Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.
However, these issues are closely related, since memory recall of facts could be considered a "trivial" conceptual process where prior exemplars representing the concept are invariant.
So, when designing a curriculum or learning through this method, comparing like and unlike examples are key in defining the characteristics of a topic.
[5] According to Paivio’s dual -coding theory, concrete concepts are the one that is remembered easier from their perceptual memory codes.
Some ideas like chair and dog are more cut and dry in their perceptions but concepts like cold and fantasy can be seen in a more obscure way.
Abstract-concept learning is seeing the comparison of the stimuli based on a rule (e.g., identity, difference, oddity, greater than, addition, subtraction) and when it is a novel stimulus.
[9] Binder, Westbury, McKiernan, Possing, and Medler (2005)[10] used fMRI to scan individuals' brains as they made lexical decisions on abstract and concrete concepts.
An example of this is in elementary school when learning the direction of the compass North, East, South and West.
A schema is an organization of smaller concepts (or features) and is revised by situational information to assist in comprehension.
Reinforcement learning as described by Watson and elaborated by Clark Hull created a lasting paradigm in behavioral psychology.
Neural networks also are open to neuroscience and psychophysiological models of learning following Karl Lashley and Donald Hebb.
When rules are used in learning, decisions are made based on properties alone and rely on simple criteria that do not require a lot of memory ( Rouder and Ratcliff, 2006).
An important result of exemplar models in psychology literature has been a de-emphasis of complexity in concept learning.
More recently, cognitive psychologists have begun to explore the idea that the prototype and exemplar models form two extremes.
There are two distinct subgroups or conceptual clusters: spoons tend to be either large and wooden, or small and made of metal.
The prototypical spoon would then be a medium-size object made of a mixture of metal and wood, which is clearly an unrealistic proposal.
A number of different proposals have been made in this regard (Anderson, 1991; Griffiths, Canini, Sanborn & Navarro, 2007; Love, Medin & Gureckis, 2004; Vanpaemel & Storms, 2008).
[20] The Bayesian concept of Prior Probability stops being overly specific, while the likelihood of a hypothesis ensures the definition is not too broad.
The hypothesis that the word "horse" refers to all animals of this species is most likely of the three possible definitions, as it has both a reasonable prior probability and likelihood given examples.
Bayes' theorem is important because it provides a powerful tool for understanding, manipulating and controlling data5 that takes a larger view that is not limited to data analysis alone6.
However, if Bayesians show that the accumulated evidence and the application of Bayes' law are sufficient, the work will overcome the subjectivity of the inputs involved7.
This theory exploits the idea that each task humans perform consists of a series of discrete operations.
The model has been applied to learning and memory, higher level cognition, natural language, perception and attention, human-computer interaction, education, and computer generated forces.
[citation needed] In addition to John R. Anderson, Joshua Tenenbaum has been a contributor to the field of concept learning; he studied the computational basis of human learning and inference using behavioral testing of adults, children, and machines from Bayesian statistics and probability theory, but also from geometry, graph theory, and linear algebra.
M. D. Merrill's component display theory (CDT) is a cognitive matrix that focuses on the interaction between two dimensions: the level of performance expected from the learner and the types of content of the material to be learned.
Merrill classifies a learner's level of performance as: find, use, remember, and material content as: facts, concepts, procedures, and principles.
Another significant aspect of the CDT model is that it allows for the learner to control the instructional strategies used and adapt them to meet his or her own learning style and preference.
A major goal of this model was to reduce three common errors in concept formation: over-generalization, under-generalization and misconception.