Alignability-based free categorization

Clapper, John P.

doi:10.1016/j.cognition.2017.02.003

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Thus, categorization itself may be categorized by task or goal (Ashby & Maddox, 2011), neural circuit (Iordan et al, 2015; Nomura & Reber, 2008), utility (J. D. Smith, 2014), and context (Barsalou, 1987; Koriat & Sorka, 2015, 2017; Roth & Shoben, 1983). The most common and accepted theoretical mechanisms for categorization are still rule based, defining clear boundaries between categories (Davis & Love, 2010; Goldstone & Kersten, 2003; Sloutsky, 2003; E. E. Smith, Langston, & Nisbett, 1992) and their cortical representations (Iordan et al, 2015, 2016; Kriegeskorte et al, 2008), and prototype-based or exemplar-based, defining family resemblance (Ashby & Maddox, 2011; Goldstone & Kersten, 2003; Iordan et al, 2016; Maddox & Ashby, 1993; Medin, Altom, & Murphy, 1984; Nosofsky, 2011; Posner and Keele, 1968; Rosch, 1973; Rosch, Mervis, et al, 1976; see also Clapper, 2017).…”

mentioning

confidence: 99%

Relating categorization to set summary statistics perception

Khayat

Hochstein

2019

Atten Percept Psychophys

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Two cognitive processes have been explored that compensate for the limited information that can be perceived and remembered at any given moment. The first parsimonious cognitive process is object categorization. We naturally relate objects to their category, assume they share relevant category properties, often disregarding irrelevant characteristics. Another scene organizing mechanism is representing aspects of the visual world in terms of summary statistics. Spreading attention over a group of objects with some similarity, one perceives an ensemble representation of the group. Without encoding detailed information of individuals, observers process summary data concerning the group, including set mean for various features (from circle size to face expression). Just as categorization may include/depend on prototype and intercategory boundaries, so set perception includes property mean and range. We now explore common features of these processes. We previously investigated summary perception of low-level features with a rapid serial visual presentation (RSVP) paradigm and found that participants perceive both the mean and range extremes of stimulus sets, automatically, implicitly, and on-the-fly, for each RSVP sequence, independently. We now use the same experimental paradigm to test category representation of high-level objects. We find participants perceive categorical characteristics better than they code individual elements. We relate category prototype to set mean and same/different category to in/out-of-range elements, defining a direct parallel between low-level set perception and high-level categorization. The implicit effects of mean or prototype and set or category boundaries are very similar. We suggest that object categorization may share perceptual-computational mechanisms with set summary statistics perception.

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confidence: 99%

Relating categorization to set summary statistics perception

Khayat

Hochstein

2019

Atten Percept Psychophys

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“…'bad guy'. According to this prior information, the state changed to a new state Ψ t1 at the time of t 1 , the coordinate can be denoted as Ψ (1). For between good and bad, get a different quantum probability distribution.…”

Section: Methodsmentioning

confidence: 99%

“…Categorization has a important influence on a decision-making result [1,2,3]. Some researchers have discovered that there is a classification fallacy in the categorization process.…”

Section: Introductionmentioning

confidence: 99%

A Negation Quantum Decision Model to Predict the Interference Effect in Categorization

Wu,

Deng

2021

Preprint

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Categorization is a significant task in decision-making, which is a key part of human behavior. An interference effect is caused by categorization in some cases, which breaks the total probability principle. A negation quantum model (NQ model) is developed in this article to predict the interference. Taking the advantage of negation to bring more information in the distribution from a different perspective, the proposed model is a combination of the negation of a probability distribution and the quantum decision model. Information of the phase contained in quantum probability and the special calculation method to it can easily represented the interference effect. The results of the proposed NQ model is closely to the real experiment data and has less error than the existed models.

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“…Billman and Davies (2005), for example, show that participants sorting complex objects prefer constructing taxonomic systems in which within‐category commonalities and between‐category differences were aligned. Clapper (2017) also shows that alignability is more important in free‐sort categorization than commonalities of local elements. These results suggest that when subjects sort complex stimuli, the resulting taxonomy mirrors the similarity relationship, discussed above, among contrast classes in conceptual hierarchy.…”

Section: Contrast Classes As Constraining Similarity Judgements In Camentioning

confidence: 99%

“…3030This is not to imply that all items are compared simultaneously (see discussion in Clapper, 2017). …”

mentioning

confidence: 99%

Similarity Reimagined (with Implications for a Theory of Concepts)

Bloch-Mullins

2019

Theoria

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Similarity‐based theories of concepts have a broad intuitive appeal and have been successful in accounting for various phenomena related to the formation and application of concepts. Their adequacy as theories of concepts has been questioned, however, as similarity is often taken as too flexible, too unconstrained, to be explanatory of categorization. In this article, I propose an account of similarity that takes the “foil” against which the target items are measured as integral to the process of comparison, making the similarity relation a fundamentally triadic one. I argue that this account delivers more internal constraints on the process of comparison than the traditional, dyadic account of similarity does. Finally, I propose that this account is advantageous for a similarity‐based theory of concepts, as it easily integrates with the notion of conceptual taxonomy and uses it to deliver additional constraints on categorization. On such a theory of concepts, the foil, whose variability is in large part responsible for the flexible nature of similarity judgements, is turned within the framework of conceptual taxonomy to the very element that constrains similarity judgements in categorization. Therefore, this notion of similarity mitigates the worry that similarity is too flexible to be explanatory of categorization.

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Alignability-based free categorization

Cited by 7 publications

References 43 publications

Relating categorization to set summary statistics perception

Relating categorization to set summary statistics perception

A Negation Quantum Decision Model to Predict the Interference Effect in Categorization

Similarity Reimagined (with Implications for a Theory of Concepts)

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