Transcranial magnetic stimulation disrupts the perception and embodiment of facial expressions

Copyright © 2008 Society for Neuroscience and the authors. The The Journal of Neuroscience uses a Creative Commons Attribution-NonCommercial-ShareAlike licence: http://creativecommons.org/licenses/by-nc-sa/4.0/.Theories of embodied cognition propose that recognizing facial expressions requires visual processing followed by simulation of the somatovisceral responses associated with the perceived expression. To test this proposal, we targeted the right occipital face area (rOFA) and the face region of right somatosensory cortex (rSC) with repetitive transcranial magnetic stimulation (rTMS) while participants discriminated facial expressions. rTMS selectively impaired discrimination of facial expressions at both sites but had no effect on a matched face identity task. Site specificity within the rSC was demonstrated by targeting rTMS at the face and finger regions while participants performed the expression discrimination task. rTMS targeted at the face region impaired task performance relative to rTMS targeted at the finger region. To establish the temporal course of visual and somatosensory contributions to expression processing, double-pulse TMS was delivered at different times to rOFA and rSC during expression discrimination. Accuracy dropped when pulses were delivered at 60–100 ms at rOFA and at 100–140 and 130–170 ms at rSC. These sequential impairments at rOFA and rSC support embodied accounts of expression recognition as well as hierarchical models of face processing. The results also demonstrate that nonvisual cortical areas contribute during early stages of expression processing.Biotechnology and Biological Sciences Research Counci

Mixed reality participants in smart meeting rooms and smart home enviroments

Human–computer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Cortical topography of intracortical inhibition influences the speed of decision making

The neocortex contains orderly topographic maps; however, their functional role remains controversial. Theoretical studies have suggested a role in minimizing computational costs, whereas empirical studies have focused on spatial localization. Using a tactile multiple-choice reaction time (RT) task before and after the induction of perceptual learning through repetitive sensory stimulation, we extend the framework of cortical topographies by demonstrating that the topographic arrangement of intracortical inhibition contributes to the speed of human perceptual decision-making processes. RTs differ among fingers, displaying an inverted U-shaped function. Simulations using neural fields show the inverted U-shaped RT distribution as an emergent consequence of lateral inhibition. Weakening inhibition through learning shortens RTs, which is modeled through topographically reorganized inhibition. Whereas changes in decision making are often regarded as an outcome of higher cortical areas, our data show that the spatial layout of interaction processes within representational maps contributes to selection and decision-making processes