The dimensional analysis of a new series of facial expressions.

Engen, Trygg; Levy, Nissim; Schlosberg, Harold

doi:10.1037/h0047240

Cited by 68 publications

(32 citation statements)

References 7 publications

(7 reference statements)

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“…If the set of constraints is correlated, this will give the configuration a certain shape (see Kruskal, 1972, for a discussion of this concept), which can be studied, for instance, by computing its principal components. This approach will now be illustrated with a classical example concerning the perception of facial expressions, using the dissimilarities collected by Abelson and Sermat (1962) and three median rating scale variables obtained for the same 13 objects by Engen, Levy, and Schlosberg (1958). The objects are selected photographs from the &dquo;Lightfoot-scries&dquo; in which an actress expresses the emotions listed in Table 3.…”

Section: Symmetry In Treatment Of Setsmentioning

confidence: 99%

Constrained Multidimensional Scaling, Including Confirmation

Heiser

Meulman

1983

Applied Psychological Measurement

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Constrained and confirmatory multidimensional scaling (MDS) are not equivalent. Constraints refer to the translation of either theoretical or data analytical objectives into computational specifications. Confirmation refers to a study of the balance between systematic and random variation in the data for modeling of the systematic part. Among the topics discussed from this perspective are the role of substantive theory in MDS studies, the type of constraints currently envisaged, and the relationships with other data analysis methods. This paper points out the possibility of using either sampling models or resampling schemes to study the stability of MDS solutions. Parallel to Akaike's (1974) information criterion for choosing one out of many models for the same data, a general stability criterion is proposed and illustrated, based on the ratio of within to total spread of configurations issued from resampling. Scaling Analysis and OptimizationThe coupling of constraints and scaling, although not new, is still extremely vital. The first modem scaling model, developed by Thurstone (e.g., 1927), immediately entailed the necessity of a judicious choice of constraints, as classified in the five famous 6bc~sese&dquo; These were internal constraints in the sense that the modified model would still accommodate the same set of observations, possibly less accurately, but with more predictive value. Guttman (1946) took a number of important additional steps. He showed that Thurstone's s method, relying on an underlying multivariate normal distribution, could be replaced by an approach in which the optimization of a goodness-of-fit function is the central concept. This was an extension of similar developments occurring at that time in test theory and in contingency table analysis (for current reviews, s~e l~lishisato, 1980, and Gin, 1981) to the scaling of paired comparison and rank order data. Moreover, Guttman showed that introducing an additional type of constraint was not a problem. The proper objective would be to find scale values for the stimulus objects that are &dquo;best able to reproduce the judgment of each person in the population on each comparison&dquo; (Guttman, 1946, p. 14~) and that are also perfectly linearly predictable from a predeterrrgined set of (design) variables.Although through hindsight it seems clear that it would have been possible to formulate a constrained multidimensional scaling (MDS) method within Guttman's framework, the early paradigm for MDS was to split the problem into two distinct steps. From the very first published psychometric application by Klingberg (1941)-based on earlier work by Richardson (1938)-to the authoritative presentation in Torgerson's (1958) book, it is evident that the main burden and first step of the analysis was to find a one-dimensional scale of psychological distances between pairs of stimulus objects. This was usually done by any of the varat UNIVERSITE DE MONTREAL on June 23, 2015 apm.sagepub.com Downloaded from

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Section: Symmetry In Treatment Of Setsmentioning

confidence: 99%

Constrained Multidimensional Scaling, Including Confirmation

Heiser

Meulman

1983

Applied Psychological Measurement

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“…In Figure 4(a), we have the additional information on the best partition in five classes (the choice of the number of classes will be discussed below), which is better represented than in the plot of the ordinary MDS. MDSCLUREG was also applied to the squared Euclidean distance matrix, computed between facial expressions given by Engen, Levy and Schlosberg (1958) on the three-dimensional scales in Table 2 (PU, AR, TS). In Figure 4(b), the scatter plot of the 2-D solution with five-clusters is shown.…”

Section: Facial Expression Datamentioning

confidence: 99%

Simultaneous classification and multidimensional scaling with external information

2005

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For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis ( CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures ( CA and MDS, or MDS and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets

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“…For example, Engen, Levy & Schlosberg (39) carried out a dimensional analysis of facial expressions. Using 48 photo graphs, they asked their subjects to rate in terms of "pleasant-unpleasant," "attention-rejection," and "sleep-tension."…”

Section: Current Researchmentioning

confidence: 99%

Perceptual Learning

Drever¹

1960

Annu. Rev. Psychol.

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The dimensional analysis of a new series of facial expressions.

Cited by 68 publications

References 7 publications

Constrained Multidimensional Scaling, Including Confirmation

Constrained Multidimensional Scaling, Including Confirmation

Simultaneous classification and multidimensional scaling with external information

Perceptual Learning

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