Dimensions of Color Vision

Ekman, Gösta

doi:10.1080/00223980.1954.9712953

Cited by 223 publications

(103 citation statements)

References 2 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…For example, numerous experiments have obtained judgments from people of the subjective similarity of color samples. In an experiment by Ekman (1954), subjects were asked to rate the similarity of all possible pairwise combinations of 14 spectral (single-wavelength) colors. The results can be plotted as a series of 14 generalization gradients: for each color (wavelength), the height of the gradient is just inversely related to the judged similarity between it and any of the other 13 wavelengths.…”

Section: Similaritymentioning

confidence: 99%

Adaptive Behavior and Learning

Staddon

2016

View full text Add to dashboard Cite

Every day at about 4:30, Jazz, a Hungarian Vizsla dog, leaps up on the sofa and looks out for his owner who always comes home at 5:00. He doesn't need an internal clock because he has an acute sense of smell that allows him to measure how long his master has been absent. Explaining complex behavior in simple ways, this book is a fascinating exploration of the evolution, development and processes of learning in animals. Now in its second edition, there is increased emphasis on development, evolution and dynamics; new accounts of taxic orientation, reflex induction, habituation and operant learning in organisms; more discussion of spatial learning and the processes underlying it; expanded chapters on choice and completely new chapters on molar laws, classical conditioning theories and comparative cognition. J. E. R. Staddon provides a definitive summary of contemporary theoretical understanding suitable for graduates and advanced undergraduates.

show abstract

Section: Similaritymentioning

confidence: 99%

Adaptive Behavior and Learning

Staddon

2016

View full text Add to dashboard Cite

show abstract

“…1 can be used even if none of the features is directly observed. We applied the model to a matrix containing human judgments of the similarity between all pairs of 14 pure-wavelength hues (38). The ring in Fig.…”

mentioning

confidence: 99%

The discovery of structural form

Kemp

Tenenbaum

2008

Proc. Natl. Acad. Sci. U.S.A.

440

441

View full text Add to dashboard Cite

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., ''bigger than'' or ''better than'') respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.cognitive development ͉ structure discovery ͉ unsupervised learning

show abstract

“…As a third example, we study the color similarity data collected by Ekman (1954). For these data, MDS finds a circular configuration with points ordered in terms of the physical wavelengths of the colors that they represent.…”

Section: Results: Real Data Applicationsmentioning

confidence: 99%

“…A second application uses ratings of 327 psychology students on the importance of personal values as guiding principles in their life Finally, a classic MDS application is on data on the subjective similarity of different colors by Ekman (1954). These data were used in the first papers on ordinal MDS by Shepard (1962) and Kruskal (1964) where it was shown that the structure of the observed similarities is an almost perfect color circle with very low Stress.…”

Section: Real Data Applicationsmentioning

confidence: 99%

The Choice of Initial Configurations in Multidimensional Scaling: Local Minima, Fit, and Interpretability

Borg¹,

Mair

2017

AJS

View full text Add to dashboard Cite

Multidimensional scaling (MDS) algorithms can easily end up in local minima, depending on the starting configuration. This is particularly true for 2-dimensional ordinal MDS. A simulation study shows that there can be many local minima that all have an excellent model fit (i.e., small Stress) even if they do not recover a known latent configuration very well, and even if they differ substantially among each other. MDS programs give the user only one supposedly Stress-optimal solution. We here present a procedure for analyzing all MDS solutions resulting from using a variety of different starting configurations. The solutions are compared in terms of fit and configurational similarity. This allows the MDS user to identify different types of solutions with acceptable Stress, if they exist, and then pick the one that is best interpretable.

show abstract

Dimensions of Color Vision

Cited by 223 publications

References 2 publications

Adaptive Behavior and Learning

Adaptive Behavior and Learning

The discovery of structural form

The Choice of Initial Configurations in Multidimensional Scaling: Local Minima, Fit, and Interpretability

Contact Info

Product

Resources

About