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Summary: Spatial cognition and mobility are typically impaired in congenitally blind individuals, as vision usually calibrates space perception by providing the most accurate distal spatial cues. We have previously shown that sight restoration from congenital bilateral cataracts guides the development of more accurate space perception, even when cataract removal occurs years after birth. However, late cataract-treated individuals do not usually reach the performance levels of the typically sighted population. Here, we developed a brief multisensory training that associated audiovisual feedback with body movements. Late cataract-treated participants quickly improved their space representation and mobility, performing as well as typically sighted controls in most tasks. Their improvement was comparable with that of a group of blind participants, who underwent training coupling their movements with auditory feedback alone. These findings suggest that spatial cognition can be enhanced by a training program that strengthens the association between bodily movements and their sensory feedback (either auditory or audiovisual).

Abstract The Bouba-Kiki effect is the systematic mapping between round/spiky shapes and speech sounds (“Bouba”/“Kiki”). In the size-weight illusion, participants judge the smaller of two equally-weighted objects as being heavier. Here we investigated the contribution of visual experience to the development of these phenomena. We compared three groups: early blind individuals (no visual experience), individuals treated for congenital cataracts years after birth (late visual experience), and typically sighted controls (visual experience from birth). We found that, in cataract-treated participants (tested visually/visuo-haptically), both phenomena are absent shortly after sight onset, just like in blind individuals (tested haptically). However, they emerge within months following surgery, becoming statistically indistinguishable from the sighted controls. This suggests a pivotal role of visual experience and refutes the existence of an early sensitive period: A short period of experience, even when gained only years after birth, is sufficient for participants to visually pick-up regularities in the environment, contributing to the development of these phenomena.

A brief period of monocular deprivation (MD) induces short-term plasticity of the adult visual system. Whether MD elicits neural changes beyond visual processing is yet unclear. Here, we assessed the specific impact of MD on neural correlates of multisensory processes. Neural oscillations associated with visual and audio-visual processing were measured for both the deprived and the non-deprived eye. Results revealed that MD changed neural activities associated with visual and multisensory processes in an eye-specific manner. Selectively for the deprived eye, alpha synchronization was reduced within the first 150 ms of visual processing. Conversely, gamma activity was enhanced in response to audio-visual events only for the non-deprived eye within 100–300 ms after stimulus onset. The analysis of gamma responses to unisensory auditory events revealed that MD elicited a crossmodal upweight for the non-deprived eye. Distributed source modeling suggested that the right parietal cortex played a major role in neural effects induced by MD. Finally, visual and audio-visual processing alterations emerged for the induced component of the neural oscillations, indicating a prominent role of feedback connectivity. Results reveal the causal impact of MD on both unisensory (visual and auditory) and multisensory (audio-visual) processes and, their frequency-specific profiles. These findings support a model in which MD increases excitability to visual events for the deprived eye and audio-visual and auditory input for the non-deprived eye.

Neural mechanisms that arbitrate between integrating and segregating multisensory information are essential for complex scene analysis. Here, the authors show the existence of multisensory correlation detectors in the human brain which explains why and how causal inference is driven by the temporal correlation of multisensory signals.

Over the last few years online platforms for running psychology experiments beyond simple questionnaires and surveys have become increasingly popular. This trend has especially increased after many laboratory facilities had to temporarily avoid in-person data collection following COVID-19-related lockdown regulations. Yet, while offering a valid alternative to in-person experiments in many cases, platforms for online experiments are still not a viable solution for a large part of human-based behavioral research. Two situations in particular pose challenges: First, when the research question requires design features or participant interaction which exceed the customization capability provided by the online platform; and second, when variation among hardware characteristics between participants results in an inadmissible confounding factor. To mitigate the effects of these limitations, we developed ReActLab (Remote Action Laboratory), a framework for programming remote, browser-based experiments using freely available and open-source JavaScript libraries. Since the experiment is run entirely within the browser, our framework allows for portability to any operating system and many devices. In our case, we tested our approach by running experiments using only a specific model of Android tablet. Using ReActLab with this standardized hardware allowed us to optimize our experimental design for our research questions, as well as collect data outside of laboratory facilities without introducing setup variation among participants. In this paper, we describe our framework and show examples of two different experiments carried out with it: one consisting of a visuomotor adaptation task, the other of a visual localization task. Through comparison with results obtained from similar tasks in in-person laboratory settings, we discuss the advantages and limitations for developing browser-based experiments using our framework.

Whenever we grasp and lift an object, our tactile system provides important information on the contact location and the force exerted on our skin. The human brain integrates signals from multiple sites for a coherent representation of object shape, inertia, weight, and other material properties. It is still an open question whether the control of grasp force occurs at the level of individual fingers or whether it is also influenced by the control and the signals from the other fingers of the same hand. In this work, we approached this question by asking participants to lift, transport, and replace a sensorized object, using three- and four-digit grasp. Tactile input was altered by covering participant's fingertips with a rubber thimble, which reduced the reliability of the tactile sensory input. In different experimental conditions, we covered between one and three fingers opposing the thumb. Normal forces at each finger and the thumb were recorded while grasping and holding the object, with and without the thimble. Consistently with previous studies, reducing tactile sensitivity increased the overall grasping force. The gasping force increased in the covered finger, whereas it did not change from baseline in the remaining bare fingers (except the thumb for equilibrium constraints). Digit placement and object tilt were not systematically affected by rubber thimble conditions. Our results suggest that, in each finger opposing thumb, digit normal force is controlled locally in response to the applied tactile perturbation.

While interacting with the world our senses and nervous system are constantly challenged to identify the origin and coherence of sensory input signals of various intensities. This problem becomes apparent when stimuli from different modalities need to be combined, e.g., to find out whether an auditory stimulus and a visual stimulus belong to the same object. To cope with this problem, humans and most other animal species are equipped with complex neural circuits to enable fast and reliable combination of signals from various sensory organs. This multisensory integration starts in the brain stem to facilitate unconscious reflexes and continues on ascending pathways to cortical areas for further processing. To investigate the underlying mechanisms in detail, we developed a canonical neural network model for multisensory integration that resembles neurophysiological findings. For example, the model comprises multisensory integration neurons that receive excitatory and inhibitory inputs from unimodal auditory and visual neurons, respectively, as well as feedback from cortex. Such feedback projections facilitate multisensory response enhancement and lead to the commonly observed inverse effectiveness of neural activity in multisensory neurons. Two versions of the model are implemented, a rate-based neural network model for qualitative analysis and a variant that employs spiking neurons for deployment on a neuromorphic processing. This dual approach allows to create an evaluation environment with the ability to test model performances with real world inputs. As a platform for deployment we chose IBM's neurosynaptic chip TrueNorth. Behavioral studies in humans indicate that temporal and spatial offsets as well as reliability of stimuli are critical parameters for integrating signals from different modalities. The model reproduces such behavior in experiments with different sets of stimuli. In particular, model performance for stimuli with varying spatial offset is tested. In addition, we demonstrate that due to the emergent properties of network dynamics model performance is close to optimal Bayesian inference for integration of multimodal sensory signals. Furthermore, the implementation of the model on a neuromorphic processing chip enables a complete neuromorphic processing cascade from sensory perception to multisensory integration and the evaluation of model performance for real world inputs.

Feedback is essential for skill acquisition as it helps identifying and correcting performance errors. Nowadays, Virtual Reality can be used as a tool to guide motor learning, and to provide innovative types of augmented feedback that exceed real world opportunities. Concurrent feedback has shown to be especially beneficial for novices. Moreover, watching skilled performances helps novices to acquire a motor skill, and this effect depends on the perspective taken by the observer. To date, however, the impact of watching one's own performance together with full body superimposition of a skilled performance, either from the front or from the side, remains to be explored. Here we used an immersive, state-of-the-art, low-latency cave automatic virtual environment (CAVE), and we asked novices to perform squat movements in front of a virtual mirror. Participants were assigned to one of three concurrent visual feedback groups: participants either watched their own avatar performing full body movements or were presented with the movement of a skilled individual superimposed on their own performance during movement execution, either from a frontal or from a side view. Motor performance and cognitive representation were measured in order to track changes in movement quality as well as motor memory across time. Consistent with our hypotheses, results showed an advantage of the groups that observed their own avatar performing the squat together with the superimposed skilled performance for some of the investigated parameters, depending on perspective. Specifically, for the deepest point of the squat, participants watching the squat from the front adapted their height, while those watching from the side adapted their backward movement. In a control experiment, we ruled out the possibility that the observed improvements were due to the mere fact of performing the squat movements—irrespective of the type of visual feedback. The present findings indicate that it can be beneficial for novices to watch themselves together with a skilled performance during execution, and that improvement depends on the perspective chosen.

The human brain integrates inputs across multiple sensory streams into a unified percept. Here Parise and Ernst present a model that assesses the correlation, lag and synchrony of multisensory stimuli, and predicts psychophysical performance on multisensory temporal and spatial tasks.

Memories of places often include landmark cues, i.e., information provided by the spatial arrangement of distinct objects with respect to the target location. To study how humans combine landmark information for navigation, we conducted two experiments: To this end, participants were either provided with auditory landmarks while walking in a large sports hall or with visual landmarks while walking on a virtual-reality treadmill setup. We found that participants cannot reliably locate their home position due to ambiguities in the spatial arrangement when only one or two uniform landmarks provide cues with respect to the target. With three visual landmarks that look alike, the task is solved without ambiguity, while audio landmarks need to play three unique sounds for a similar performance. This reduction in ambiguity through integration of landmark information from 1, 2, and 3 landmarks is well modeled using a probabilistic approach based on maximum likelihood estimation. Unlike any deterministic model of human navigation (based e.g., on distance or angle information), this probabilistic model predicted both the precision and accuracy of the human homing performance. To further examine how landmark cues are integrated we introduced systematic conflicts in the visual landmark configuration between training of the home position and tests of the homing performance. The participants integrated the spatial information from each landmark near-optimally to reduce spatial variability. When the conflict becomes big, this integration breaks down and precision is sacrificed for accuracy. That is, participants return again closer to the home position, because they start ignoring the deviant third landmark. Relying on two instead of three landmarks, however, goes along with responses that are scattered over a larger area, thus leading to higher variability. To model the breakdown of integration with increasing conflict, the probabilistic model based on a simple Gaussian distribution used for Experiment 1 needed a slide extension in from of a mixture of Gaussians. All parameters for the Mixture Model were fixed based on the homing performance in the baseline condition which contained a single landmark. from the 1-Landmark Condition. This way we found that the Mixture Model could predict the integration performance and its breakdown with no additional free parameters. Overall these data suggest that humans use similar optimal probabilistic strategies in visual and auditory navigation, integrating landmark information to improve homing precision and balance homing precision with homing accuracy.