Perceptual dimensions of tactile surface texture: A multidimensional scaling analysis
TL;DR: Roughness-smoothness and hardness-softness were found to be robust and orthogonal dimensions; the third dimension did not correspond closely with any of the rating scales used, but post hoc inspection of the data suggested that it may reflect the compressional elasticity (“springiness”) of the surface.
Abstract: The purpose of this study was to examine the subjective dimensionality of tactile surface texture perception. Seventeen tactile stimuli, such as wood, sandpaper, and velvet, were moved across the index finger of the subject, who sorted them into categories on the basis of perceived similarity. Multidimensional scaling (MDS) techniques were then used to position the stimuli in a perceptual space on the basis of combined data of 20 subjects. A three-dimensional space was judged to give a satisfactory representation of the data. Subjects’ ratings of each stimulus on five scales representing putative dimensions of perceived surface texture were then fitted by regression analysis into the MDS space. Roughness-smoothness and hardness-softness were found to be robust and orthogonal dimensions; the third dimension did not correspond closely with any of the rating scales used, but post hoc inspection of the data suggested that it may reflect the compressional elasticity (“springiness”) of the surface.
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TL;DR: Key aspects of performing MDS are discussed, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output.
Abstract: The concept of similarity, or a sense of 'sameness' among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis. WIREs Cogn Sci 2013, 4:93-103. doi: 10.1002/wcs.1203 This article is categorized under: Psychology > Perception and Psychophysics.
2,577 citations
Dissertation•
01 Jan 1995
TL;DR: Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.
Abstract: Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.
354 citations
TL;DR: Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities, and the method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.
Abstract: In order to endow robots with humanlike abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac®) we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of “prior experience” to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate the texture from a set of plausible candidates, a process we call Bayesian exploration. Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures generally required a small number of well-chosen exploratory movements (median=5) and yielded a 95.4% success rate. The method of “Bayesian exploration” developed and tested in this paper may generalize well to other cognitive problems.
335 citations
Cites background from "Perceptual dimensions of tactile su..."
...This dimension has been suggested to be relatively orthogonal to the perceptual dimension of roughness (Hollins et al., 1993)....
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...Early studies into the perceptive dimensionality of surfaces have suggested that sticky/slippery, hard/soft, and rough/smooth represent three independent dimensions of a surface (Hollins et al., 1993)....
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TL;DR: Four types of mechanoreceptive afferents innervate the glabrous skin of the hand support the idea that each afferent type serves a distinctly different sensory function and that these functions explain most of tactual perceptual function.
Abstract: Four types of mechanoreceptive afferents innervate the glabrous skin of the hand. Evidence from more than three decades of combined psychophysical and neurophysiological research supports the idea that each afferent type serves a distinctly different sensory function and that these functions explain most of tactual perceptual function. The available evidence supports the following hypotheses: (1) The slowly adapting type 1 system provides the information on which form and texture perception are based. (2) The cutaneous rapidly adapting system provides information about minute skin motion and, thereby, plays a critical role in grip control. (3) The Pacinian system is responsible for the detection and perception of distant events by vibrations transmitted through objects, probes, and tools held in the hand. (4) The slowly adapting type 2 system provides information for the perception of hand conformation and for the perception of forces acting on the hand. The authors review the evidence on which these hypotheses are based. They also review the role of proprioceptive afferents in the perception of hand conformation because they appear to play a significant role along with cutaneous afferents.
315 citations
TL;DR: It is concluded that tactile textures are composed of three prominent psychophysical dimensions that are perceived as roughness/smoothness, hardness/softness, and coldness/warmness.
Abstract: This paper reviews studies on the tactile dimensionality of physical properties of materials in order to determine a common structure for these dimensions. Based on the commonality found in a number of studies and known mechanisms for the perception of physical properties of textures, we conclude that tactile textures are composed of three prominent psychophysical dimensions that are perceived as roughness/smoothness, hardness/softness, and coldness/warmness. The roughness dimension may be divided into two dimensions: macro and fine roughness. Furthermore, it is reasonable to consider that a friction dimension that is related to the perception of moistness/dryness and stickiness/slipperiness exists. Thus, the five potential dimensions of tactile perception are macro and fine roughness, warmness/coldness, hardness/softness, and friction (moistness/dryness, stickiness/slipperiness). We also summarize methods such as psychological experiments and mathematical approaches for structuring tactile dimensions and their limitations.
312 citations
Cites background or methods from "Perceptual dimensions of tactile su..."
...Classification methods were used in [20], [21], [22], [23]....
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...[20] investigated the perceptual dimensions of 17 materials, including paper, plastic, and velvet....
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...Validation using adjective labels was performed in [19], [20], [21], [25]....
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...[20] attempted to specify the perceptual dimensions using five adjective labels: rough/smooth (fine roughness), flat/bumpy (macro roughness), hard/soft, slippery/sticky, and warm/cool....
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References
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TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Abstract: Multidimensional scaling is the problem of representingn objects geometrically byn points, so that the interpoint distances correspond in some sense to experimental dissimilarities between objects. In just what sense distances and dissimilarities should correspond has been left rather vague in most approaches, thus leaving these approaches logically incomplete. Our fundamental hypothesis is that dissimilarities and distances are monotonically related. We define a quantitative, intuitively satisfying measure of goodness of fit to this hypothesis. Our technique of multidimensional scaling is to compute that configuration of points which optimizes the goodness of fit. A practical computer program for doing the calculations is described in a companion paper.
6,875 citations
TL;DR: The numerical methods required in the approach to multi-dimensional scaling are described and the rationale of this approach has appeared previously.
Abstract: We describe the numerical methods required in our approach to multi-dimensional scaling. The rationale of this approach has appeared previously.
4,561 citations
TL;DR: Two experiments establish links between desired knowledge about objects and hand movements during haptic object exploration, and establish that in free exploration, a procedure is generally used to acquire information about an object property, not because it is merely sufficient, butBecause it is optimal or even necessary.
1,723 citations
Book•
29 Mar 2012
TL;DR: The Scientific Use of Factor Analysis In Behavioral and Life Sciences%0D as mentioned in this paper is a good book to read and it can be used as a good buddy at any time, especially when taking a train, hesitating for checklist, and awaiting a person or other.
Abstract: Download PDF Ebook and Read OnlineThe Scientific Use Of Factor Analysis In Behavioral And Life Sciences%0D. Get The Scientific Use Of Factor Analysis In Behavioral And Life Sciences%0D Well, publication the scientific use of factor analysis in behavioral and life sciences%0D will make you closer to exactly what you are eager. This the scientific use of factor analysis in behavioral and life sciences%0D will be consistently good buddy at any time. You could not forcedly to always complete over reviewing a publication basically time. It will be simply when you have leisure and spending few time to make you really feel pleasure with just what you read. So, you could get the meaning of the notification from each sentence in guide. the scientific use of factor analysis in behavioral and life sciences%0D. In what situation do you like reviewing a lot? What concerning the kind of the book the scientific use of factor analysis in behavioral and life sciences%0D The should read? Well, everyone has their own reason should read some books the scientific use of factor analysis in behavioral and life sciences%0D Primarily, it will relate to their need to obtain expertise from the publication the scientific use of factor analysis in behavioral and life sciences%0D as well as want to check out simply to get home entertainment. Books, story book, and various other enjoyable books end up being so preferred today. Besides, the scientific e-books will also be the very best need to select, especially for the pupils, educators, physicians, entrepreneur, and also other professions that enjoy reading. Do you recognize why you must read this site and also just what the relation to checking out book the scientific use of factor analysis in behavioral and life sciences%0D In this contemporary age, there are numerous methods to get guide and they will be considerably less complicated to do. One of them is by obtaining the publication the scientific use of factor analysis in behavioral and life sciences%0D by online as just what we tell in the web link download. Guide the scientific use of factor analysis in behavioral and life sciences%0D can be a choice because it is so appropriate to your necessity now. To obtain guide online is really simple by simply downloading them. With this chance, you could read the publication wherever as well as whenever you are. When taking a train, hesitating for checklist, and awaiting a person or other, you could review this on the internet e-book the scientific use of factor analysis in behavioral and life sciences%0D as a good close friend again.
1,664 citations
TL;DR: In this paper, a new procedure is discussed which fits either the weighted or simple Euclidian model to data that may (a) be defined at either the nominal, ordinal, interval or ratio levels of measurement; (b) have missing observations; (c) be symmetric or asymmetric; (d) be conditional or unconditional; (e) be replicated or unreplicated; (f) be continuous or discrete.
Abstract: A new procedure is discussed which fits either the weighted or simple Euclidian model to data that may (a) be defined at either the nominal, ordinal, interval or ratio levels of measurement; (b) have missing observations; (c) be symmetric or asymmetric; (d) be conditional or unconditional; (e) be replicated or unreplicated; and (f) be continuous or discrete. Various special cases of the procedure include the most commonly used individual differences multidimensional scaling models, the familiar nonmetric multidimensional scaling model, and several other previously undiscussed variants. The procedure optimizes the fit of the model directly to the data (not to scalar products determined from the data) by an alternating least squares procedure which is convergent, very quick, and relatively free from local minimum problems. The procedure is evaluated via both Monte Carlo and empirical data. It is found to be robust in the face of measurement error, capable of recovering the true underlying configuration in the Monte Carlo situation, and capable of obtaining structures equivalent to those obtained by other less general procedures in the empirical situation.
961 citations