exemplarteori

Exemplarteori, or exemplar theory, is a cognitive theory of categorization and memory that posits people represent categories by storing memories of specific instances, or exemplars, rather than forming a single abstract prototype. When faced with a new object, a person assesses its category by comparing it to the stored exemplars and computing overall similarity; the category with the greatest summed similarity is chosen. Some versions weight exemplars by factors such as recency or frequency and may incorporate decay over time.

Origin and development: Exemplar theory emerged in the late 20th century as an alternative to prototype theory, aiming to account for empirical findings in both categorization and recognition memory that prototypes could not fully explain. Notable proponents include Hintzman, who developed exemplar-based memory models such as MINERVA 2, and Nosofsky, who formulated probabilistic exemplar models like the Generalized Context Model (GCM). The approach has been applied to a range of domains, including visual and auditory categorization, language, and consumer psychology.

Variants and applications: In computational terms, exemplar models often operate through memory traces of individual instances and a similarity-based decision rule. In psychology, they help explain gradient typicality effects, boundary flexibility, and the influence of experience on categorization. In machine learning, nearest-neighbor methods are conceptually related to exemplar ideas.

Criticisms: The theory can be computationally intensive as categories accumulate many exemplars, and some findings are more readily explained by prototype or hybrid models. Critics also argue that exemplar approaches may overemphasize memorized detail at the expense of abstraction in some domains.

In languages such as Danish or Norwegian, the term exemplarteori is used to refer to this concept. See also prototype theory, category learning, recognition memory.