propositional learning

propositional learning

comprehension that involves the higher cognitive functions of abstraction and symbolization.
See also: abstraction (5), symbolization (2). Compare: procedural memory.
References in periodicals archive ?
As postulated by the dual-process models (Furedy & Riley, 1987; Lovibond & Shanks, 2002), the changes arising from repeated CS-US pairings are the result of two independent learning processes: a propositional learning that leads to conscious knowledge of association between the stimuli, and a lower level, non-propositional conditioning process which directly triggers an automatic activation of a conditioned response by an associative mechanism, such as an excitatory link between CS and US nodes or between the CS and RC.
In other words, if one argues that a single psychological process (propositions) accounts for human learning, then it should follow that both implicit and explicit attitudes are a product of propositional learning. Given that it is generally assumed that propositions can determine explicit attitudes (i.e., nonautomatic evaluations of objects; see Gawronski & Bodenhausen, 2007a, 2007b), the challenge for a propositional model of attitudes is to explain how implicit attitudes (i.e., automatic evaluations of objects) can come about.
This approach, of independently quantified arguments, allows propositional learning algorithms to be applied systematically to learning relational concepts in polynomial time and in a modular fashion.
It provides a direct method of learning in a relational domain by means of a propositional learning algorithm.