mteb/BrightProPsychologyRetrieval huggingface dataset:
Negative aspects  

The activity of sorting people according to subjective or objective criteria
can be seen as a negative process because of its tendency to lead to violence
from a group to another.  [55]  Indeed, similarities gather people who share
common traits but differences between groups can lead to tensions and then the
use of violence between those groups. The creation of social groups by people
is responsible of a hierarchization of relations between groups.  [55]  These
hierarchical relations participate in the promotion of stereotypes about
people and groups, sometimes based on subjective criteria. Social categories
can encourage people to associate stereotypes to groups of people. Associating
stereotypes to a group, and to people who belong to this group, can lead to
forms of discrimination towards people of this group.  [56]  The perception of
a group and the stereotypes associated with it have an impact on social
relations and activities.

Some social categories have more weight than others in society. For instance,
in history and still today, the category of "race" is one of the first
categories used to sort people. However, only a few categories of race are
commonly used such as "Black", "White", "Asian" etc. It participates in the
reduction of the multitude of ethnicities to a few categories based mostly on
people's skin color.  [57]

The process of sorting people creates a vision of the other as 'different',
leading to the dehumanization of people. Scholars talk about intergroup
relations with the concept of  social identity theory
 developed by H.
Tajfel.  [55]  Indeed, in history, many examples of social categorization have
led to forms of domination or violence from a dominant group to a dominated
group. Periods of colonisation are examples of times when people from a group
chose to dominate and control other people belonging to other groups because
they considered them as inferior. Racism, discrimination and violence are
consequences of social categorization and can occur because of it. When people
see others as different, they tend to develop hierarchical relation with other
groups.  [55]
Exemplar theory  

Main article:  Exemplar theory 

Another instance of the similarity-based approach to categorization, the
exemplar theory likewise compares the similarity of candidate category members
to stored memory representations. Under the exemplar theory, all known
instances of a category are stored in memory as exemplars. When evaluating an
unfamiliar entity's category membership, exemplars from potentially relevant
categories are retrieved from memory, and the entity's similarity to those
exemplars is summed to formulate a categorization decision.  [33]  Medin and
Schaffer's (1978)  Context model 
employs a  nearest neighbor  approach which, rather than summing an entity's
similarities to relevant exemplars, multiplies them to provide weighted
similarities that reflect the entity's proximity to relevant exemplars.  [36]
This effectively biases categorization decisions towards exemplars most
similar to the entity to be categorized.  [36]  [37]
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From Wikipedia, the free encyclopedia

Organization of things, objects, and ideas

For particular uses, see  Category (disambiguation)
 "Category \(disambiguation\)") .

For information about Wikipedia's categories, see  Help:Category
 or  Wikipedia:Categorization
 .

 Information science 
---
[ General aspects ](/wiki/Outline_of_information_science "Outline of
Conceptual clustering  

Main article:  Conceptual clustering 

**Conceptual clustering** is a  machine learning  paradigm for  unsupervised classification
 that was
defined by  Ryszard S. Michalski  in 1980.  [38]  [39]  It is a modern variation of the classical
approach of categorization, and derives from attempts to explain how knowledge
is represented. In this approach,  classes 
"Class \(philosophy\)") (clusters or entities) are generated by first
formulating their conceptual descriptions and then classifying the entities
according to the descriptions.  [40]

Conceptual clustering developed mainly during the 1980s, as a  machine
 paradigm for  unsupervised
learning  . It is
distinguished from ordinary  data clustering  by generating a concept description for each generated
category.

Conceptual clustering is closely related to  fuzzy set  theory, in which objects may belong to one or more groups, in
varying degrees of fitness. A  cognitive 
approach accepts that natural categories are graded (they tend to be  fuzzy
 at their boundaries) and inconsistent
in the status of their constituent members. The idea of necessary and
sufficient conditions is almost never met in categories of naturally occurring
things.
7 to 11 Years
External links  

Look up _** categorization
 ** _ in Wiktionary, the free
dictionary.

*  To Cognize is to Categorize: Cognition is Categorization   Archived  2012-02-08 at the  Wayback Machine 
*  Wikipedia Categories Visualizer 
*  Interdisciplinary Introduction to Categorization: Interview with Dvora Yanov (political sciences), Amie Thomasson (philosophy) and Thomas Serre (artificial intelligence) 
*  "Category"  . _ Encyclopædia Britannica  _ . Vol. 5 (11th ed.). 1911. pp. 508–510.

*  v  
*  t  
*  e  

 Philosophy of language 

---

 Index of language articles 

 Philosophers  |

*  Confucius 
*  Gorgias 
*  Cratylus 
*  Plato 
*  Aristotle 
*  Eubulides 
*  Diodorus 
*  Chrysippus 
*  Zhuangzi 
*  Xunzi  "Xunzi \(philosopher\)")
*  Averroes 
*  Ibn Khaldun 
*  Hobbes 
*  Leibniz 
*  Herder 
*  von Humboldt 
*  Mauthner 
*  Ricœur 
*  de Saussure 
*  Frege 
*  Boas 
*  Tillich 
*  Sapir 
*  Bloomfield 
*  Bergson 
*  Vygotsky 
*  Wittgenstein 
*  Russell
*  Ambiguity 
*  Cant  "Cant \(language\)")
*  Linguistic relativity 
*  Language 
*  Truth-bearer 
*  Proposition 
*  Use–mention distinction 
*  Concept 
* Categories
*  Set  "Set \(mathematics\)")
*  Class  "Class \(philosophy\)")
*  Family resemblance 
*  Intension 
*  Logical form 
*  Metalanguage 
*  Mental representation 
*  Modality (natural language)  "Modality \(natural language\)")
*  Presupposition 
*  Principle of compositionality 
*  Property  "Property \(philosophy\)")
*  Sign  "Sign \(semiotics\)")
*  Sense and reference 
*  Speech act 
*  Symbol 
*  Sentence  "Sentence \(linguistics\)")
*  Statement  "Statement \(logic\)")
* _ more...  _

Works  |

* _ Cratylus  "Cratylus \(dialogue\)") _ (n.d.)
* _ Port-Royal Grammar  _ (1660)
* _ De Arte Combinatoria  _ (1666)
* _ An Essay Towards a Real Character, and a Philosophical Language  _ (1668)
* _ Alciphron  "Alciphron \(book\)") _ (1732)
* "  On Denoting  "  (1905)
* _ Tractatus Logico-Philosophicus  _ (1921)
* _ Language, Truth, and Logic  _ (1936)
* _ Two Dogmas of Empiricism  _ (1951)
* _ Philosophical Investigations  _ (1953)
* _ Of Grammatology  _ (1967)
* _ Naming and Necessity  _ (1980)
* _ Wittgenstein on Rules and Private Language  _ (1982)
* _ Limited Inc  _ (1988)

Related articles  |

*  Analytic philosophy 
*  Philosophy of information 
*  Philosophical logic 
*  Linguistics 
*  Pragmatics 
*  Rhetoric 
*  Scholasticism 
*  School of Names 
*  Semantics  "Semantics \(natural language\)")
Formal models  

 Computational models  of
categorization have been developed to test theories about how humans represent
and use category information.  [33]  To accomplish this, categorization models
can be fit to experimental data to see how well the predictions afforded by
the model line up with human performance. Based on the model's success at
explaining the data, theorists are able to draw conclusions about the accuracy
of their theories and their theory's relevance to human category
representations.

To effectively capture how humans represent and use category information,
categorization models generally operate under variations of the same three
basic assumptions.  [45]  [33]  First, the model must make some kind of
assumption about the internal representation of the stimulus (e.g.,
representing the perception of a stimulus as a point in a multi-dimensional
space).  [45]  Second, the model must make an assumption about the specific
information that needs to be accessed in order to formulate a response (e.g.,
exemplar models require the collection of all available exemplars for each
category).  [36]  Third, the model must make an assumption about how a
response is selected given the available information.  [45]

Though all categorization models make these three assumptions, they
distinguish themselves by the ways in which they represent and transform an
input into a response representation.  [33]  The internal knowledge structures
of various categorization models reflect the specific representation(s) they
use to perform these transformations. Typical representations employed by
models include exemplars, prototypes, and rules.  [33]

* **Exemplar models** store all distinct instances of stimuli with their corresponding category labels in memory. Categorization of subsequent stimuli is determined by the stimulus' collective similarity to all known exemplars.
* **Prototype models** store a summary representation of all instances in a category. Categorization of subsequent stimuli is determined by selecting the category whose prototype is most similar to the stimulus.
* **Rule-based models** define categories by storing summary lists of the necessary and sufficient features required for category membership. Boundary models can be considered as atypical rule models, as they do not define categories based on their content. Rather, boundary models define the edges (boundaries) between categories, which subsequently serve as determinants for how a stimulus gets categorized.
Rule-based models  

**RULEX (Rule-Plus-Exception) Model [47]  ** While simple logical rules are
ineffective at learning poorly defined category structures, some proponents of
the rule-based theory of categorization suggest that an imperfect rule can be
used to learn such category structures if exceptions to that rule are also
stored and considered. To formalize this proposal, Nosofsky and colleagues
(1994) designed the RULEX model. The RULEX model attempts to form a decision
tree  [48]  composed of sequential tests of an object's attribute values.
Categorization of the object is then determined by the outcome of these
sequential tests. The RULEX model searches for rules in the following ways:
[49]

* **Exact** Search for a rule that uses a single attribute to discriminate between classes without error.
* **Imperfect** Search for a rule that uses a single attribute to discriminate between classes with few errors
* **Conjunctive** Search for a rule that uses multiple attributes to discriminate between classes with few errors.
* **Exception** Search for exceptions to the rule.

The method that RULEX uses to perform these searches is as follows:  [49]
First, RULEX attempts an exact search. If successful, then RULEX will
continuously apply that rule until misclassification occurs. If the exact
search fails to identify a rule, either an imperfect or conjunctive search
will begin. A sufficient, though imperfect, rule acquired during one of these
search phases will become permanently implemented and the RULEX model will
then begin to search for exceptions. If no rule is acquired, then the model
will attempt the search it did not perform in the previous phase. If
successful, RULEX will permanently implement the rule and then begin an
exception search. If none of the previous search methods are successful RULEX
will default to only searching for exceptions, despite lacking an associated
rule, which equates to acquiring a random rule.
2 to 7 Years
Prototype models  

**Weighted Features Prototype Model** [32]  An early instantiation of the
prototype model was produced by Reed in the early 1970s. Reed (1972) conducted
a series of experiments to compare the performance of 18 models on explaining
data from a categorization task that required participants to sort faces into
one of two categories.  [32]  Results suggested that the prevailing model was
the weighted features prototype model, which belonged to the family of average
distance models. Unlike traditional average distance models, however, this
model differentially weighted the most distinguishing features of the two
categories. Given this model's performance, Reed (1972) concluded that the
strategy participants used during the face categorization task was to
construct prototype representations for each of the two categories of faces
and categorize test patterns into the category associated with the most
similar prototype. Furthermore, results suggested that similarity was
determined by each categories most discriminating features.
Living and Loving in a Diverse Society
Category learning theories  

Category learning researchers have proposed various theories for how humans
learn categories.  [43]  Prevailing theories of category learning include the
prototype theory, the exemplar theory, and the decision bound theory.  [41]

The prototype theory suggests that to learn a category, one must learn the
category's prototype. Subsequent categorization of novel stimuli is then
accomplished by selecting the category with the most similar prototype.  [41]

The exemplar theory suggests that to learn a category, one must learn about
the exemplars that belong to that category. Subsequent categorization of a
novel stimulus is then accomplished by computing its similarity to the known
exemplars of potentially relevant categories and selecting the category that
contains the most similar exemplars.  [36]

Decision bound theory suggests that to learn a category, one must either learn
the regions of a stimulus space associated with particular responses or the
boundaries (the decision bounds) that divide these response regions.
Categorization of a novel stimulus is then accomplished by determining which
response region it is contained within.  [44]
The Depth and Breadth of the Human-Animal Bond
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Limitations  

Limitations that apply to both BIRGing and CORFing are perceptions and
expectations about performance and how they have an impact. Many scholars have
found that the confirmation and disconfirmation of the expected victory or
loss has a significant effect on BIRGing and CORFing. For example, if one
predicts that one's favorite team is going to lose, one is less likely to be
afraid to associate with that team because they predicted the loss. This
supports that a person is more likely to participate in BIRGing or CORFing if
their public image is threatened. In addition, most studies only test for the
identification with one favorite team, whereas individuals may have multiple
favorite teams.  [22]  Whether having multiple favorite teams increases the
occurrences of BIRGing could be investigated further.  [22]

A study published by Spinda in 2011 found significant differences between
those identifying as male and female in BIRGing and CORFing.  [22]  However, a
study by Ware and Kowalski did not find significant differences among sports
fans based on gender, though it found males were more likely to identify as
die-hard fans.  [23]
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