A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains

This paper aims to provide a non-technical overview of multidimensional scaling (MDS) so that a broader population of psychologists, in particular, will consider using this statistical procedure. A brief description regarding the type of data used in MDS, its acquisition and analyses via MDS is provided. Also included is a commentary on the unique challenges associated with assessing the output of MDS. Our second aim, by way of discussing representative studies, is to highlight and evaluate the utility of this method in various domains in psychology

Tactile perceptual dimensions: a study with light-weight wool fabrics

Volume 5024 of the series Lecture Notes in Computer Science.The aim of this study was to identify the tactile dimensions in the discrimination of light-weight wool fabrics. The participants judged the overall similarity between 21 light-weight wool fabrics using free sorting tasks. The fabrics were evaluated using active touch with limited exploratory procedure. Multidimensional scaling (MDS) was used to generate the perceptual space, revealing one dimension of tactile perception. Finally, through regression analysis we were able to interpret this dimension, using verbal attributes and physical properties of the fabrics. We discuss the relevance of the stimuli properties and the associations between verbal attributes and between physical properties, on the evaluation of the fabrics, considering its theoretical implications

Tactile perception of randomly rough surfaces

Most everyday surfaces are randomly rough and self-similar on sufficiently small scales. We investigated the tactile perception of randomly rough surfaces using 3D-printed samples, where the topographic structure and the statistical properties of scale-dependent roughness were varied independently. We found that the tactile perception of similarity between surfaces was dominated by the statistical micro-scale roughness rather than by their topographic resemblance. Participants were able to notice differences in the Hurst roughness exponent of 0.2, or a difference in surface curvature of 0.8 mm−1 for surfaces with curvatures between 1 and 3 mm−1. In contrast, visual perception of similarity between color-coded images of the surface height was dominated by their topographic resemblance. We conclude that vibration cues from roughness at the length scale of the finger ridge distance distract the participants from including the topography into the judgement of similarity. The interaction between surface asperities and fingertip skin led to higher friction for higher micro-scale roughness. Individual friction data allowed us to construct a psychometric curve which relates similarity decisions to differences in friction. Participants noticed differences in the friction coefficient as small as 0.035 for samples with friction coefficients between 0.34 and 0.45

Inspection System for Benchmarking of Perceived and Technical Characteristics of Surfaces

Haptic perception of texture is an important tool in the evaluation of products such as smartphones, automobiles, kitchen appliances etc. Successful companies thus aim at actively designing the haptic impression of their products to the haptic preferences of their customers. However doing so requires information as to how comparable product surfaces are perceived by the customer and how these surfaces can be characterized technically. The perceived haptic impression of an object can be estimated using human studies, whereas its technical characteristics can be measured. However, direct correlation between perceived and standard measurements often shows poor significance, thus perceived haptic impression can not be simply derived from standard measurements. It remains to be analysed, if alternative measurements can provide a more significant characterization of surfaces in terms of their perceived haptic impression. To allow a more efficient estimation of the perceived impression of an object, an automatic inspection system is presented. This system consists of a force controlled robot driving a biomimetic sensor by the company Syntouch, providing a vibration signal during traverse over a surface. The paper presents results of a study to correlate the sensor signals to the perceived roughness of different surfaces evaluated by human subjects and set this in comparison to a correlation between standard roughness values and the perceived roughness

Tactile perception of randomly rough surfaces

Most everyday surfaces are randomly rough and self-similar on sufficiently small scales. We investigated the tactile perception of randomly rough surfaces using 3D-printed samples, where the topographic structure and the statistical properties of scale-dependent roughness were varied independently. We found that the tactile perception of similarity between surfaces was dominated by the statistical micro-scale roughness rather than by their topographic resemblance. Participants were able to notice differences in the Hurst roughness exponent of 0.2, or a difference in surface curvature of 0.8 mm−1 for surfaces with curvatures between 1 and 3 mm−1. In contrast, visual perception of similarity between color-coded images of the surface height was dominated by their topographic resemblance. We conclude that vibration cues from roughness at the length scale of the finger ridge distance distract the participants from including the topography into the judgement of similarity. The interaction between surface asperities and fingertip skin led to higher friction for higher micro-scale roughness. Individual friction data allowed us to construct a psychometric curve which relates similarity decisions to differences in friction. Participants noticed differences in the friction coefficient as small as 0.035 for samples with friction coefficients between 0.34 and 0.45

A similarity-based approach to perceptual feature validation

Which object properties matter most in human perception may well vary according to sensory modality, an important consideration for the design of multimodal interfaces. In this study, we present a similarity-based method for comparing the perceptual importance of object properties across modalities and show how it can also be used to perceptually validate computational measures of object properties. Similarity measures for a set of three-dimensional (3D) objects varying in shape and texture were gathered from humans in two modalities (vision and touch) and derived from a set of standard 2D and 3D computational measures (image and mesh subtraction, object perimeter, curvature, Gabor jet filter responses, and the Visual Difference Predictor (VDP)). Multidimensional scaling (MDS) was then performed on the similarity data to recover configurations of the stimuli in 2D perceptual/computational spaces. These two dimensions corresponded to the two dimensions of variation in the stimulus set: shape and texture. In the human visual space, shape strongly dominated texture. In the human haptic space, shape and texture were weighted roughly equally. Weights varied considerably across subjects in the haptic experiment, indicating that different strategies were used. Maps derived from shape-dominated computational measures provided good fits to the human visual map. No single computational measure provided a satisfactory fit to the map derived from mean human haptic data, though good fits were found for individual subjects; a combination of measures with individually-adjusted weights may be required to model the human haptic similarity judgments. Our method provides a high-level approach to perceptual validation, which can be applied in both unimodal and multimodal interface design

Haptic adaptation to slant: No transfer between exploration modes

Human touch is an inherently active sense: to estimate an object’s shape humans often move their hand across its surface. This way the object is sampled both in a serial (sampling different parts of the object across time) and parallel fashion (sampling using different parts of the hand simultaneously). Both the serial (moving a single finger) and parallel (static contact with the entire hand) exploration modes provide reliable and similar global shape information, suggesting the possibility that this information is shared early in the sensory cortex. In contrast, we here show the opposite. Using an adaptation-and-transfer paradigm, a change in haptic perception was induced by slant-adaptation using either the serial or parallel exploration mode. A unified shape-based coding would predict that this would equally affect perception using other exploration modes. However, we found that adaptation-induced perceptual changes did not transfer between exploration modes. Instead, serial and parallel exploration components adapted simultaneously, but to different kinaesthetic aspects of exploration behaviour rather than object-shape per se. These results indicate that a potential combination of information from different exploration modes can only occur at down-stream cortical processing stages, at which adaptation is no longer effective