Multidigit tactile perception I: motion integration benefits for tactile trajectories presented bimanually.

Interactions with objects involve simultaneous contact with multiple, not necessarily adjacent, skin regions. Although advances have been made in understanding the capacity to selectively attend to a single tactile element among distracting stimulations, here, we examine how multiple stimulus elements are explicitly integrated into an overall tactile percept. Across four experiments, participants averaged the direction of two simultaneous tactile motion trajectories of varying discrepancy delivered to different fingerpads. Averaging performance differed between within- and between-hands conditions in terms of sensitivity and precision but was unaffected by somatotopic proximity between stimulated fingers. First, precision was greater in between-hand compared with within-hand conditions, demonstrating a bimanual perceptual advantage in multi-touch integration. Second, sensitivity to the average direction was influenced by the discrepancy between individual motion signals, but only for within-hand conditions. Overall, our experiments identify key factors that influence perception of simultaneous tactile events. In particular, we show that multi-touch integration is constrained by hand-specific rather than digit-specific mechanisms.NEW & NOTEWORTHY Object manipulation involves encoding spatially and temporally extended tactile signals, yet most studies emphasize minimal units of tactile perception (e.g., selectivity). Instead, we asked participants to average two tactile motion trajectories delivered simultaneously to two different fingerpads. Our results show strong integration between multiple tactile inputs, but subject to limitations for inputs delivered within a hand. As such, the present study establishes a paradigm for studying unified experience of touch despite distinct stimulus elements.

Vision-based speedometer regulates human walking.

Can we recover self-motion from vision? This basic issue remains unsolved since, while the human visual system is known to estimate the direction of self-motion from optic flow, it remains unclear whether it also estimates the speed. Importantly, the latter requires disentangling self-motion speed and depths of objects in the scene as retinal velocity depends on both. Here we show that our automatic regulator of walking speed based on vision, which estimates and maintains the speed to its preferred range by adjusting stride length, is robust to changes in the depths. The robustness was not explained by temporal-frequency-based speed coding previously suggested to underlie depth-invariant object-motion perception. Meanwhile, it broke down, not only when the interocular distance was virtually manipulated but also when monocular depth cues were deceptive. These observations suggest that our visuomotor system embeds a speedometer that calculates self-motion speed from vision by integrating monocular/binocular depth and motion cues.

Seeing motion of controlled object improves grip timing in adults with autism spectrum condition: evidence for use of inverse dynamics in motor control.

Previous studies (Haswell et al. in Nat Neurosci 12:970-972, 2009; Marko et al. in Brain J Neurol 138:784-797, 2015) reported that people with autism rely less on vision for learning to reach in a force field. This suggested a possibility that they have difficulties in extracting force information from visual motion signals, a process called inverse dynamics computation. Our recent study (Takamuku et al. in J Int Soc Autism Res 11:1062-1075, 2018) examined the ability of inverse computation with two perceptual tasks and found similar performances in typical and autistic adults. However, this tested the computation only in the context of sensory perception while it was possible that the suspected disability is specific to the motor domain. Here, in order to address the concern, we tested the use of inverse dynamics computation in the context of motor control by measuring changes in grip timing caused by seeing/not seeing a controlled object. The motion of the object was informative of its inertial force and typical participants improved their grip timing based on the visual feedback. Our interest was on whether the autism participants show the same improvement. While some autism participants showed atypical hand slowing when seeing the controlled object, we found no evidence of abnormalities in the inverse computation in our grip timing task or in a replication of the perceptual task. This suggests that the ability of inverse dynamics computation is preserved not only for sensory perception but also for motor control in adults with autism.

Better grip force control by attending to the controlled object: Evidence for direct force estimation from visual motion.

Estimating forces acting between our hand and objects is essential for dexterous motor control. An earlier study suggested that vision contributes to the estimation by demonstrating changes in grip force pattern caused by delayed visual feedback. However, two possible vision-based force estimation processes, one based on hand position and another based on object motion, were both able to explain the effect. Here, to test each process, we examined how visual feedback of hand and object each contribute to grip force control during moving an object (mass) connected to the grip by a damped-spring. Although force applied to the hand could be estimated from its displacement, we did not find any improvements by the hand feedback. In contrast, we found that visual feedback of object motion significantly improved the synchrony between grip and load forces. Furthermore, when both feedback sources were provided, the improvement was observed only when participants were instructed to direct their attention to the object. Our results suggest that visual feedback of object motion contributes to estimation of dynamic forces involved in our actions by means of inverse dynamics computation, i.e., the estimation of force from motion, and that visual attention directed towards the object facilitates this effect.

Typical use of inverse dynamics in perceiving motion in autistic adults: Exploring computational principles of perception and action.

There is increasing evidence for motor difficulties in many people with autism spectrum condition (ASC). These difficulties could be linked to differences in the use of internal models which represent relations between motions and forces/efforts. The use of these internal models may be dependent on the cerebellum which has been shown to be abnormal in autism. Several studies have examined internal computations of forward dynamics (motion from force information) in autism, but few have tested the inverse dynamics computation, that is, the determination of force-related information from motion information. Here, we examined this ability in autistic adults by measuring two perceptual biases which depend on the inverse computation. First, we asked participants whether they experienced a feeling of resistance when moving a delayed cursor, which corresponds to the inertial force of the cursor implied by its motion-both typical and ASC participants reported similar feelings of resistance. Second, participants completed a psychophysical task in which they judged the velocity of a moving hand with or without a visual cue implying inertial force. Both typical and ASC participants perceived the hand moving with the inertial cue to be slower than the hand without it. In both cases, the magnitude of the effects did not differ between the two groups. Our results suggest that the neural systems engaged in the inverse dynamics computation are preserved in ASC, at least in the observed conditions. Autism Res 2018, 11: 1062-1075. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We tested the ability to estimate force information from motion information, which arises from a specific "inverse dynamics" computation. Autistic adults and a matched control group reported feeling a resistive sensation when moving a delayed cursor and also judged a moving hand to be slower when it was pulling a load. These findings both suggest that the ability to estimate force information from motion information is intact in autism.

What you feel is what you see: inverse dynamics estimation underlies the resistive sensation of a delayed cursor.

How our central nervous system (CNS) learns and exploits relationships between force and motion is a fundamental issue in computational neuroscience. While several lines of evidence have suggested that the CNS predicts motion states and signals from motor commands for control and perception (forward dynamics), it remains controversial whether it also performs the 'inverse' computation, i.e. the estimation of force from motion (inverse dynamics). Here, we show that the resistive sensation we experience while moving a delayed cursor, perceived purely from the change in visual motion, provides evidence of the inverse computation. To clearly specify the computational process underlying the sensation, we systematically varied the visual feedback and examined its effect on the strength of the sensation. In contrast to the prevailing theory that sensory prediction errors modulate our perception, the sensation did not correlate with errors in cursor motion due to the delay. Instead, it correlated with the amount of exposure to the forward acceleration of the cursor. This indicates that the delayed cursor is interpreted as a mechanical load, and the sensation represents its visually implied reaction force. Namely, the CNS automatically computes inverse dynamics, using visually detected motions, to monitor the dynamic forces involved in our actions.