Sentence examples for audiovisual inputs from inspiring English sources

We propose a hierarchical architecture with growing self-organizing neural networks for learning human actions from audiovisual inputs.

Together, these results show that our hierarchical neural architecture accounts for the development of robust multimodal representations from dynamic audiovisual inputs.

By the end of the first year of life, responsiveness to nonnative audiovisual inputs declines both in sound face matching for other species and in nonnative language (Lewkowicz & Ghazanfar, 2009; Pons, Lewkowicz, Soto-Faraco, & Sebastián-Gallés, 2009).

Neuropsychology tells us that activities involving a powerful audiovisual input have a stronger motivational effect.

This network reflects all stages of the object matching task from early sensory audiovisual input, through semantic processing, decision-making, motor preparation and response execution.

In this way, we considered only the sound shift due to the conflicting audiovisual input and neglected the additional effect due to synaptic adaptation.

Firstly, independent of whether increased perceptual information was provided by uni-modal visual stimuli that were appropriately coloured, or by the combination of crossmodal audiovisual perceptual inputs, an enhanced response was observed in bilateral occipital cortices and the right anterior fusiform gyrus.

To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events.

In summary, the present study contrasts the effects of matching verbal versus nonverbal simultaneously presented audiovisual pairs by manipulating the type of input material.

In another study, the role of the left IFS in decisions about audiovisual object categorization was examined by manipulating the saliency of sensory inputs.

Once a database is created, it becomes possible to (1) visualize the basic step models contained in it, and (2) compare a new input dance performance with the stored models and provide direct audiovisual feedback on the quality of that performance.