Decoding self-motion from visual image sequence predicts distinctive features of reflexive motor responses to visual motion

Abstract

Visual motion analysis is crucial for humans to detect external moving objects and self-motion which are informative for planning and executing actions for various interactions with environments. Here we show that the image motion analysis trained to decode the self-motion during human natural movements by a convolutional neural network exhibits similar specificities with the reflexive ocular and manual responses induced by a large-field visual motion, in terms of stimulus spatiotemporal frequency tuning. The spatiotemporal frequency tuning of the decoder peaked at high-temporal and low-spatial frequencies, as observed in the reflexive ocular and manual responses, but differed significantly from the frequency power of the visual image itself and the density distribution of self-motion. Further, artificial manipulations of the learning data sets predicted great changes in the specificity of the spatiotemporal tuning. Interestingly, despite similar spatiotemporal frequency tunings in the vertical-axis rotational direction and in the transversal direction to full-field visual stimuli, the tunings for center-masked stimuli were different between those directions, and the specificity difference is qualitatively similar to the discrepancy between ocular and manual responses, respectively. In addition, the representational analysis demonstrated that head-axis rotation was decoded by relatively simple spatial accumulation over the visual field, while the transversal motion was decoded by more complex spatial interaction of visual information. These synthetic model examinations support the idea that visual motion analyses eliciting the reflexive motor responses, which are critical in interacting with the external world, are acquired for decoding self-motion.