Similarity and dissimilarity as evidence in perceptual categorization

Stewart, Neil; Brown, Gordon D.

doi:10.1016/j.jmp.2005.06.001

Cited by 43 publications

(55 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, the results support the sign-andmagnitude model (Stewart & Brown, 2005), as the size of the contrast effect was an increasing function of the dissimilarity between the context and target stimuli.…”

Section: Resultssupporting

confidence: 78%

“…In corroboration of Experiment 1, the results of Experiment 2 support the sign-and-magnitude SD-GCM (Stewart & Brown, 2005), which predicts increasing contrast as the context and target stimuli become more dissimilar. No assimilation effect was apparent, although the slope of the context effect did appear to flatten when the stimuli were only one step apart.…”

Section: Resultssupporting

confidence: 74%

“…Plausible extensions to traditional similarity-based decision models are unable to account for contrast effects in this task. However, the SD-GCM (Stewart & Brown, 2005) does provide a potential account of the data. The sign-and-magnitude version of the SD-GCM predicts increasing contrast with increasing interstimulus distance and a negatively accelerated effect as distance becomes more extreme.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, the sign-only model received support. Despite this result, though, Stewart and Brown (2005) subsequently advanced the sign-andmagnitude model rather than the sign-only model. So it appears that before rejecting either model, further investigation is necessary.…”

Section: Similarity-based Models Of Categorizationmentioning

confidence: 98%

“…The Similarity-Dissimilarity Model An alternative account of interstimulus comparison has been proposed by Stewart and Brown (2005). Before describing their model, we first describe the category contrast effect that it was devised to explain.…”

Section: Similarity-based Models Of Categorizationmentioning

confidence: 99%

See 4 more Smart Citations

Comparison and Contrast in Perceptual Categorization.

Hampton

Estes

Simmons

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

View full text Add to dashboard Cite

People categorized pairs of perceptual stimuli that varied in both category membership and pairwise similarity. Experiments 1 and 2 showed categorization of 1 color of a pair to be reliably contrasted from that of the other. This similarity-based contrast effect occurred only when the context stimulus was relevant for the categorization of the target (Experiment 3). The effect was not simply owing to perceptual color contrast (Experiment 4), and it extended to pictures from common semantic categories (Experiment 5). Results were consistent with a sign-and-magnitude version of N. Stewart and G. D. A. Brown's (2005) similarity-dissimilarity generalized context model, in which categorization is affected by both similarity to and difference from target categories. The data are also modeled with criterion setting theory (M. Treisman & T. C. Williams, 1984), in which the decision criterion is systematically shifted toward the mean of the current stimuli.

show abstract

Section: Resultssupporting

confidence: 78%

Section: Resultssupporting

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Section: Similarity-based Models Of Categorizationmentioning

confidence: 98%

Section: Similarity-based Models Of Categorizationmentioning

confidence: 99%

See 3 more Smart Citations

Comparison and Contrast in Perceptual Categorization.

Hampton

Estes

Simmons

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

View full text Add to dashboard Cite

show abstract

The Locus of Semantics and the Decoding-Inferring Distinction

Sztencel

2018

Perspectives in Pragmatics, Philosophy &Amp; Psychology

View full text Add to dashboard Cite

Opposition-Based Computing

Tizhoosh

Ventresca

Rahnamayan

Studies in Computational Intelligence

View full text Add to dashboard Cite

Summary. Diverse forms of opposition are already existent virtually everywhere around us but the nature and significance of oppositeness is well understood only in specific contexts within the fields of philosophy, linguistics, psychology, logic and physics. The interplay between entities and opposite entities is apparently fundamental for maintaining universal balance. However, it seems that there is a gap regarding oppositional thinking in engineering, mathematics and computer science. Although many opposition-based techniques exist in these fields, the oppositional properties they employ are not usually directly studied. A better understanding of opposition could potentially establish new search, reasoning, optimization and learning schemes with a wide range of applications. For instance, improving convergence rate for hyperdimensional problems could be improved through the use of oppositional strategies.A large number of problems in engineering and science cannot be approached with conventional schemes and are generally handled with intelligent techniques such as evolutionary, neural, reinforcing and swarm-based techniques. These methodologies, however, suffer from high computational costs. In this work, the outlines of opposition-based computing, a proposed framework for computational intelligence, will be introduced. The underlying idea is simultaneous consideration of guess and opposite guess, estimate and opposite estimate, viewpoint and opposite viewpoint and so on in order to make better decisions in a shorter time, something that all aforementioned techniques could benefit from. The goal is to better understand the role of opposition within a computational intelligence context with the intention of improving existing or developing more powerful and robust approaches to handle complex problems.Due to its diversity, it is difficult, if not impossible, to uniquely and universally define the nature and the scope of what we call opposition. However, there is no doubt that we need a mathematical formalism if we are going to, at least to some degree, exploit the oppositional relationships in real-world systems. Hence, we attempt to establish a generic framework for computing with opposites in this chapter, a framework which may not mathematically capture the very essence of oppositeness in all systems accurately but it will be, as we hope, a point of departure for moving toward opposition-based computing. The ideas put forward in this chapter may not be shared by all those who work on some aspect of opposition (and usually without labeling it opposition), nevertheless, we believe that this framework can be useful for understanding and employing opposition.

show abstract

Similarity and dissimilarity as evidence in perceptual categorization

Cited by 43 publications

References 32 publications

Comparison and Contrast in Perceptual Categorization.

Comparison and Contrast in Perceptual Categorization.

The Locus of Semantics and the Decoding-Inferring Distinction

Opposition-Based Computing

Contact Info

Product

Resources

About