Embodied Precision : Intranasal Oxytocin Modulates Multisensory Integration

© 2018 Massachusetts Institute of Technology.Multisensory integration processes are fundamental to our sense of self as embodied beings. Bodily illusions, such as the rubber hand illusion (RHI) and the size-weight illusion (SWI), allow us to investigate how the brain resolves conflicting multisensory evidence during perceptual inference in relation to different facets of body representation. In the RHI, synchronous tactile stimulation of a participant's hidden hand and a visible rubber hand creates illusory body ownership; in the SWI, the perceived size of the body can modulate the estimated weight of external objects. According to Bayesian models, such illusions arise as an attempt to explain the causes of multisensory perception and may reflect the attenuation of somatosensory precision, which is required to resolve perceptual hypotheses about conflicting multisensory input. Recent hypotheses propose that the precision of sensorimotor representations is determined by modulators of synaptic gain, like dopamine, acetylcholine, and oxytocin. However, these neuromodulatory hypotheses have not been tested in the context of embodied multisensory integration. The present, double-blind, placebo-controlled, crossover study ( N = 41 healthy volunteers) aimed to investigate the effect of intranasal oxytocin (IN-OT) on multisensory integration processes, tested by means of the RHI and the SWI. Results showed that IN-OT enhanced the subjective feeling of ownership in the RHI, only when synchronous tactile stimulation was involved. Furthermore, IN-OT increased an embodied version of the SWI (quantified as estimation error during a weight estimation task). These findings suggest that oxytocin might modulate processes of visuotactile multisensory integration by increasing the precision of top-down signals against bottom-up sensory input.Peer reviewedFinal Accepted Versio

¿Los sujetos con obesidad subestiman su tamaño corporal? Una revisión narrativa de los métodos de estimación y teorías explicativas

The widespread of overweight and obesity in the developed countries is a real societal issue, nevertheless a considerable amount of subjects with obesity do not recognize their condition. Researchers used different methods to assess body size perception by obese subjects and the results show that while some subjects with obesity estimate accurately or overestimate their body size, others underestimate their weight and their body size measures. A failure to identify overweight or obesity has serious consequences on the subject's health, as it is widely recognised that self-awareness is the first step to engage in a rehabilitation program. The spread of obesity underestimation and its implications make the case for a new hypothetical body image disorder, which has been called Fatorexia (TM). It consists in the significant underestimation of body size by subjects with obesity, as they are unable or unwilling to acknowledge their condition. Some researchers proposed a social explanation to the underestimation phenomenon, but here an alternative hypothesis, the Allocentric Lock Theory (ALT), is outlined to describe the mechanisms behind the underestimation of body size by subjects with obesity

Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes