Categorization is a type of cognition involving conceptual differentiation between characteristics of conscious experience, such as objects, events, or ideas.
Categorization is important in learning, prediction, inference, decision making, language, and many forms of organisms' interaction with their environments.
[6] The three levels of abstraction are as follows: 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.
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.
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.
[12] Further, Latvian professor Igor Val Danilov expanded this concept to the intrauterine period by introducing a Mother-Fetus Neurocognitive model:[13] 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.
[16][17][18][19][20][21] These data show that collaborative interaction without sensory cues can emerge in mother-child dyads, providing Shared intentionality.
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.
[citation needed] The classical view of categories first appeared 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.
Examples of the use of the classical view of categories can be found in the western philosophical works of Descartes, Blaise Pascal, Spinoza and John Locke, and in the 20th century in Bertrand Russell, G.E.
It has been a cornerstone of analytic philosophy and its conceptual analysis, with more recent formulations proposed in the 1990s by Frank Cameron Jackson and Christopher Peacocke.
[25][26][27] 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.
[28][29] 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][30] Modern versions of classical categorization theory study how the brain learns and represents categories by detecting the features that distinguish members from nonmembers.
[36] Another instance of the similarity-based approach to categorization, the exemplar theory likewise compares the similarity of candidate category members to stored memory representations.
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.
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.
To effectively capture how humans represent and use category information, categorization models generally operate under variations of the same three basic assumptions.
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.
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.
The RULEX model attempts to form a decision tree[49] composed of sequential tests of an object's attribute values.
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.
The unknown stimulus dimension value (e.g., category label) is then predicted by the winning cluster, which, in turn, informs the categorization decision.
Social categorization consists of putting human beings into groups in order to identify them based on different criteria.
[53] 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.
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.