Anchoring In Action: Manual Estimates Of Slant Are Powerfully Biased Toward Initial Hand Orientation And Are Correlated With Verbal Report

People verbally overestimate hill slant by approximately 15 degrees to 25 degrees, whereas manual estimates (e. g., palm board measures) are thought to be more accurate. The relative accuracy of palm boards has contributed to the widely cited theoretical claim that they tap into an accurate, but unconscious, motor representation of locomotor space. In the current work, 4 replications (total N = 204) carried out by 2 different laboratories tested an alternative anchoring hypothesis that manual action measures give low estimates because they are always initiated from horizontal. The results of all 4 replications indicate that the bias from response anchoring can entirely account for the difference between manual and verbal estimates. Moreover, consistent correlations between manual and verbal estimates given by the same observers support the conclusion that both measures are based on the same visual representation. Concepts from the study of judgment under uncertainty apply even to action measures in information rich environments

Audio-tactile stimuli to improve health and well-being : a preliminary position paper

From literature and through common experience it is known that stimulation of the tactile (touch) sense or auditory (hearing) sense can be used to improve people's health and well-being. For example, to make people relax, feel better, sleep better or feel comforted. In this position paper we propose the concept of combined auditory-tactile stimulation and argue that it potentially has positive effects on human health and well-being through influencing a user's body and mental state. Such effects have, to date, not yet been fully explored in scientific research. The current relevant state of the art is briefly addressed and its limitations are indicated. Based on this, a vision is presented of how auditory-tactile stimulation could be used in healthcare and various other application domains. Three interesting research challenges in this field are identified: 1) identifying relevant mechanisms of human perception of combined auditory-tactile stimuli; 2) finding methods for automatic conversions between audio and tactile content; 3) using measurement and analysis of human bio-signals and behavior to adapt the stimulation in an optimal way to the user. Ideas and possible routes to address these challenges are presented

Quantifying perception of nonlinear elastic tissue models using multidimensional scaling

Simplified soft tissue models used in surgical simulations cannot perfectly reproduce all material behaviors. In particular, many tissues exhibit the Poynting effect, which results in normal forces during shearing of tissue and is only observed in nonlinear elastic material models. In order to investigate and quantify the role of the Poynting effect on material discrimination, we performed a multidimensional scaling (MDS) study. Participants were presented with several pairs of shear and normal forces generated by a haptic device during interaction with virtual soft objects. Participants were asked to rate the similarity between the forces felt. The selection of the material parameters – and thus the magnitude of the shear\ud and normal forces – was based on a pre-study prior to the MDS experiment. It was observed that for nonlinear elastic tissue models exhibiting the Poynting effect, MDS analysis indicated that both shear and normal forces affect user perception

Wearable Haptic Devices for Gait Re-education by Rhythmic Haptic Cueing

This research explores the development and evaluation of wearable haptic devices for gait sensing and rhythmic haptic cueing in the context of gait re-education for people with neurological and neurodegenerative conditions. Many people with long-term neurological and neurodegenerative conditions such as Stroke, Brain Injury, Multiple Sclerosis or Parkinson’s disease suffer from impaired walking gait pattern. Gait improvement can lead to better fluidity in walking, improved health outcomes, greater independence, and enhanced quality of life. Existing lab-based studies with wearable devices have shown that rhythmic haptic cueing can cause immediate improvements to gait features such as temporal symmetry, stride length, and walking speed. However, current wearable systems are unsuitable for self-managed use for in-the-wild applications with people having such conditions. This work aims to investigate the research question of how wearable haptic devices can help in long-term gait re-education using rhythmic haptic cueing. A longitudinal pilot study has been conducted with a brain trauma survivor, providing rhythmic haptic cueing using a wearable haptic device as a therapeutic intervention for a two-week period. Preliminary results comparing pre and post-intervention gait measurements have shown improvements in walking speed, temporal asymmetry, and stride length. The pilot study has raised an array of issues that require further study. This work aims to develop and evaluate prototype systems through an iterative design process to make possible the self-managed use of such devices in-the-wild. These systems will directly provide therapeutic intervention for gait re-education, offer enhanced information for therapists, remotely monitor dosage adherence and inform treatment and prognoses over the long-term. This research will evaluate the use of technology from the perspective of multiple stakeholders, including clinicians, carers and patients. This work has the potential to impact clinical practice nationwide and worldwide in neuro-physiotherapy

Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment

Haptic displays are commonly limited to transmitting a discrete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the linear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and personalisation through sensitivity adjustment increased decoding precision

Exploring relationships between touch perception and surface physical properties

This paper reports a study of materials for confectionery packaging. The aim was to explore the touch perceptions of textures and identify their relationships with the surfaces' physical properties. Thirty-seven tactile textures were tested including 22 cardboards, nine flexible materials and six laminate boards. Semantic differential questionnaires were administered to assess responses to touching the textures against six word pairs: warm-cold, slippery-sticky, smooth,-rough, hard-soft, bumpy-flat, and wet-dry. Four physical measurements were conducted to characterize the surfaces' roughness, compliance, friction, and the rate of cooling of an artificial finger when touching the surface. Correlation and regression analyses were carried out to identify the relationships between the people's responses and the physical measurements. Results show that touch perception is often associated with more than one physical property, and the strength and form of the combined contribution can be represented by a regression model. © 2009 Chen, Shao, Barnes, Childs, & Henson

A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing

Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer based sensing can be used for material recognition due to differences in the thermal properties of materials. While data-driven methods have shown promise for this recognition problem, many factors can influence performance, including sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the duration of contact. We present a physics-based mathematical model that predicts material recognition performance given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a real robot with 12 materials. Our model predicted the material recognition performance of support vector machine (SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we also provide insight into the roles of various factors on recognition performance, such as the temperature difference between the sensor and the object. Overall, our results suggest that our model could be used to help design better thermal sensors for robots and enable robots to use them more effectively.Comment: This article is currently under review for possible publicatio