scholarly journals Waving goodbye to contrast: Self-generated hand movements attenuate visual sensitivity

2018 ◽  
Author(s):  
Madis Vasser ◽  
Laurène Vuillaume ◽  
Axel Cleeremans ◽  
Jaan Aru
Keyword(s):  
Virtual Reality ◽  
Visual Sensitivity ◽  
Hand Movements ◽  
Active Inference ◽  
Body Movements ◽  
Apparent Contrast ◽  
First Order ◽  
Metacognitive Judgments ◽  
Sensory Attenuation ◽  
Inference Theory

AbstractIt is well known that the human brain continuously predicts the sensory consequences of its own body movements, which typically results in sensory attenuation. Yet, the extent and exact mechanisms underlying sensory attenuation are still debated. To explore this issue, we asked participants to decide which of two visual stimuli was of higher contrast in a virtual reality situation where one of the stimuli could appear behind the participants’ invisible moving hand or not. Over two experiments, we measured the effects of such “virtual occlusion” on first-order sensitivity and on metacognitive monitoring. Our findings show that self-generated hand movements reduced the apparent contrast of the stimulus. This result can be explained by the active inference theory. Moreover, sensory attenuation seemed to affect only first-order sensitivity and not (second-order) metacognitive judgments of confidence.

scholarly journals Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
George Deane
Keyword(s):  
Phenomenal Consciousness ◽  
Predictive Processing ◽  
Active Inference ◽  
Wide Range ◽  
Psychedelic Drugs ◽  
Deep Inference ◽  
Sensory Attenuation ◽  
Subjective Valuation ◽  
Inference Theory ◽  
Central Claim

Abstract Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by ‘subjective valuation’—a deep inference about the precision or ‘predictability’ of the self-evidencing (‘fitness-promoting’) outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of ‘selflessness’; in particular the ‘totally selfless’ states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

scholarly journals From aliens to invisible limbs: The transitions that never make it into conscious experience

2018 ◽  
Author(s):  
Jaan Aru
Keyword(s):  
Virtual Reality ◽  
Motor Cortex ◽  
Neural Activity ◽  
Conscious Experience ◽  
Neural Mechanisms ◽  
Active Inference ◽  
Eye Blinks ◽  
Sensory Cortices ◽  
Sensory Activity ◽  
Inference Theory

Here it is suggested that one interesting but not well-studied property of consciousness is its continuity – the fact that my experience is stable in time despite the myriads of changes in the underlying neural activity. It is proposed that there are specific mechanisms that maintain the continuity of consciousness by preventing certain transitions in the environment from entering conscious experience. These mechanisms are the key reason why we do not perceive the involuntary eye-blinks or why our own moving limbs do not capture our attention. I will describe some studies we have conducted with virtual reality to demonstrate that one mechanism supporting the continuity of consciousness seems to be the withdrawal of attention from the specific predictable sensory activity. It is described how the active inference theory can explain this set of findings. It seems that (for now) the active inference theory is the only theory that can account for the continuity of consciousness. Next, we explore the neural mechanisms of how the motor cortex conveys specific predictions to the sensory cortices and inhibit the predicted sensory consequences of own movement. Although not much is learned from this piece about the mechanisms of continuity, it is concluded that the topic is worth to be explored more thoroughly.

scholarly journals Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 198
Author(s):  
Stephen Fox
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Social Organization ◽  
Generative Models ◽  
Industrial Engineering ◽  
Prediction Errors ◽  
Active Inference ◽  
For Profit ◽  
Different Types ◽  
Inference Theory

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

scholarly journals Virtual Reality and Assistive Technologies: A Survey

2018 ◽  
Vol 18 (2) ◽  
pp. 30-57
Author(s):  
Shamima Yasmin
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Virtual Environment ◽  
Assistive Technologies ◽  
Sensory Substitution ◽  
Body Movements ◽  
Rehabilitation Therapy ◽  
Extensive Survey ◽  
Different Types ◽  
Mental Exercise

This paper conducts an extensive survey on existing Virtual Reality (VR)-based rehabilitation approaches in the context of different types of impairments: mobility, cognitive, and visual. Some VR-based assistive technologies involve repetitions of body movements, some require persistent mental exercise, while some work as sensory substitution systems. A multi-modal VR-based environment can incorporate a number of senses, (i.e., visual, auditory, or haptic) into the system and can be an immense source of motivation and engagement in comparison with traditional rehabilitation therapy. This survey categorizes virtual environments on the basis of different available modalities. Each category is again subcategorized by the types of impairments while introducing available devices and interfaces. Before concluding the survey, the paper also briefly focuses on some issues with existing VR-based approaches that need to be optimized to exploit the utmost benefit of virtual environment-based rehabilitation systems .

Virtual Reality Exergames: Promoting Physical Health Among Industry Workers

2021 ◽  
Author(s):  
Thomas Stranick ◽  
Christian Lopez
Keyword(s):  
Virtual Reality ◽  
Activity Level ◽  
Sensor Technology ◽  
Depth Sensor ◽  
Body Movements ◽  
Warm Up ◽  
Work Related ◽  
Positive Effects ◽  
Pass Through ◽  
Game Elements

Abstract This work introduces a Virtual Reality (VR) Exergame application to prevent Work Related Musculoskeletal Disorders (WMSDs). WMSDs are an important issue that can have a direct economic impact since they can injure workers, who are then forced to take time off. Exercise and stretching is one method that can benefit workers’ muscles and help prevent WMSDs. While several applications have been developed to prevent WMSDs, most of the existing applications suffer from a lack of immersivity or just focus on education and not necessarily helping workers warm-up or stretch. Hence, this work presents an Exergame application that leverages VR and Depth-sensor technology to help provide users with an immersive first-person experience. The objective of the VR Exergame is to encourage and motivate users to perform full-body movements in order to pass through a series of obstacles. The application implements a variety of game elements to help motivate users to play the game and stretch. While in the game, users can visualize their motions by controlling the virtual avatar with their body movements. It is expected that this immersivity will motivate and encourage the users. Initial findings show the positive effects that the base exergame has on individuals’ motivation and physical activity level. The results indicate that the application was able to engage individuals in low-intensity exercises that produced significant and consistent increases in their heart rate. Lastly, this work explores the development and benefits that this VR Exergame could bring by motivating workers and preventing WMSDs.

scholarly journals A Virtual Reality Soldier Simulator with Body Area Networks for Team Training

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 451 ◽  
Author(s):  
Yun-Chieh Fan ◽  
Chih-Yu Wen
Keyword(s):  
Virtual Reality ◽  
Physical Space ◽  
Body Area Networks ◽  
Simulation System ◽  
Training Data ◽  
Training Process ◽  
Body Movements ◽  
Body Area ◽  
Training Strategy ◽  
Military Tactics

Soldier-based simulators have been attracting increased attention recently, with the aim of making complex military tactics more effective, such that soldiers are able to respond rapidly and logically to battlespace situations and the commander’s decisions in the battlefield. Moreover, body area networks (BANs) can be applied to collect the training data in order to provide greater access to soldiers’ physical actions or postures as they occur in real routine training. Therefore, due to the limited physical space of training facilities, an efficient soldier-based training strategy is proposed that integrates a virtual reality (VR) simulation system with a BAN, which can capture body movements such as walking, running, shooting, and crouching in a virtual environment. The performance evaluation shows that the proposed VR simulation system is able to provide complete and substantial information throughout the training process, including detection, estimation, and monitoring capabilities.

Movement paths in operating hand-held tools: tests of distal-shift hypotheses

2013 ◽  
Vol 109 (11) ◽  
pp. 2680-2690 ◽  
Author(s):  
Sandra Sülzenbrück ◽  
Herbert Heuer
Keyword(s):  
Boundary Conditions ◽  
Visual Feedback ◽  
The Body ◽  
Hand Movements ◽  
First Order ◽  
Dynamic Transformation ◽  
Movement Characteristics ◽  
Effective Part

Extending the body with a tool could imply that characteristics of hand movements become characteristics of the movement of the effective part of the tool. Recent research suggests that such distal shifts are subject to boundary conditions. Here we propose the existence of three constraints: a strategy constraint, a constraint of movement characteristics, and a constraint of mode of control. We investigate their validity for the curvature of transverse movements aimed at a target while using a sliding first-order lever. Participants moved the tip of the effort arm of a real or virtual lever to control a cursor representing movements of the tip of the load arm of the lever on a monitor. With this tool, straight transverse hand movements are associated with concave curvature of the path of the tip of the tool. With terminal visual feedback and when targets were presented for the hand, hand paths were slightly concave in the absence of the dynamic transformation of the tool and slightly convex in its presence. When targets were presented for the tip of the lever, both the concave and convex curvatures of the hand paths became stronger. Finally, with continuous visual feedback of the tip of the lever, curvature of hand paths became convex and concave curvature of the paths of the tip of the lever was reduced. In addition, the effect of the dynamic transformation on curvature was attenuated. These findings support the notion that distal shifts are subject to at least the three proposed constraints.

scholarly journals Waving goodbye to contrast: self-generated hand movements attenuate visual sensitivity

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Madis Vasser ◽  
Laurène Vuillaume ◽  
Axel Cleeremans ◽  
Jaan Aru
Keyword(s):  
Visual Sensitivity ◽  
Hand Movements

scholarly journals Machine Learning and Virtual Reality on Body Movements’ Behaviors to Classify Children with Autism Spectrum Disorder

2020 ◽  
Vol 9 (5) ◽  
pp. 1260 ◽  
Author(s):  
Mariano Alcañiz Raya ◽  
Javier Marín-Morales ◽  
Maria Eleonora Minissi ◽  
Gonzalo Teruel Garcia ◽  
Luis Abad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Quantitative Methods ◽  
Real Life ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Body Movements ◽  
Children With Asd

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.

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