Search

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.

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.

Summary 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., Graphical abstract, Highlights • Changes in optic flow speed triggers implicit adjustments of walking speed • The response is invariant with respect to the depths of objects in the scene • The invariance is not explained by temporal-frequency-based speed coding • Both binocular and monocular depth cues contribute to the invariance, Biological sciences; Neuroscience; Behavioral neuroscience; Sensory neuroscience

Whilst everyday interactions with objects often involve multiple tactile contacts, integration of tactile signals remains poorly understood. Here we characterise the integration process of tactile motion on multiple fingerpads. Across four experiments, participants averaged the direction of two simultaneous tactile motion trajectories delivered to different fingerpads. Averaging performance differed between within- and between-hands in terms of sensitivity and precision but was unaffected by somatotopic proximity between stimulated fingers. The sensitivity to the average direction was influenced by the discrepancy between individual motion signals, but only for within-hand conditions. This was explained by a model, in which the ‘virtually leading finger’ received a higher perceptual weighting. Precision was greater in between-hand compared to within-hand conditions. While biased weighting accounted for differences in sensitivity, it was not sufficient to explain the difference in precision, implying additional sensory limitations during within-hand integration. We suggest that unimanual integration is limited and thus exploits a ‘natural’ cognitive prior involving a single object moving relative to the hand to maximise information gain.Author summaryTactile stimulation is always on. Yet little is known about how the brain combines widespread tactile inputs for perception. Most tactile studies emphasize a single point of tactile stimulation (e.g., location or intensity of a static stimulus) and minimal units of tactile perception (e.g., acuity or selectivity). However, our daily interactions with the world involve encoding spatially and temporally extended tactile signals. Perceiving tactile objects and events as coherent entities requires the somatosensory system to aggregate tactile afferent signals across separate skin regions (i.e., separate digits). Across four experiments we asked participants to average direction of two tactile motion trajectories delivered simultaneously to two different fingerpads, either on the same, or on different hands. Our results show strong integration between multiple tactile inputs, but subject to limitations for inputs delivered within a hand. Our model suggests that tactile inputs are weighted according to an integrative model of hand-object interaction that operates within-hands on purely geometric information to prioritise ‘novel’ information from a ‘virtually leading finger’ (VLF).

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.

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.

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.

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.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.

The acquisition of object categories which underlie the human lexicon is a prerequisite for domestic robots to communicate with users in a human-like manner. The theory of J. J. Gibson inspires the...

Considering the wide range of possible behaviours to be acquired for domestic robots, applying a single learning method is clearly insufficient. In this paper, we propose a new strategy for behaviour acquisition for domestic robots where the behaviours are acquired using multiple differing learning methods that are subsequently incorporated into a common behaviour selection system, enabling them to be performed in appropriate situations. An example of the implementation of this strategy applied to the entertainment humanoid robot QRIO is introduced and the results are discussed.

An illusory sense of continuous directional pulling force can be induced by applying asymmetric vibration to the human skin [Amemiya, et. al., 2005]. The illusion enables a force display device without any external fulcrum, a requirement for portable or wearable devices (e.g., smartphones) [Nakamura and Fukui 2007; Rekimoto 2013; Amemiya and Gomi 2014]. Once such haptic display device could be implemented into such mobile devices, this would enable series of attractive applications such as intuitive lead-by-hand navigation for people with visual impairment as well as computer games with force feedback. However, the display device needs to be smaller, lighter, and require less energy compared to the current versions for the implementation. Since this would cause the devices to be weaker, capable of producing smaller force sensation, a technique to improve the energy efficiency of the force display devices is essential.

Studies on lexicon acquisition systems are gaining attention in the search for a natural human–robot interface and a test environment to model the infant lexicon acquisition process. Although various lexicon acquisition systems that ground words to sensory experience have been developed, existing systems have clear limitations on the ability to autonomously associate words to objects. This limitation is due to the fact that categories for words are formed in a passive manner, either by teaching of caregivers or finding similarities in visual features. This paper presents a system for lexicon acquisition through behavior learning. Based on a modified multi-module reinforcement learning system, the robot is able to automatically associate words to objects with various visual features based on similarities in affordances or in functions. The system was implemented on a mobile robot acquiring a lexicon related to different rolling preferences. The experimental results are given and future issues are discussed.

Thermal sense plays an important role in object recognition. The idea to identify materials by heat flow from the skin to the touched materials have been introduced to artificial thermal sensors and proved effective. Moreover, the addition of tactile sensors is found to improve the performance of material identification by obtaining further properties of objects such as roughness and stiffness. However, the application of these sensors to autonomous recognition by a robot is not a well investigated topic. Contact with the objects will vary in such cases, but no previous sensor has shown the ability of material recognition robust to change in contact conditions with the required size and softness. The limitation is partly due to the design of current thermal sensors where the skin and the sensing devices are separated. This paper introduces a soft anthropomorphic finger with both thermal and tactile sensing elements embedded in soft polyurethane material. Experimental results show the ability to discriminate materials robust to variety in contact condition by virtue of the deformable tissue and compensation by the tactile sensors. Recognition robust to change of temperature is also realized by an additional thermal sensor.

Skin is an essential component of artificial hands. It enables the use of object affordance for recognition and control, but due to its intrinsic locality and low density of current tactile sensors, stable and proper manual contacts with the objects are indispensable. Recently, design of hand structure have shown to be effective for adaptive grasping. However, such adaptive design are only introduced to the fingers in existing works of haptics and their role in recognition remains unclear. This paper introduces the design of the Bionic Hand; an anthropomorphic hand with adaptive design introduced to the whole hand and fully covered with sensitive skin. The experiment shows that anthropomorphic design of hand structure enables robust haptic recognition by convergence of object contact conditions into stable representative states through repetitive grasping. The structure of the human hand is found to solve the issue of narrowing down the sensor space for haptic object recognition by morphological computation.

One of the key aspects of understanding human intelligence is to investigate how humans interact with their environment. Performing articulated movement and manipulation tasks in a constantly changing environment, have proven more difficult than expected. The difficulties of robot manipulation are in part due to the unbalanced relation between vision and haptic sensing. Most robots are equipped with high resolution cameras, which images are processed by well established computer vision algorithms such as color segmentation, motion detection, edge detection, etc. However, the majority of robots have very limited haptic capabilities. This paper presents our attempt to overcome this difficulties by: (a) using a tendon driven robotic hand with rich dynamical movements and (b) covering the hand with a set of haptic sensors on the palm and the fingertips, the sensors are based on a simplified version of an artificial skin with strain gauges and PVDF (polyvinylidene fluoride) films. The results show that if the robotic hand actively explores different objects using the exploratory procedures: tapping and squeezing, material properties such as hardness and texture can be used to discriminate haptically between different objects.

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.

Studies on lexicon acquisition systems are gaining attention in hope for a natural human-robot interface and a test environment for theories of infant lexicon acquisition. This paper presents a system that forms word categories based on object-oriented behaviors. By using physical experiences, the system is able to generalize names to objects with various visual features. The system was implemented to a mobile robot acquiring lexicon about object categories with different rolling preferences. The system successfully acquired the lexicon and generalized the names to objects with various visual features in accordance with their rolling preferences