optogenetics to study the motor circuits driving rolling behavior. For this, we simultaneously implement the Gal4-UAS and LexA-Aop binary systems to express two distinct optogenetic channels, GtACR and Chrimson, in motor neuron (MN) subsets and rolling command neurons (Goro), respectively.
View Article and Find Full Text PDFCommun Med (Lond)
December 2024
Institute of Computational Biology, Helmholtz Munich, 85764, Neuherberg, Germany.
Background: Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet.
Methods: Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data.
J Physiol
December 2024
Delft University of Technology, Delft, The Netherlands.
A task as simple as holding a cup between your fingers generates complex motor commands to finely regulate the forces applied by muscles. These fine force adjustments ensure the stability and integrity of the object by preventing it from slipping out of grip during manipulation and by reacting to perturbations. To do so, our sensorimotor system constantly monitors tactile and proprioceptive information about the force object exerts on fingertips and the friction of the surfaces to determine the optimal grip force.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!