Cognitive categorization
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Categorization is an activity that consists of putting things (objects, ideas, people) into categories (classes, types, index) based on their similarities or common criteria. It allows humans to organize things, objects, and ideas that exist around them and simplify their understanding of the world.[1] Categorization is something that humans and other organisms do: "doing the right thing with the right kind of thing." The activity of categorizing things can be nonverbal or verbal. For humans, both concrete objects and abstract ideas are recognized, differentiated, and understood through categorization. Objects are usually categorized for some adaptive or pragmatic purposes.
Categorization is grounded in the features that distinguish the category's members from nonmembers. Categorization is important in learning, prediction, inference, decision making, language, and many forms of organisms' interaction with their environments.
There are many theories of categorization, among them:
- Classical categorization
- Conceptual clustering
- Prototype theory
The classical view of categorization
Classical categorization first appears in the context of Western Philosophy in the work of Plato, who, in his Statesman dialogue, introduces the approach of grouping objects based on their similar properties. This approach was further explored and systematized by Aristotle in his Categories treatise, where he analyzes the differences between classes and objects. Aristotle also applied intensively the classical categorization scheme in his approach to the classification of living beings (which uses the technique of applying successive narrowing questions such as "Is it an animal or vegetable?", "How many feet does it have?", "Does it have fur or feathers?", "Can it fly?"...), establishing this way the basis for natural taxonomy.
According to the classical Aristotelian view, categories are discrete entities characterized by a set of features that are shared by their members. In analytic philosophy, these features are assumed to establish the conditions which are both necessary and sufficient conditions to capture meaning.
In the classical view, categories need to be clearly defined, mutually exclusive and collectively exhaustive. This way, any entity in the given classification universe belongs unequivocally to one, and only one, of the proposed categories.
Modern versions of classical categorization theory study how the brain learns and represents categories by detecting the features that distinguish members from nonmembers.[2][3]
Conceptual clustering
Conceptual clustering is a modern variation of the classical approach, and derives from attempts to explain how knowledge is represented. In this approach, classes (clusters or entities) are generated by first formulating their conceptual descriptions and then classifying the entities according to the descriptions.
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.
Categorization tasks in which category labels are provided to the learner for certain objects are referred to as supervised classification, supervised learning, or concept learning. Categorization tasks in which no labels are supplied are referred to as unsupervised classification, unsupervised learning, or data clustering. The task of supervised classification involves extracting information from the labeled examples that allows accurate prediction of class labels of future examples. This may involve the abstraction of a rule or concept relating observed object features to category labels, or it may not involve abstraction (e.g., exemplar models). The task of clustering involves recognizing inherent structure in a data set and grouping objects together by similarity into classes. It is thus a process of generating a classification structure.
Conceptual clustering is closely related to fuzzy set theory, in which objects may belong to one or more groups, in varying degrees of fitness.
Prototype theory
Since the research by Eleanor Rosch and George Lakoff in the 1970s, categorization can also be viewed as the process of grouping things based on prototypes—the idea of necessary and sufficient conditions is almost never met in categories of naturally occurring things. It has also been suggested that categorization based on prototypes is the basis for human development, and that this learning relies on learning about the world via embodiment.
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.
Systems of categories are not objectively "out there" in the world but are rooted in people's experience. Conceptual categories are not identical for different cultures, or indeed, for every individual in the same culture.
Categories form part of a hierarchical structure when applied to such subjects as taxonomy in biological classification: higher level: life-form level, middle level: generic or genus level, and lower level: the species level. These can be distinguished by certain traits that put an item in its distinctive category. But even these can be arbitrary and are subject to revision.
Categories at the middle level are perceptually and conceptually the more salient. The generic level of a category tends to elicit the most responses and richest images and seems to be the psychologically basic level. Typical taxonomies in zoology for example exhibit categorization at the embodied level, with similarities leading to formulation of "higher" categories, and differences leading to differentiation within categories.
Miscategorization
There cannot be categorization without the possibility of miscategorization. To do "the right thing with the right kind of thing."[4], 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.[5]
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.[6] 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.
See also
- Categorical perception
- Library classification
- Multi-label classification
- Pattern recognition
- Statistical classification
- Symbol grounding problem
- Characterization (mathematics)
References
- ^ McGarty, Craig, et al. “Social Categorization.” International Encyclopedia of the Social & Behavioral Sciences, 2015, pp. 186–191.
- ^ Ashby, F. G., & Valentin, V. V. (2017). Multiple systems of perceptual category learning: Theory and cognitive tests. In: Cohen, H., & Lefebvre, C. (Eds.). (2017).Handbook of Categorization in Cognitive Science (2nd edition). Elsevier.
- ^ Pérez-Gay Juárez, F., Thériault, C., Gregory, M., Rivas, D., Sabri, H., & Harnad, S. (2017). How and Why Does Category Learning Cause Categorical Perception? International Journal of Comparative Psychology, 30.
- ^ Cohen & Lefebvre 2017
- ^ Lasnik, H., & Lidz, J. L. (2017). The Argument from the Poverty of the Stimulus. In: Ian Roberts (ed.) The Oxford Handbook of Universal Grammar.
- ^ Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., ... & Bagci, U. (2018). Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks British Journal of Radiology, 91(1089), 20170545.
External links
- To Cognize is to Categorize: Cognition is Categorization
- Wikipedia Categories Visualizer
- Interdisciplinary Introduction to Categorization: Interview with Dvora Yanov (political sciences), Amie Thomasson (philosophy) and Thomas Serre (artificial intelligence)
- . Encyclopædia Britannica. Vol. 5 (11th ed.). 1911. pp. 508–510.