mteb/BrightProPsychologyRetrieval huggingface dataset:
[26]  At the beginning of the 20th century, the question of categories was
introduced into the empirical social sciences by Durkheim and Mauss, whose
pioneering work has been revisited in contemporary scholarship.  [27]  [28]

The classical model of categorization has been used at least since the 1960s
from linguists of the  structural semantics  paradigm, by  Jerrold Katz  and  Jerry Fodor  in 1963,
which in turn have influenced its adoption also by psychologists like  Allan
M. Collins  and  M. Ross Quillian
") .  [1]  [29]

Modern versions of classical categorization theory study how the brain learns
and represents categories by  detecting the features
 "Feature detection \(nervous
system\)") that distinguish members from nonmembers.  [30]  [31]
Prototype theory  

Main article:  Prototype theory 

The pioneering research by psychologist  Eleanor Rosch  and colleagues since 1973, introduced the  prototype theory
 , according to which
categorization can also be viewed as the process of grouping things based on 
prototypes  . This approach has been highly
influential, particularly for  cognitive linguistics
 .  [1]  It was in part
based on previous insights, in particular the formulation of a category model
based on  family resemblance 
by  Wittgenstein  (1953), and by  Roger
Brown  "Roger Brown \(psychologist\)") 's
_How shall a thing be called?_ (1958).  [1]

Prototype theory has been then adopted by cognitive linguists like  George
Lakoff  . The prototype theory is an
example of a similarity-based approach to categorization, in which a stored
category representation is used to assess the similarity of candidate category
members.  [32]  Under the prototype theory, this stored representation
consists of a summary representation of the category's members. This prototype
stimulus can take various forms. It might be a central tendency that
represents the category's average member, a modal stimulus representing either
the most frequent instance or a stimulus composed of the most common category
features, or, lastly, the "ideal" category member, or a caricature that
emphasizes the distinct features of the category.  [33]  An important
consideration of this prototype representation is that it does not necessarily
reflect the existence of an actual instance of the category in the world.
[33]  Furthermore, prototypes are highly sensitive to context.  [34]  For
example, while one's prototype for the category of beverages may be soda or
seltzer, the context of brunch might lead them to select mimosa as a
prototypical beverage.

The prototype theory claims that members of a given category share a  family
resemblance  , and categories
are defined by sets of typical features (as opposed to all members possessing
necessary and sufficient features).  [35]
Knolling "Tom Sachs")
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Overview  

**Categories** are distinct collections of concrete or abstract instances
(category members) that are considered equivalent by the cognitive system.
Using category knowledge requires one to access  mental representations
 that define the core
features of category members (cognitive psychologists refer to these category-
specific mental representations as  concepts  ).
[4]  [5]

To categorization theorists, the categorization of objects is often considered
using  taxonomies  "Taxonomy \(biology\)") with
three hierarchical levels of  abstraction 
.  [6]  For example, a plant could be identified at a high level of
abstraction by simply labeling it a flower, a medium level of abstraction by
specifying that the flower is a rose, or a low level of abstraction by further
specifying this particular rose as a dog rose. Categories in a taxonomy are
related to one another via class inclusion, with the highest level of
abstraction being the most inclusive and the lowest level of abstraction being
the least inclusive.  [6]  The three levels of abstraction are as follows:

* **Superordinate level, Genus** (e.g., Flower) - The highest and most inclusive level of abstraction. Exhibits the highest degree of generality and the lowest degree of within-category similarity.  [7]
* **Basic Level, Species** (e.g., Rose) - The middle level of abstraction. Rosch and colleagues (1976) suggest the basic level to be the most cognitively efficient.  [6]  Basic level categories exhibit high within-category _similarities_ and high between-category _dissimilarities_ . Furthermore, the basic level is the most inclusive level at which category exemplars share a generalized identifiable shape.  [6]  Adults most-often use basic level object names, and children learn basic object names first.  [6]
* **Subordinate level** (e.g., Dog Rose) - The lowest level of abstraction. Exhibits the highest degree of specificity and within-category similarity.  [7]
*  Carnap 
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Concepts  |
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Category learning  

Main article:  Category learning 

_While an exhaustive discussion of category learning is beyond the scope of
this article, a brief overview of category learning and its associated
theories is useful in understanding formal models of categorization._

If categorization research investigates how categories are maintained and
used, the field of category learning seeks to understand how categories are
acquired in the first place. To accomplish this, researchers often employ
novel categories of arbitrary objects (e.g., dot matrices) to ensure that
participants are entirely unfamiliar with the stimuli.  [41]  Category
learning researchers have generally focused on two distinct forms of category
learning.  Classification learning 
tasks participants with predicting category labels for a stimulus based on its
provided features. Classification learning is centered around learning
between-category information and the diagnostic features of categories.  [42]
In contrast,  inference learning  tasks
participants with inferring the presence/value of a category feature based on
a provided category label and/or the presence of other category features.
Inference learning is centered on learning within-category information and the
category's prototypical features.  [42]

Category learning tasks can generally be divided into two categories,
supervised and unsupervised learning.  Supervised learning
 tasks provide learners with
category labels. Learners then use information extracted from labeled example
categories to classify stimuli into the appropriate category, which may
involve the  abstraction  of a rule or
concept relating observed object features to category labels.  Unsupervised
learning  tasks do not
provide learners with category labels. Learners must therefore recognize
inherent structures in a data set and group stimuli together by similarity
into classes. Unsupervised learning is thus a process of generating a
classification structure. Tasks used to study category learning take various
forms:

* **Rule-based tasks** present categories that participants can learn through explicit reasoning processes. In these kinds of tasks, classification of stimuli is accomplished via the use of an acquired rule (i.e., if stimulus is large on dimension x, respond A).
* **Information-integration tasks** require learners to synthesize perceptual information from multiple stimulus dimensions prior to making categorization decisions. Unlike rule-based tasks, information-integration tasks do not afford rules that are easily articulable. Reading an X-ray and trying to determine if a tumor is present can be thought of as a real-world instantiation of an information-integration task.
* **Prototype distortion tasks** require learners to generate a prototype for a category. Candidate exemplars for the category are then produced by randomly manipulating the features of the prototype, which learners must classify as either belonging to the category or not.
Miscategorization  

There cannot be categorization without the possibility of  miscategorization
 .  [58]  To do "the right thing
with the right _kind_ of thing.",  [59]  there has to be both a right and a
wrong thing to do. Not only does a category of which "everything" is a member
lead logically to the  Russell paradox  ("is it or is it not a member of itself?"), but without the
possibility of error, there is no way to detect or define what distinguishes
category members from nonmembers.

An example of the absence of nonmembers is the problem of the  poverty of the
stimulus  in
language learning by the child: children learning the language do not hear or
make errors in the rules of  Universal Grammar  (UG). Hence they never get corrected for errors in UG.
Yet children's speech obeys the rules of UG, and speakers can immediately
detect that something is wrong if a linguist generates (deliberately) an
utterance that violates UG. Hence speakers can categorize what is UG-compliant
and UG-noncompliant. Linguists have concluded from this that the rules of UG
must be somehow encoded innately in the human brain.  [60]

Ordinary categories, however, such as "dogs," have abundant examples of
nonmembers (cats, for example). So it is possible to learn, by trial and
error, with error-correction, to detect and define what distinguishes dogs
from non-dogs, and hence to correctly categorize them.  [61]  This kind of
learning, called  reinforcement learning  in the behavioral literature and  supervised
learning  in the
computational literature, is fundamentally dependent on the possibility of
error, and error-correction. Miscategorization—examples of nonmembers of the
category—must always exist, not only to make the category learnable, but for
the category to exist and be definable at all.
Exemplar models  

**Generalized Context Model [46]  ** Medin and Schaffer's (1978)  context
model  was expanded upon by Nosofsky
(1986) in the mid-1980s, resulting in the production of the Generalized
Context Model (GCM).  [46]  The GCM is an exemplar model that stores exemplars
of stimuli as exhaustive combinations of the features associated with each
exemplar.  [33]  By storing these combinations, the model establishes contexts
for the features of each exemplar, which are defined by all other features
with which that feature co-occurs. The GCM computes the similarity of an
exemplar and a stimulus in two steps. First, the GCM computes the 
psychological distance  between the exemplar and the stimulus. This is accomplished by
summing the absolute values of the dimensional difference between the exemplar
and the stimulus. For example, suppose an exemplar has a value of 18 on
dimension X and the stimulus has a value of 42 on dimension X; the resulting
dimensional difference would be 24. Once psychological distance has been
evaluated, an  exponential decay function  determines the similarity of the exemplar and the
stimulus, where a distance of 0 results in a similarity of 1 (which begins to
decrease exponentially as distance increases). Categorical responses are then
generated by evaluating the similarity of the stimulus to each category's
exemplars, where each exemplar provides a "vote"  [33]  to their respective
categories that varies in strength based on the exemplar's similarity to the
stimulus and the strength of the exemplar's association with the category.
This effectively assigns each category a selection probability that is
determined by the proportion of votes it receives, which can then be fit to
data.
See also  

*  Categorical perception 
*  Characterization (mathematics)  "Characterization \(mathematics\)")
*  Classification (general theory)  "Classification \(general theory\)")
* [ Knolling ](/wiki/Tom_Sachs
information science")

*  Access 
*  Architecture 
*  Behavior 
*  Management 
*  Retrieval 
*  Seeking 
*  Society 
*  Knowledge organization 
*  Ontology  "Ontology \(computer science\)")
*  Philosophy 
*  Science and technology studies 
*  Taxonomy 

Related fields and subfields

*  Bibliometrics 
* Categorization
*  Censorship 
*  Classification  "Classification of the sciences \(Peirce\)")
*  Computer data storage 
*  Cultural studies 
*  Data modeling 
*  Informatics 
*  Information technology 
*  Intellectual freedom 
*  Intellectual property 
*  Library and information science 
*  Memory 
*  Preservation  "Preservation \(library and archival science\)")
*  Privacy 
*  Quantum information science 

*  v  
*  t  
*  e
Beginning of categorization  

The essential issue in studying categorization is how conceptual
differentiation between characteristics of conscious experience begins in
young, inexperienced organisms. Growing experimental data show evidence of
differentiation between characteristics of objects and events in newborns and
even in foetuses during the prenatal period.  [8]  [9]  This development
succeeds in organisms that only demonstrate simple reflexes (see articles on
the  binding problem  ,  cognition
 ,  cognitive development
 ,  infant cognitive
development  ,  multisensory integration  , and  perception  ). For their nervous systems, the environment is a cacophony of
sensory stimuli: electromagnetic waves, chemical interactions, and pressure
fluctuations.  [10]

Categorization thought involves the abstraction and differentiation of aspects
of experience that rely upon such power of mind as  intentionality
 and  perception  . The problem is that these young organisms should already grasp
the abilities of intentionality and perception to categorize the environment.
[8]  Intentionality and perception already require their ability to recognise
objects (or events), i.e., to identify objects by the  sensory system
 .  [11]  This is a vicious circle:
categorization needs intentionality and perception, which only appear in the
categorized environment. So, the young, inexperienced organism does not have
abstract thinking and cannot independently accomplish conceptual
differentiation between characteristics of conscious experience if it solves
the categorization problem alone.

Studying the origins of social cognition in child development, developmental
psychologist  Michael Tomasello  developed the notion of  Shared intentionality
 to account for unaware
processes during social learning after birth to explain processes in shaping 
intentionality  .  [12]  Further, Igor
Val Danilov expanded this concept to the intrauterine period by introducing a
hypothesis of neurophysiological processes occurring during  Shared
intentionality  .  [8]
The hypothesis attempts to explain the beginning of cognitive development in
organisms at different levels of bio-system complexity, from interpersonal
dynamics to neuronal interactions.  [13]  [14]  Evidence in neuroscience
supports the hypothesis. Hyperscanning research studies observed inter-brain
activity under conditions without communication in pairs while subjects were
solving the shared cognitive problem, and they registered an increased inter-
brain activity in contrast to the condition when subjects solved a similar
problem alone.  [15]  [16]  [17]  [18]  [19]  [20]  These data show that
collaborative interaction without sensory cues can emerge in mother-child
dyads, providing  Shared intentionality  .  [8]  It shows the mode to cognize at the stage without
communication and abstract thinking. The significance of this knowledge is
that it can reveal the new direction to study  consciousness
 since the latter refers to awareness of
internal and external existence relying on  intentionality
 ,  perception  and categorization of the environment.
Categorization

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Social categorization  

**Social categorization** consists of putting human beings into groups in
order to identify them based on different criteria. Categorization is a
process studied by scholars in cognitive science but can also be studied as a
social activity. Social categorization is different from the categorization of
other things because it implies that people create categories for themselves
and others as human beings.  [3]  Groups can be created based on ethnicity,
country of origin, religion, sexual identity, social privileges, economic
privileges, etc. Various ways to sort people exist according to one's schemas.
People belong to various social groups because of their ethnicity, religion,
or age.  [52]

Social categories based on age, race, and gender are used by people when they
encounter a new person. Because some of these categories refer to physical
traits, they are often used automatically when people do not know each other.
[53]  These categories are not objective and depend on how people see the
world around them. They allow people to identify themselves with similar
people, and to identify people who are different. They are useful in one's
identity formation with the people around them. One can build their own
identity by identifying themselves in a group or by rejecting another group.
[54]

Social categorization is similar to other types of categorization since it
aims at simplifying the understanding of people. However, creating social
categories implies that people will position themselves in relation to other
groups. A hierarchy in group relations can appear as a result of social
categorization.  [54]

Scholars argue that the categorization process starts at a young age when
children start to learn about the world and the people around them. Children
learn how to know people according to categories based on similarities and
differences. Social categories made by adults also impact their understanding
of the world. They learn about social groups by hearing generalities about
these groups from their parents. They can then develop prejudices about people
as a result of these generalities.  [53]

Another aspect of social categorization is mentioned by Stephen Reicher and
Nick Hopkins and is related to political domination. They argue that political
leaders use social categories to influence political debates.  [52]
Hybrid models  

**SUSTAIN (Supervised and Unsupervised Stratified
 Adaptive
Incremental Network)  [50]  ** It is often the case that learned category
representations vary depending on the learner's goals,  [51]  as well as how
categories are used during learning.  [5]  Thus, some categorization
researchers suggest that a proper model of categorization needs to be able to
account for the variability present in the learner's goals, tasks, and
strategies.  [50]  This proposal was realized by Love and colleagues (2004)
through the creation of SUSTAIN, a flexible clustering model capable of
accommodating both simple and complex categorization problems through
incremental adaptation to the specifics of problems.

In practice, the SUSTAIN model first converts a stimulus' perceptual
information into features that are organized along a set of dimensions. The
representational space that encompasses these dimensions is then distorted
(e.g., stretched or shrunk) to reflect the importance of each feature based on
inputs from an attentional mechanism. A set of clusters (specific instances
grouped by similarity) associated with distinct categories then compete to
respond to the stimulus, with the stimulus being subsequently assigned to the
cluster whose representational space is closest to the stimulus'. The unknown
stimulus dimension value (e.g., category label) is then predicted by the
winning cluster, which, in turn, informs the categorization decision.

The flexibility of the SUSTAIN model is realized through its ability to employ
both supervised and unsupervised learning at the cluster level. If SUSTAIN
incorrectly predicts a stimulus as belonging to a particular cluster,
corrective feedback (i.e., supervised learning) would signal sustain to
recruit an additional cluster that represents the misclassified stimulus.
Therefore, subsequent exposures to the stimulus (or a similar alternative)
would be assigned to the correct cluster. SUSTAIN will also employ
unsupervised learning to recruit an additional cluster if the similarity
between the stimulus and the closest cluster does not exceed a threshold, as
the model recognizes the weak predictive utility that would result from such a
cluster assignment. SUSTAIN also exhibits flexibility in how it solves both
simple and complex categorization problems. Outright, the internal
representation of SUSTAIN contains only a single cluster, thus biasing the
model towards simple solutions. As problems become increasingly complex (e.g.,
requiring solutions consisting of multiple stimulus dimensions), additional
clusters are incrementally recruited so SUSTAIN can handle the rise in
complexity.
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