Similarity After Goodman

Decock, Lieven; Douven, Igor

doi:10.1007/s13164-010-0035-y

Cited by 50 publications

(33 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, more sophisticated versions of the geometric model (for an overall framework, see Gärdenfors, 2004) can account for these anomalies (Decock and Douven, 2011). The anomalies are accounted for by allowing flexible selection and weighting of the dimensions of the space.…”

Section: Introductionmentioning

confidence: 99%

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

Kriegeskorte¹,

Mur²

2012

Front. Psychology

208

300

View full text Add to dashboard Cite

The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request.

show abstract

Section: Introductionmentioning

confidence: 99%

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

Kriegeskorte¹,

Mur²

2012

Front. Psychology

208

300

View full text Add to dashboard Cite

show abstract

“…The geometrical model of the mental representation described above (e.g., Shepard, 1958; Torgerson, 1958; Edelman, 1998) can account for a great variety of empirical findings and the most recent versions (e.g., Gärdenfors, 2004) also account for phenomena, such as context dependence, that were initially thought to be difficult to accommodate (Goodman, 1972; Tversky, 1977; for a recent review, see Decock and Douven, 2011). Importantly, the geometrical model enables a direct comparison between brain representational similarity and similarity judgments.…”

Section: Introductionmentioning

confidence: 99%

Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation

Mur¹,

Meys²,

Bodurka³

et al. 2013

Front. Psychol.

146

193

View full text Add to dashboard Cite

Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we show that objects that elicit similar activity patterns in human IT (hIT) tend to be judged as similar by humans. The IT representation explained the human judgments better than early visual cortex, other ventral-stream regions, and a range of computational models. Human similarity judgments exhibited category clusters that reflected several categorical divisions that are prevalent in the IT representation of both human and monkey, including the animate/inanimate and the face/body division. Human judgments also reflected the within-category representation of IT. However, the judgments transcended the IT representation in that they introduced additional categorical divisions. In particular, human judgments emphasized human-related additional divisions between human and non-human animals and between man-made and natural objects. hIT was more similar to monkey IT than to human judgments. One interpretation is that IT has evolved visual-feature detectors that distinguish between animates and inanimates and between faces and bodies because these divisions are fundamental to survival and reproduction for all primate species, and that other brain systems serve to more flexibly introduce species-dependent and evolutionarily more recent divisions.

show abstract

“…Numerical taxonomy, also known as the phenetics school (from the Greek phaineros for "appearance") classified groups according to their "overall similarity." This fell prey to the problems discussed by Nelson Goodman; as he says, similarity is cheap (see Decock and Douven [2011] for a discussion):…”

Section: Theoretically Defined Speciesmentioning

confidence: 99%

The Hunting of the SNaRC: A Snarky Solution to the Species Problem

Mishler¹,

Wilkins²

2018

Philosophy, Theory, and Practice in Biology

View full text Add to dashboard Cite

We argue that the logical outcome of the cladistics revolution in biological systematics, and the move towards rankless phylogenetic classification of nested monophyletic groups as formalized in the PhyloCode, is to eliminate the species rank along with all the others and simply name clades. We propose that the lowest level of formally named clade be the SNaRC, the Smallest Named and Registered Clade. The SNaRC is an epistemic level in the classification, not an ontic one. Naming stops at that level because there is no currently acceptable evidence for clades within it, not because no smaller clades exist. Later, included clades may be named. They would then become the SNaRCs, while the original SNaRC would keep its original name. We argue that all theoretical tasks of biology, in evolution and ecology, as well as practical tasks such as conservation assessment, are better approached using this rankless phylogenetic approach.

show abstract

Similarity After Goodman

Cited by 50 publications

References 25 publications

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

Human Object-Similarity Judgments Reflect and Transcend the Primate-IT Object Representation

The Hunting of the SNaRC: A Snarky Solution to the Species Problem

Contact Info

Product

Resources

About